This PDF file contains the front matter associated with SPIE Proceedings Volume 9803, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

The concept “Disaster Mitigation and Sustainable Engineering” is introduced comprehensively and several examples are shown in this paper. It is emphasized that it can be effectively realized in the field “smart materials and structural systems.” As serious disasters may not occur for a long period of time, and the structures for disaster mitigation suffer from vast amount of maintenance cost etc., they are better to be used daily. Their compactness and deploying function are also very useful. In order to demonstrate the concept, two examples having been experimentally tried are introduced, that is, artificial forests and deployable structure based on honeycomb to be used against flooding. Other examples and products in the world are also introduced and future directions are discussed.

One of the major benefits of optical fiber sensors for applications to structural health monitoring and other structural measurements is their inherent multiplexing capabilities, meaning that a large number of sensing locations can be achieved with a single optical fiber. It has been well demonstrated that point wise sensors can be multiplexed to form sensor networks or optical fibers integrated with distributed sensing techniques. The spacing between sensing locations can also be tuned to match different length scales of interest. This article presents an overview of directions to adapt optical fiber sensor networking techniques into new applications where limitations such as available power or requirements for high data acquisition speeds are a driving factor. In particular, the trade-off between high fidelity sensor information vs. rapid signal processing or data acquisition is discussed.

Despite advances in computational cognition, there are many cyber-physical systems where human supervision and control is desirable. One pertinent example is the control of a robot arm, which can be found in both humanoid and commercial ground robots. Current control mechanisms require the user to look at several screens of varying perspective on the robot, then give commands through a joystick-like mechanism. This control paradigm fails to provide the human operator with an intuitive state feedback, resulting in awkward and slow behavior and underutilization of the robot's physical capabilities. To overcome this bottleneck, we introduce a new human-machine interface that extends the operator's proprioception by exploiting sensory substitution. Humans have a proprioceptive sense that provides us information on how our bodies are configured in space without having to directly observe our appendages. We constructed a wearable device with vibrating actuators on the forearm, where frequency of vibration corresponds to the spatial configuration of a robotic arm. The goal of this interface is to provide a means to communicate proprioceptive information to the teleoperator. Ultimately we will measure the change in performance (time taken to complete the task) achieved by the use of this interface.

The objective of this paper is to characterize frequency-dependent wave propagation of footstep induced floor vibration to improve robustness of vibration-based occupant localization. Occupant localization is an essential part of many smart structure applications (e.g., energy management, patient/customer tracking, etc.). Exist- ing techniques include visual (e.g. cameras and IR sensors), acoustic, RF, and load-based approaches. These approaches have many deployment and operational requirements that limits their adaptation. To overcome these limitations, prior work has utilized footstep-induced vibrations to allow sparse sensor configuration and non-intrusive detection. However, frequency dependent propagation characteristics and low signal-to-noise ratio (SNR) of footstep-induced vibrations change the shape of the signal. Furthermore, estimating the wave propagation velocity for forming the multilateration equations and localizing the footsteps is a challenging task. They, in turn, lead to large errors of localization. In this paper, we present a structural vibration based indoor occupant localization technique using improved time-difference-of-arrival between multiple vibration sensors. In particular we overcome signal distortion by decomposing the signal into frequency components and focusing on high energy components for accurate indoor localization. Such decomposition leverages the frequency-specific propagation characteristics and reduces the effect of low SNR (by choosing the components of highest energy). Furthermore, we develop a velocity calibration method that finds the optimal velocity which minimizes the localization error. We validate our approach through field experiments in a building with human participants. We are able to achieve an average localization error of less than 0.21 meters, which corresponds to a 13X reduction in error when compared to the baseline method using raw data.

The number of people passing through different indoor areas is useful in various smart structure applications, including occupancy-based building energy/space management, marketing research, security, etc. Existing approaches to estimate occupant traffic include vision-, sound-, and radio-based (mobile) sensing methods, which have placement limitations (e.g., requirement of line-of-sight, quiet environment, carrying a device all the time). Such limitations make these direct sensing approaches difficult to deploy and maintain. An indirect approach using geophones to measure floor vibration induced by footsteps can be utilized. However, the main challenge lies in distinguishing multiple simultaneous walkers by developing features that can effectively represent the number of mixed signals and characterize the selected features under different traffic conditions. This paper presents a method to monitor multiple persons. Once the vibration signals are obtained, features are extracted to describe the overlapping vibration signals induced by multiple footsteps, which are used for occupancy traffic estimation. In particular, we focus on analysis of the efficiency and limitations of the four selected key features when used for estimating various traffic conditions. We characterize these features with signals collected from controlled impulse load tests as well as from multiple people walking through a real-world sensing area. In our experiments, the system achieves the mean estimation error of ±0.2 people for different occupant traffic conditions (from one to four) using k-nearest neighbor classifier.

The needs for monitoring systems to be used in houses are getting stronger because of the increase of the single household population due to the low birth rate and longevity. Among others, gait parameters are under the spotlight to be examined as the relations with several diseases have been reported. It is known that the gait parameters obtained at a walk test are different from those obtained under the daily life. Thus, the system which can measure the gait parameters in the real living environment is needed. Generally, gait abilities are evaluated by a measurement test, such as Timed Up and Go test and 6-minute walking test. However, these methods need measurers, so the accuracy depends on them and the lack of objectivity is pointed out. Although, a precise motion capture system is used for more objective measurement, it is hard to be used in daily measurement, because the subjects have to put the markers on their body. To solve this problem, marker less sensors, such as Kinect, are developed and used for gait information acquisition. When they are attached to a mobile robot, there is no limitation of distance. However, they still have challenges of calibration for gait parameters, and the important gait parameters to be acquired are not well examined. Therefore, in this study, we extract the important parameters for gait analysis, which have correlations with diseases and age differences, and suggest the gait parameters extraction from depth data by Kinect v2 which is mounted on a mobile robot aiming at applying to the living environment.

Magnetorheological (MR) dampers are promising to substitute traditional oil dampers because of adaptive properties of MR fluids. During vibration, significant energy is wasted due to the energy dissipation in the damper. Meanwhile, for conventional MR damping systems, extra power supply is needed. In this paper, a new energy harvester is designed in an MR damper that integrates controllable damping and energy harvesting functions into one device. The energy harvesting part of this MR damper has a unique mechanism converting linear motion to rotary motion that would be more stable and cost effective when compared to other mechanical transmissions. A Maxon motor is used as a power generator to convert the mechanical energy into electrical energy to supply power for the MR damping system. Compared to conventional approaches, there are several advantages in such an integrated device, including weight reduction, ease in installation with less maintenance. A mechanical energy harvested MR damper with linear-rotary motion converter and motion rectifier is designed, fabricated, and tested. Experimental studies on controllable damping force and harvested energy are performed with different transmissions. This energy harvesting MR damper would be suitable to vehicle suspensions, civil structures, and smart prostheses.

The batteries of the current pacing devices are relatively large and occupy over 60 percent of the size of pulse generators. Therefore, they cannot be placed in the subtle areas of human body. In this paper, the mastication force and the resulting tooth pressure are converted to electricity. The pressure energy can be converted to electricity by using the piezoelectric effect. The tooth crown is used as a power autonomous pulse generator. We refer to this envisioned pulse generator as the smart tooth. The smart tooth is in the form of a dental implant. A piezoelectric vibration energy harvester is designed and modeled for this purpose. The Piezoelectric based energy harvesters investigated and analyzed in this paper initially includes a single degree of freedom piezoelectric based stack energy harvester which utilizes a harvesting circuit employing the case of a purely resistive circuit. The next step is utilizing and investigating a bimorph piezoelectric beam which is integrated/embedded in the smart tooth implant. Mastication process causes the bimorph beam to buckle or return to unbuckled condition. The transitions results in vibration of the piezoelectric beam and thus generate energy. The power estimated by the two mechanisms is in the order of hundreds of microwatts. Both scenarios of the energy harvesters are analytically modeled. The exact analytical solution of the piezoelectric beam energy harvester with Euler–Bernoulli beam assumptions is presented. The electro-mechanical coupling and the geometric nonlinearities have been included in the model for the piezoelectric beam.

This study presents an adaptive bridge bearing that can sense structural loads and tune its properties to mitigate structural vibrations. The bearing utilizes magnetorheological elastomer (MRE) layers which allow for an increased stiffness induced with a magnetic field. The system also features a MRE-based sensing system for sensing the structural wind and traffic load. The sensing system is capable of transmitting data wirelessly to a central logging computer for monitoring bridge performance and sending alerts in the case of a major event. The capability of the MRE-based sensing system for sensing structural loads and wireless transmission of data were investigated. The adaptive bridge bearing incorporates a closed-loop magnetic circuit that results in an enhanced magnetic field in the MRE layers. Results show the sensitivity of the MRE-based sensors and the performance of the wireless system, as well as the design and analysis of the tunable bridge bearing.

Main tension elements are critical to the overall stability of cable-supported bridges. A dependable and rapid determination of cable tension is desired to assess the state of a cable-supported bridge and evaluate its operability. A portable smart sensor setup is presented to reduce post-processing time and deployment complexity while reliably determining cable tension using dynamic characteristics extracted from spectral analysis. A self-recording accelerometer is coupled with a single-board microcomputer that communicates wirelessly with a remote host computer. The portable smart sensing device is designed such that additional algorithms, sensors and controlling devices for various monitoring applications can be installed and operated for additional structural assessment. The tension-estimating algorithms are based on taut string theory and expand to consider bending stiffness. The successful combination of cable properties allows the use of a cable’s dynamic behavior to determine tension force. The tension-estimating algorithms are experimentally validated on a through-arch steel bridge subject to ambient vibration induced by passing traffic. The tension estimation is determined in well agreement with previously determined tension values for the structure.

Ice accretion on cables of bridge structures poses serious risk to the structure as well as to vehicular traffic when the ice falls onto the road. Detection of ice formation, quantification of the amount of ice accumulated, and prediction of icefalls will increase the safety and serviceability of the structure. In this paper, an ice accretion detection algorithm is presented based on the Continuous Wavelet Transform (CWT). In the proposed algorithm, the acceleration signals obtained from bridge cables are transformed using wavelet method. The damage sensitive features (DSFs) are defined as a function of the wavelet energy at specific wavelet scales. It is found that as ice accretes on the cables, the mass of cable increases, thus changing the wavelet energies. Hence, the DSFs can be used to track the change of cables mass. To validate the proposed algorithm, we use the data collected from a laboratory experiment conducted at the Technical University of Denmark (DTU). In this experiment, a cable was placed in a wind tunnel as ice volume grew progressively. Several accelerometers were installed at various locations along the testing cable to collect vibration signals.

Understanding the dynamic behavior of complex structures such as long-span bridges requires dense deployment of sensors. Traditional wired sensor systems are generally expensive and time-consuming to install due to cabling. With wireless communication and on-board computation capabilities, wireless smart sensor networks have the advantages of being low cost, easy to deploy and maintain and therefore facilitate dense instrumentation for structural health monitoring. A long-term monitoring project was recently carried out for a cable-stayed bridge in South Korea with a dense array of 113 smart sensors, which feature the world’s largest wireless smart sensor network for civil structural monitoring. This paper presents a comprehensive statistical analysis of the modal properties including natural frequencies, damping ratios and mode shapes of the monitored cable-stayed bridge. Data analyzed in this paper is composed of structural vibration signals monitored during a 12-month period under ambient excitations. The correlation between environmental temperature and the modal frequencies is also investigated. The results showed the long-term statistical structural behavior of the bridge, which serves as the basis for Bayesian statistical updating for the numerical model.

This paper proposes a new way for guided wave structural health monitoring using in-plane shear (d36 type) piezoelectric wafer active sensors phased arrays. Conventional piezoelectric wafer active sensors phased arrays based on inducing into specific Lamb wave modes (d31 type) has already widely used for health monitoring of the thin-wall structures. Rather than Lamb wave modes, the in-plane shear piezoelectric wafer active sensors phased arrays induces in-plane shear horizontal (SH) guided waves. The SH guided waves are distinct with the Lamb waves with simple waveform and less additional converted wave modes and the zero symmetric mode (SH₀) is non-dispersive. In this paper, the advantage of the shear horizontal wave and the in-plane shear piezoelectric wafers capability to generate SH waves is first reviewed. Then finite element analysis of a 4-in-plane shear wafer active sensors phased array embedded on a rectangular aluminium plate is performed. In addition, numerical simulations with respect to creaks with different sizes as well as locations are implemented by the in-plane shear wafer active sensors phased array. For comparison purposes, the same numerical simulations using the conventional piezoelectric wafer active sensors phased arrays are also employed at the same time. Results indicate that the in-plane shear (d36 type) piezoelectric wafer active sensors phased arrays has the potential to identify damage location and assess damage severity in structural health monitoring.

Bondline integrity is still one of the most critical concerns in the design of aircraft structures up to date. Due to the lack of confidence on the integrity of the bondline both during fabrication and service, the industry standards and regulations still require assembling the composite using conventional fasteners. Furthermore, current state-of-the-art non-destructive evaluation (NDE) and structural health monitoring (SHM) techniques are incapable of offering mature solutions on the issue of bondline integrity monitoring. Therefore, the objective of this work is the development of an intelligent adhesive film with integrated micro-sensors for monitoring the integrity of the bondline interface. The proposed method makes use of an electromechanical-impedance (EMI) based method, which is a rapidly evolving approach within the SHM family. Furthermore, an innovative screen-printing technique to fabricate piezoelectric ceramic sensors with minimal thickness has been developed at Stanford. The approach presented in this study is based on the use of (i) micro screen-printed piezoelectric sensors integrated into adhesive leaving a minimal footprint on the material, (ii) numerical and analytical modeling of the EMI spectrum of the adhesive bondline, (iii) novel diagnostic algorithms for monitoring the bondline integrity based on advanced signal processing techniques, and (iv) the experimental assessment via prototype adhesively bonded structures in static (varying loads) and dynamic (fatigue) environments. The proposed method will provide a huge confidence on the use of bonded joints for aerospace structures and lead to a paradigm change in their design by enabling enormous weight savings while maximizing the economic and performance efficiency.

The application of guided waves using surface-bonded piezoceramic transducers for nondestructive testing (NDT) and Structural Health Monitoring (SHM) have shown great potential. However, due to difficulty in identification of individual wave modes resulting from their dispersive and multi-modal nature, selective mode excitement methods are highly desired. The presented work focuses on an optimization-based approach to design of a piezoelectric transducer for selective guided waves generation. The concept of the presented framework involves a Finite Element Method (FEM) model in the optimization process. The material of the transducer is optimized in topological sense with the aim of tuning piezoelectric properties for actuation of specific guided wave modes.

Ultrasonic wave based structural health monitoring (SHM) is an innovative method for nondestructive detection and an area of growing interest. This is due to high demands for wireless detection in the field of structural engineering. Through optically exciting and detecting ultrasonic waves, electrical wire connections can be avoided, and non-contact SHM can be achieved. With the combination of piezoelectric transducer (PZT) (which possesses high heat resistance) and the noncontact detection, this system has a broad range of applications, even in extreme conditions. This paper reports an all-optically driven SHM system. The resonant frequencies of the PZT transducers are sensitive to a variety of structural damages. Experimental results have verified the feasibility of the all-optically driven SHM system.

Effective monitoring of asset integrity subject to corrosion and erosion while minimizing the exposure of personnel to difficult and hazardous working environments has always been a major problem in many industries. One solution of this problem is permanently installed ultrasonic monitoring equipment which can continuously provide information on the rate of corrosion or cracking, even in the most severe environments and at extreme temperatures to prevent the need for shutdown. Here, a permanently installed 5 MHz ultrasonic monitoring system based on our HotSense® technology is designed and investigated. The system applicability for wall thickness, crack monitoring and weld inspection in high temperature environments is demonstrated through experimental studies on a range of Schedule 40 pipes at temperatures up to 350 °C continuously. The applicability for this technology to be distributed to Aerospace and Nuclear sectors are also explored and preliminary results discussed.

Structural health monitoring (SHM) systems using structurally-integrated sensors potentially allow the ability to inspect for damage in aircraft structures on-demand and could provide a basis for the development of condition-based maintenance approaches for airframes. These systems potentially offer both substantial cost savings and performance improvements over conventional nondestructive inspection (NDI). Acousto-ultrasonics (AU), using structurallyintegrated piezoelectric transducers, offers a promising basis for broad-field damage detection in aircraft structures. For these systems to be successfully applied in the field the hardware for AU excitation and interrogation needs to be easy to use, compact, portable, light and, electrically and mechanically robust. Highly flexible and inexpensive instrumentation for basic background laboratory investigations is also required to allow researchers to tackle the numerous scientific and engineering issues associated with AU based SHM. The Australian Defence Science and Technology Group (DST Group) has developed the Acousto Ultrasonic Structural health monitoring Array Module (AUSAM⁺), a compact device for AU excitation and interrogation. The module, which has the footprint of a typical current generation smart phone, provides autonomous control of four send and receive piezoelectric elements, which can operate in pitch-catch or pulse-echo modes and can undertake electro-mechanical impedance measurements for transducer and structural diagnostics. Modules are designed to operate synchronously with other units, via an optical link, to accommodate larger transducer arrays. The module also caters for fibre optic sensing of acoustic waves with four intensity-based optical inputs. Temperature and electrical resistance strain gauge inputs as well as external triggering functionality are also provided. The development of a Matlab hardware object allows users to easily access the full hardware functionality of the device and provides enormous flexibility for the creation of custom interfaces. This paper discusses the impetus for the concept, and outlines key aspects of the hardware design and the module capabilities. The efficacy of the system is demonstrated through the results of first-of-class testing, as well as laboratory AU studies on a flat plate using an array of piezoelectric elements.

IDTs have the potential of increasing the versatility of SHM systems by their multiple capabilities. Migration of the IDT technology in SHM systems and devices is reviewed in this paper. A summary review of different types of IDTs is presented and their salient features are presented in terms of applicability in the Lamb wave based SHM systems. Comprehensive review is provided concerning the implementation of IDT capabilities towards the development of SHM systems. Experimental results obtained with prototype IDTs are provided for illustration. Finally, future development directions of the IDTs dedicated to SHM systems are outlined.

Remarkable electrical properties of carbon nanotubes (CNT) have lead to increased interest in studying CNT- based devices. Many of current researches are devoted to using all kinds of carbon nanomaterials in the con- struction of sensory elements. One of the most common applications is the development of high performance, large scale sensors. Due to the remarkable conductivity of CNT's such devices represent very high sensitivity. However, there are no sufficient tools for studying and designing such sensors. The main objective of this paper is to develop and validate a multiscale numerical model for a carbon nanotubes based sensor. The device utilises the change of electrical conductivity of a nanocomposite material under applied deformation. The nanocomposite consists of a number of CNTs dispersed in polymer matrix. The paper is devoted to the analysis of the impact of spatial distribution of carbon nanotubes in polymer matrix on electrical conductivity of the sensor. One of key elements is also to examine the impact of strain on electric charge ow in such anisotropic composite structures. In the following work a multiscale electro-mechanical model for CNT - based nanocomposites is proposed. The model comprises of two length scales, namely the meso- and the macro-scale for mechanical and electrical domains. The approach allows for evaluation of macro-scale mechanical response of a strain sensor. Electrical properties of polymeric material with certain CNT fractions were derived considering electrical properties of CNTs, their contact and the tunnelling effect.

This study aims to devise multifunctional composites using fracto-mechanoluminescent (FML) materials and photoactive sensing thin films for autonomous and self-powered impact damage detection. In previous studies, multifunctional photoactive thin films were suggested as a strain sensor that does not require any external electrical source. Instead, the photoactive thin films generated direct current (DC) (or photocurrent) under ambient light, whose magnitude varied linearly with applied strain. In this study, multifunctional FML materials-photoactive thin film composites will be devised for autonomously sensing high-speed compressive strains without supplying any external photonic or electrical energy. FML materials exhibit transformative properties that emit light when its crystalline structures are fractured. The developed photoactive strain sensing thin film will be integrated with the FML materials. Thus, it is envisioned that the FML materials will emit light, which will be supplied to the photoactive sensing thin films when the high-speed compressive loadings break FML materials’ crystalline structures. First, synthesized europium tetrakit(dibenzoylmethide) triethylammonium (EuD4TEA) crystals will be embedded in the elastomeric and transparent polydimethylsiloxane (PDMS) matrix to prepare test specimens. Second, the FML properties of the EuD4TEA-PDMS composites will be characterized at various compressive strains, which will be applied by Kolsky bar testing setup. Light emission from the EuD4TEA-PDMS test specimens will be recorded using a high-speed camera. Intensity of the light emissions will be quantified via image processing techniques by taking into account pixel profiles of the high-speed camera captured images (e.g., pixel values, counts of pixels, and RGB values) at various levels of compressive strains. Lastly, the autonomous high-speed compressive sensor modules will be fabricated by integrating the EuD4TEA-PDMS composites with the photoactive thin film sensor. Self-powered sensing capability will be validated by measuring DC at various compressive strains.

Tamper-evident seals are commonly used for non-proliferation applications. A properly engineered tamper-evident seal enables the detection of unauthorized access to a protected item or a secured zone. Tamper-evident seals must be susceptible to malicious attacks. These attacks should cause irreversible and detectable damage to the seals. At the same time, tamper-evident seals must demonstrate robustness to environmental changes in order to minimize false-positive and false-negative rates under real operating conditions. The architecture of the tamper-evident seal presented in this paper features a compressive sampling (CS) acquisition scheme, which provides the seal with a means for self- authentication and self-state of health awareness. The CS acquisition scheme is implemented using a micro-controller unit (MCU) and an array of resistors engraved on a graphite oxide (GO) film. CS enables compression and encryption of messages sent from the seal to the remote reader in a non-bit sensitive fashion. As already demonstrated in our previous work through the development of a simulation framework, the CS non-bit sensitive property ensures satisfactory reconstruction of the encrypted messages sent back to the reader when the resistance values of the resistor array are simultaneously affected by modest changes. This work investigates the resistive behavior of the reduced GO film to changes in temperature and humidity when tested in an environmental chamber. The goal is to characterize the humidity and temperature range for reliable operation of a GO-based seal.

The authors have developed a capacitive-based thin film sensor for monitoring strain on mesosurfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. The measurement principle is based on a measurable change in capacitance provoked by strain. In the case of bidirectional in-plane strain, the sensor output contains the additive measurement of both principal strain components. In this paper, we present an algorithm for retrieving unidirectional strain from the bidirectional measurements of the capacitive-based thin film sensor when place in a hybrid dense sensor network with state-of-the-art unidirectional strain sensors. The algorithm leverages the advantages of a hybrid dense network for application of the thin film sensor to reconstruct the surface strain maps. A bidirectional shape function is assumed, and it is differentiated to obtain expressions for planar strain. A least squares estimator (LSE) is used to reconstruct the planar strain map from the networks measurements, after the system’s boundary conditions have been enforced in the model. The coefficients obtained by the LSE can be used to reconstruct the estimated strain map. Results from numerical simulations and experimental investigations show good performance of the algorithm.

New advances in nanotechnology and material processing is creating opportunities for the design and fabrication of a new generation of thin film sensors that can used to assess structural health. In particular, thin film sensors attached to large areas of the structure surface has the potential to provide spatially rich data on the performance and health of a structure. This study focuses on the development of a fully integrated strain sensor that is fabricated on a flexible substrate for potentially use in sensing skins. This is completed using a carbon nanotube-polymer composite material that is patterned on a flexible polyimide substrate using optical lithography. The piezoresistive carbon nanotube elements are integrated into a complete sensing system by patterning copper electrodes and integrating off-the-shelf electrical components on the flexible film for expanded functionality. This diverse material utilization is realized in a versatile process flow to illustrate a powerful toolbox for sensing severity, location, and failure mode of damage on structural components. The fully integrated patterned carbon nanotube strain sensor is tested on a quarter-scale, composite beam column connection. The results and implications for future structural damage detection are discussed.

Visualizing mechanical strain/stress changes is an emerging area in structural health monitoring. Several ways are available for strain change visualization through the color/brightness change of the materials subjected to the mechanical stresses, for example, using mechanoluminescence (ML) materials and mechanoresponsive polymers (MRP). However, these approaches were not effectively applicable for civil engineering system yet, due to insufficient sensitivity to low-level strain of typical civil structures and limitation in measuring both static and dynamic strain. In this study, design and validation for high-sensitivity strain visualization using electroluminescence technologies are presented. A high-sensitivity Wheatstone bridge, of which bridge balance is precisely controllable circuits, is used with a gain-adjustable amplifier. The monochrome electroluminescence (EL) technology is employed to convert both static and dynamic strain change into brightness/color change of the EL materials, through either brightness change mode (BCM) or color alternation mode (CAM). A prototype has been made and calibrated in lab, the linearity between strain and brightness change has been investigated.

In the field of non-destructive evaluation, defect detection and visualization can be performed exploiting different techniques relying either on an active or a passive approach. In the following paper the passive technique is investigated due to its numerous advantages and its application to thermography is explored. In previous works, it has been shown that it is possible to reconstruct the Green’s function between any pair of points of a sensing grid by using noise originated from diffuse fields in acoustic environments. The extraction of the Green’s function can be achieved by cross-correlating these random recorded waves. Averaging, filtering and length of the measured signals play an important role in this process. This concept is here applied in an NDE perspective utilizing thermal fluctuations present on structural materials. Temperature variations interacting with thermal properties of the specimen allow for the characterization of the material and its health condition. The exploitation of the thermographic image resolution as a dense grid of sensors constitutes the basic idea underlying passive thermography. Particular attention will be placed on the creation of a proper diffuse thermal field, studying the number, placement and excitation signal of heat sources. Results from numerical simulations will be presented to assess the capabilities and performances of the passive thermal technique devoted to defect detection and imaging of structural components.

3D temperature field reconstruction is of practical interest to the power, transportation and aviation industries and it also opens up opportunities for real time control or optimization of high temperature fluid or combustion process. In our paper, a new distributed optical fiber sensing system consisting of a series of elements will be used to generate and receive acoustic signals. This system is the first active temperature field sensing system that features the advantages of the optical fiber sensors (distributed sensing capability) and the acoustic sensors (non-contact measurement). Signals along multiple paths will be measured simultaneously enabled by a code division multiple access (CDMA) technique. Then a proposed Gaussian Radial Basis Functions (GRBF)-based approach can approximate the temperature field as a finite summation of space-dependent basis functions and time-dependent coefficients. The travel time of the acoustic signals depends on the temperature of the media. On this basis, the Gaussian functions are integrated along a number of paths which are determined by the number and distribution of sensors. The inversion problem to estimate the unknown parameters of the Gaussian functions can be solved with the measured times-of-flight (ToF) of acoustic waves and the length of propagation paths using the recursive least square method (RLS). The simulation results show an approximation error less than 2% in 2D and 5% in 3D respectively. It demonstrates the availability and efficiency of our proposed 3D temperature field reconstruction mechanism.

Thailand is notoriously exposed to several natural disasters, from heavy thunder storms to earthquakes and tsunamis, since it is located in the tropical area and has tectonic cracks underneath the ground. Besides these hazards flooding, despite being less severe, occurs frequently, stays longer than the other disasters, and affects a large part of the national territory. Recently in 2011 have also been recorded the devastating effects of major flooding causing the economic damages and losses around 50 billion dollars. Since Thailand is particularly exposed to such hazards, research institutions are involved in campaigns about monitoring, prevention and mitigation of the effects of such phenomena, with the aim to secure and protect human lives, and secondly, the remarkable cultural heritage. The present paper will first make a brief excursus on the main Thailand projects aimed at the mitigation of natural disasters, referring to projects of national and international relevance, being implemented, such as the ESCAP1999 (flow regime regulation and water conservation). Adaptable devices such as foldable flood barriers and hydrodynamically supported temporary banks have been utilized when flooding. In the second part of the paper, will be described some new ideas concerning the use of smart and biomimicking column structures capable of high-velocity water interception and velocity detection in the case of tsunami. The pole configuration is composite cylindrical shell structure embedded with piezoceramic sensor. The vortex shedding of the flow around the pole induces the vibration and periodically strains the piezoelectric element, which in turn generates the electrical sensorial signal. The internal space of the shell is filled with elastic foam to enhance the load carrying capability due to hydrodynamic application. This more rigid outer shell inserted with soft core material resemble lotus stem in nature in order to prolong local buckling and ovalization of column cross-section when subjected to flexural moments. Finally it will be proposed as a warning and mitigation system that can be used on sea coasts vulnerable to potential tsunamis.

We explore the use of fractional continuum models to perform 2D tomographic imaging of interest for applications to structural damage detection. Fractional models allow a more flexible approach to field transport simulations in inhomogeneous media and, under certain conditions, enable capturing physical phenomena that cannot be accounted for by integer order models. This study addresses the specific example of heat conduction in a two- dimensional inhomogeneous domain and the reconstruction of the internal parameters based on temperature boundary measurements. The field evolution is assumed to be governed by a fractional diffusion equation while the parameter identification problem is formulated in inverse form. The reconstruction is performed with respect to the characteristic parameters of the fractional model, with particular attention to the order of the derivative. Numerical results show that the inverse procedure correctly identifies the spatial location of the inhomogeneity and, to some extent, the order of the fractional model.

In this paper, an unpowered wireless ultrasound tomography system is presented. The system consists of two subsystems; the wireless interrogation unit (WIU) and three wireless nodes installed on the structure. Each node is designed to work in generation and sensing modes, but operates at a specific microwave frequency. Wireless transmission of the ultrasound signals between the WIU and the wireless nodes is achieved by converting ultrasound signals to microwave signals and vice versa, using a microwave carrier signal. In the generation mode, both a carrier signal and an ultrasound modulated microwave signal are transmitted to the sensor nodes. Only the node whose operating frequency matches the carrier signal will receive these signals and demodulate them to recover the original ultrasound signal. In the sensing mode, a microwave carrier signal with two different frequency components matching the operating frequencies of the sensor nodes is broadcasted by the WIU. The sensor nodes, in turn, receive the corresponding carrier signals, modulate it with the ultrasound sensing signal, and wirelessly transmit the modulated signal back to the WIU. The demodulation of the sensing signals is performed in the WIU using a digital signal processing. Implementing a software receiver significantly reduces the complexity and the cost of the WIU. A wireless ultrasound tomography system is realized by interchanging the carrier frequencies so that the wireless transducers can take turn to serve as the actuator and sensors.

Electrical impedance tomography (EIT) has incredible potential for structural health monitoring (SHM) when applied to structures in which mechanical damage is coupled with changes in electrical conductivity. Practically, however, the potential of EIT for SHM is largely nullified by requiring both non-negligible computational resources and accurate initial conductivity estimates. By working in resistivity instead of conductivity and constraining the change in resistivity to be a series of two-dimensional sine waves, a novel resistivity-based EIT formulation is herein developed that significantly abates the computational requirements of EIT and is independent of initial estimates. This approach is explored analytically and then demonstrated experimentally by locating impact damage to a glass fiber/epoxy/carbon black laminate.

Digital sampling moiré (DSM) method is a newly developed vision-based technique that uses the phase information of moiré fringes to measure movement of an object. The moiré fringes are generated from a sequence of digital images, containing a cosinusoidal grating pattern attached to the object, through down-sampling and interpolation. As the moiré fringes can magnify the pattern’s movement, this technique is expected to provide more accurate displacement measurement than the other vision based approaches. In this study, a method combining DSM with monocular videogrammetric technique is proposed to measure in-plane rotation and translation of structures. In this method, images of a two-dimensional (2D) grating pattern attached to a moving structure are acquired and decomposed into two perpendicular gratings through Fourier transform. The DSM method is used to obtain 2D phase distributions of the gratings which provide an estimation of physical coordinates for those points on the grating pattern. A previously developed monocular videogrammetric technique can then be used to obtain the rotation angle and the translation of the grating pattern. The proposed method is validated using both numerical simulation and laboratory tests.

Motivated by a need to reduce energy consumption in wireless sensors for vibration-based structural health monitoring (SHM) associated with data acquisition and transmission, this paper puts forth a novel approach for undertaking operational modal analysis (OMA) and damage localization relying on compressed vibrations measurements sampled at rates well below the Nyquist rate. Specifically, non-uniform deterministic sub-Nyquist multi-coset sampling of response acceleration signals in white noise excited linear structures is considered in conjunction with a power spectrum blind sampling/estimation technique which retrieves/samples the power spectral density matrix from arrays of sensors directly from the sub-Nyquist measurements (i.e., in the compressed domain) without signal reconstruction in the time-domain and without posing any signal sparsity conditions. The frequency domain decomposition algorithm is then applied to the power spectral density matrix to extract natural frequencies and mode shapes as a standard OMA step. Further, the modal strain energy index (MSEI) is considered for damage localization based on the mode shapes extracted directly from the compressed measurements. The effectiveness and accuracy of the proposed approach is numerically assessed by considering simulated vibration data pertaining to a white-noise excited simply supported beam in healthy and in 3 damaged states, contaminated with Gaussian white noise. Good accuracy is achieved in estimating mode shapes (quantified in terms of the modal assurance criterion) and natural frequencies from an array of 15 multi-coset devices sampling at a 70% slower than the Nyquist frequency rate for SNRs as low as 10db. Damage localization of equal level/quality is also achieved by the MSEI applied to mode shapes derived from noisy sub-Nyquist (70% compression) and Nyquist measurements for all damaged states considered. Overall, the furnished numerical results demonstrate that the herein considered sub-Nyquist sampling and multi-sensor power spectral density estimation techniques coupled with standard OMA and damage detection approaches can achieve effective SHM from significantly fewer noisy acceleration measurements.

This paper describes an information repository to support bridge monitoring applications on a cloud computing platform. Bridge monitoring, with instrumentation of sensors in particular, collects significant amount of data. In addition to sensor data, a wide variety of information such as bridge geometry, analysis model and sensor description need to be stored. Data management plays an important role to facilitate data utilization and data sharing. While bridge information modeling (BrIM) technologies and standards have been proposed and they provide a means to enable integration and facilitate interoperability, current BrIM standards support mostly the information about bridge geometry. In this study, we extend the BrIM schema to include analysis models and sensor information. Specifically, using the OpenBrIM standards as the base, we draw on CSI Bridge, a commercial software widely used for bridge analysis and design, and SensorML, a standard schema for sensor definition, to define the data entities necessary for bridge monitoring applications. NoSQL database systems are employed for data repository. Cloud service infrastructure is deployed to enhance scalability, flexibility and accessibility of the data management system. The data model and systems are tested using the bridge model and the sensor data collected at the Telegraph Road Bridge, Monroe, Michigan.

Structural Health Monitoring (SHM) can be a vital tool for effective bridge management. Combining large data sets from multiple sources to create a data-driven decision-making framework is crucial for the success of SHM. This paper presents a big data analytics framework that combines multiple data sets correlated with functional relatedness to convert data into actionable information that empowers risk-based decision-making. The integrated data environment incorporates near real-time streams of semi-structured data from remote sensors, historical visual inspection data, and observations from structural analysis models to monitor, assess, and manage risks associated with the aging bridge inventories. Accelerated processing of dataset is made possible by four technologies: cloud computing, relational database processing, support from NOSQL database, and in-memory analytics. The framework is being validated on a railroad corridor that can be subjected to multiple hazards. The framework enables to compute reliability indices for critical bridge components and individual bridge spans. In addition, framework includes a risk-based decision-making process that enumerate costs and consequences of poor bridge performance at span- and network-levels when rail networks are exposed to natural hazard events such as floods and earthquakes. Big data and high-performance analytics enable insights to assist bridge owners to address problems faster.

The development of new, smart materials capable of intrinsically detecting and communicating the occurrence of external loads and resultant damage present in a material will be crucial in the advancement of future structural health monitoring (SHM) and nondestructive evaluation (NDE) technologies. Traditionally, many SHM and NDE approaches have relied on the use of physical sensors to monitor a structure for damage, but are often hindered by their requirements for power consumption and large-scale data collection. In this work, we seek to evaluate the effectiveness of ultrasmall, white-light emitting Cadmium Selenide quantum dots (CdSe QDs) as an alternative to providing in-situ material state monitoring capabilities, while also aiming to reduce reliance on data collection and power consumption to effectively monitor a material and structure for damage. To achieve this goal, CdSe QDs are embedded in an optically clear epoxy composite matrix and exposed to external mechanical loadings. Initial results show a corresponding relationship between the shifts in observed emission spectra and external load for samples containing CdSe QDs. The effectiveness of CdSe QDs as a surface strain gauge on aluminum and fiberglass are also investigated in this paper. By monitoring changes in the emission spectra for materials containing CdSe QDs before, during and after the application of external loads, the effectiveness of CdSe QDs for communicating the occurrence of external loads acting on a material and detecting changes in material state is evaluated.

Cement-based materials are widely used in infrastructure facilities. However, often the degradation of structures leads to the failures earlier than designed service life. Thus, non-destructive testing techniques are urgently needed to evaluate the health information of the structures. In this paper, the implementation of Electrical Resistance Tomography (ERT) was investigated. This low cost, radiation free and easy to perform modality is based on measuring the electrical properties of the material under test and using that to evaluate the existence of defects in that material. It uses a set of boundary potentials and injected current to reconstruct the conductivity distribution. An automatic measurement system was developed and surface damages as well as subsurface damages on mortar specimens were investigated. The reconstructed images were capable to show the presence and the location of the damages.

Cement-based smart sensors appear particularly suitable for monitoring applications, due to their self-sensing abilities, their ease of use, and their numerous possible field applications. The addition of conductive carbon nanofillers into a cementitious matrix provides the material with piezoresistive characteristics and enhanced sensitivity to mechanical alterations. The strain-sensing ability is achieved by correlating the variation of external loads or deformations with the variation of specific electrical parameters, such as the electrical resistance. Among conductive nanofillers, carbon nanotubes (CNTs) have shown promise for the fabrication of self-monitoring composites. However, some issues related to the filler dispersion and the mix design of cementitious nanoadded materials need to be further investigated. For instance, a small difference in the added quantity of a specific nanofiller in a cement-matrix composite can substantially change the quality of the dispersion and the strain sensitivity of the resulting material. The present research focuses on the strain sensitivity of concrete, mortar and cement paste sensors fabricated with different amounts of carbon nanotube inclusions. The aim of the work is to investigate the quality of dispersion of the CNTs in the aqueous solutions, the physical properties of the fresh mixtures, the electromechanical properties of the hardened materials, and the sensing properties of the obtained transducers. Results show that cement-based sensors with CNT inclusions, if properly implemented, can be favorably applied to structural health monitoring.

With the development of soft materials for applications in flexible tactile sensors, metal particles/insulated polymer composites have been studied for many years. This article proposes a method to prepare carbon iron particles (CIPs)/polydimethylsiloxane (PDMS) conductive composite with low percolation threshold and highly piezoresistive stain sensitivity. CIPs-PDMS composites with various filler volume fraction were cured under a magnetic field over 1.0 T to create chain-like structure resulting in anisotropy of conductive materials. The electrical resistivity for the longitudinal direction were measured as a function of filler volume fraction to understand the electrical percolation behavior. In this study, the percolation threshold of CIPs-PDMS composite cured under a magnetic field can be as low as 0.1 vol.%, which is much less than most of those studies in particulate composites. Meanwhile, the effects of compressive strain on the electrical properties of CIPs-PDMS composites were also investigated. The strain sensitivity depends on filler volume fraction and decreases with the increasing of compressive strain. It has been found that the composites containing a small amount of CI particles curing under a magnetic field exhibit a high strain sensitivity of over 150. The microstructures were measured by using a scanning electron microscope (SEM), and the results were also reported in this paper.

The newly developed smartphone application, named RINO, in this study allows measuring absolute dynamic displacements and processing them in real time using state-of-the-art smartphone technologies, such as high-performance graphics processing unit (GPU), in addition to already powerful CPU and memories, embedded high-speed/ resolution camera, and open-source computer vision libraries. A carefully designed color-patterned target and user-adjustable crop filter enable accurate and fast image processing, allowing up to 240fps for complete displacement calculation and real-time display. The performances of the developed smartphone application are experimentally validated, showing comparable accuracy with those of conventional laser displacement sensor.

The absence of expansion joints in Continuous Welded Rail (CWR) has created the need for the railroad industry to determine the in-situ level of thermal stresses so as to prevent train accidents caused by rail buckling in hot weather and by rail breakage in cold weather. The development of non-destructive or semi-destructive methods for determining the level of thermal stresses in rails is today a high research priority. This study explores the known hole-drilling method as a possible solution to this problem. A new set of calibration coefficients to compute the relieved stress field with the finer hole depth increments was determined by a 3D Finite Element Analysis that modeled the entire hole geometry, including the mechanics of the hole bottom and walls. To compensate the residual stress components, a linear relationship was experimentally established between the longitudinal and the vertical residual stresses of two common sizes of rails, the 136RE and the 141RE, with statistical significance. This result was then utilized to isolate the longitudinal thermal stress component in hole-drilling tests conducted on the 136RE and 141RE thermally-loaded rails at the Large-scale CWR Test-bed of UCSD’s Powell Research Laboratories. The results from the Test-bed showed that the hole-drilling procedure, with the appropriate residual stress compensation, can indeed estimate the in-situ thermal stresses to achieve a ±5°F accuracy of Neutral Temperature determination with a 90% statistical confidence, which is the desired industry gold standard.

Vibration reduction in mechanical systems is an open issue. Indeed the stresses associated to the dynamic amplifications can affect systems’ performances and life cycle time. Nowadays the possibility to integrate sensors, actuators and control logics, in order to create a smart structure able to reduce vibration levels in all the operating conditions, represents a very attractive solution. This paper presents a comparison between two control strategies for vibration suppression in flexible structures: the Direct Velocity Feedback (DVF) and the Active Tuned Mass Damper (ATMD). As test case, the model of a smart cable for electric power distribution has been investigated.

Most transportation agencies now collect pavement roughness data using the inertial profilers, which requires instrumented vehicles and technicians with specialized training to interpret the results. The extensive labor requirements of the profiling activities limit data collection for portions of the national highway system to at most once per year, resulting in outdated roughness data for decision making of maintenance needs. In this paper, a real-time roughness evaluation method was developed by linking the output of durable in-pavement strain sensors to road roughness level. The durable in-pavement sensors will continuously provide information of road roughness in real time after they are installed and calibrated during the road construction until the service life of the associated pavement. Field tests validated the developed strain method by comparison with standard inertial profiling method and the connected-vehicle method. The comparison of the results from the field tests approves the effectiveness of the developed road roughness evaluation method using in-pavement strain sensors, which can be further applied practically for needed concrete pavements.

In the oil and gas industry, pipeline integrity is a serious concern due to the consequences of pipeline failure. External corrosion was identified as one of the main causes of pipeline failures worldwide. A solution that addresses the issue of detecting and quantifying corrosivity of environment for application to existing exposed pipelines has been developed. The proposed sensor consists of an electric circuit and a sensing array connected to the circuit. The sensing array is an assembly of strips made of a metal identical to that of the pipe, having the same length and width, but different thicknesses. The sensing array is exposed to the same environment as the pipe. As corrosion propagates in the metal strips of the array, it corrodes the metal until it finally breaks the metal strip apart resulting in a discontinuity in the circuit. The sensor circuit is energized using electromagnetic field, and its function is to indicate which strips in the array are fully corroded. Visual indication is provided to the operator via LEDs. The proposed sensor can be installed on existing pipelines without altering the pipe structure or disturbing the production process. It is passive and has low maintenance requirements. Circuit design was validated through lab experiments. Results obtained from experiments were consistent with simulation results.

A corrosion monitoring system for steel using Fiber Bragg grating (FBG) sensors is proposed. FBG sensors were protected by hypodermic tubes and a layer of adhesive. The increase in volume caused by the presence of corrosion product introduces strain that can be monitored by FBG sensors. Experimental results showed a positive correlation between the strain and corrosion product, and the change in central wavelength has the potential to serve as an indicator for material weight loss due to corrosion.

The increasing number, size, and complexity of nuclear facilities deployed worldwide are increasing the need to maintain readiness and develop innovative sensing materials to monitor important to safety structures (ITS). In the past two decades, an extensive sensor technology development has been used for structural health monitoring (SHM). Technologies for the diagnosis and prognosis of a nuclear system, such as dry cask storage system (DCSS), can improve verification of the health of the structure that can eventually reduce the likelihood of inadvertently failure of a component. Fiber optical sensors have emerged as one of the major SHM technologies developed particularly for temperature and strain measurements. This paper presents the development of optical equipment that is suitable for ultrasonic guided wave detection for active SHM in the MHz range. An experimental study of using fiber Bragg grating (FBG) as acoustic emission (AE) sensors was performed on steel blocks. FBG have the advantage of being durable, lightweight, and easily embeddable into composite structures as well as being immune to electromagnetic interference and optically multiplexed. The temperature effect on the FBG sensors was also studied. A multi-channel FBG system was developed and compared with piezoelectric based AE system. The paper ends with conclusions and suggestions for further work.

Corrosion is a multi-billion dollar problem faced by industry. The ability to monitor the hidden metallic structure of an aircraft for corrosion could result in greater availability of existing aircraft fleets. Silica exposed-core microstructured optical fiber sensors are inherently suited towards this application, as they are extremely lightweight, robust, and suitable both for distributed measurements and for embedding in otherwise inaccessible corrosion-prone areas. By functionalizing the fiber with chemosensors sensitive to corrosion by-products, we demonstrate in-situ kinetic measurements of accelerated corrosion in simulated aluminum aircraft joints.

Pipelines are naturally vulnerable to operational, environmental and man-made effects such as internal erosion and corrosion; mechanical deformation due to geophysical risks and ground movements; leaks from neglect and vandalism; as well as encroachments from nearby excavations or illegal intrusions. The actual detection and localization of incipient and advanced faults in pipelines is a very difficult, expensive and inexact task. Anything that operators can do to mitigate the effects of these faults will provide increased reliability, reduced downtime and maintenance costs, as well as increased revenues. This talk will review the on-line monitoring of an extensive network of oil pipelines in service in Colombia using optical fiber Bragg grating (FBG) strain sensors for the measurement of strains and bending caused by geohazard risks such as soil movements, landslides, settlements, flooding and seismic activity. The FBG sensors were mounted on the outside of the pipelines at discrete locations where geohazard risk was expected. The system has been in service for the past 3 years with over 1,000 strain sensors mounted. The technique has been reliable and effective in giving advanced warning of accumulated pipeline strains as well as possible ruptures.

The infrastructures for oil and gas production and distribution need reliable monitoring systems. The risks for pipelines, in particular, are not only limited to natural disasters (landslides, earthquakes, extreme environmental conditions) and accidents, but involve also the damages related to criminal activities, such as oil theft. The existing monitoring systems are not adequate for detecting damages from oil theft, and in several occasion the illegal activities resulted in leakage of oil and catastrophic environmental pollution. Systems based on fiber optic FBG (Fiber Bragg Grating) sensors present a number of advantages for pipeline monitoring. FBG sensors can withstand harsh environment, are immune to interferences, and can be used to develop a smart system for monitoring at the same time several physical characteristics, such as strain, temperature, acceleration, pressure, and vibrations. The monitoring station can be positioned tens of kilometers away from the measuring points, lowering the costs and the complexity of the system. This paper describes tests on a sensor, based on FBG technology, developed specifically for detecting damages of pipeline due to illegal activities (drilling of the pipes), that can be integrated into a smart monitoring chain.

This paper investigates a new low-power structural health monitoring (SHM) strategy where fiber Bragg grating (FBG) rosettes can be used to continuously monitor for changes in a host structure’s principal strain direction, suggesting damage and thus enabling the immediate triggering of a higher power acoustic emissions (AE) sensor to provide for better characterization of the damage. Unlike traditional “always on” AE platforms, this strategy has the potential for low power, while the wireless communication between different sensor types supports the Internet of Things (IoT) approach. A combination of fiber-optic sensor rosettes for strain monitoring and a fiber-optic sensor for acoustic emissions monitoring was attached to a sample and used to monitor crack initiation. The results suggest that passive principal strain direction monitoring could be used as a damage initiation trigger for other active sensing elements such as acoustic emissions. In future work, additional AE sensors can be added to provide for damage location; and a strategy where these sensors can be powered on periodically to further establish reliability while preserving an energy efficient scheme can be incorporated.

Damage detection is an important application of structural health monitoring. With the recent development of sensing technology, additional information about structures, angular velocity, has become available. In this paper, the angular velocity signals obtained from gyroscopes are modeled as an autoregressive (AR) model. The damage sensitive features (DSFs) are defined as a function of the AR coefficients. It is found that the mean values of the DSF for the damaged and undamaged signals are different. Also, we show that the angular velocity- based AR model has a linear relationship with the acceleration-based AR model. To test the proposed damage detection method, the algorithm has been tested with the experimental data from a recent shake table test where the damage is introduced systemically. The results indicate that the change of DSF means is statistically significant, and the angular velocity-based DSFs are sensitive to damage.

Structural health monitoring requires engineers to understand the state of a structure from its observed response. When this information is uncertain, Bayesian probability theory provides a consistent framework for making inference. However, structural engineers are often unenthusiastic about Bayesian logic and prefer to make inference using heuristics. Herein we propose a quantitative method for logical inference based on a formal analogy between linear elastic mechanics and Bayesian inference with Gaussian variables. We start by discussing the estimation of a single parameter under the assumption that all of the uncertain quantities have a Gaussian distribution and that the relationship between the observations and the parameter is linear. With these assumptions, the analogy is stated as follows: the expected value of the considered parameter corresponds to the position of a bar with one degree of freedom and uncertain observations of the parameter are modelled as linear elastic springs placed in series or parallel. If we want to extend the analogy to multiple parameters, we simply have to express the potential energy of the mechanical system associated to the inference problem. The expected value of the parameters is then calculated by minimizing that potential energy. We conclude our contribution by presenting the application of mechanical equivalent to a real-life case study in which we seek the elongation trend of a cable belonging to Adige Bridge, a cable-stayed bridge located North of Trento, Italy.

Operational modal analysis (OMA) is to extract the dynamic characteristics of structures based on vibration responses of structures without considering the excitation measurement. In this study both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of a test structure ( a 3- story steel frame subjected to a series of earthquake and white noise excitations back to back) using both input and output response data or output only measurement and identify the damage location. For the modal-based identification, the multi-variant autoregressive model (MV-AR model) is used to identify the dynamic characteristics of structure. The MV-AR model parameters are then used to develop the vectors of autoregressive model and Mahalanobis distance, and then to identify the damage features and locate the damage. From the signal-based feature identification two damage features will be discussed: (1) the enhancement of time-frequency analysis of acceleration responses, and (2) WPT based energy damage indices. Discussion on the correlation of the extract local damage features from measurements with the global damage indices, such as null-space and subspace damage indices, is also made.

Structures are always exposed to the surrounding environment. The environmental variability (especially fluctuation in temperature) creates noticeable variations in structural modal properties. Two major mechanisms from temperature can cause uncertainties in natural frequency and mode shape measurements: i) the changes of material properties (elastic modulus) by temperature variation, and ii) the stress stiffening effects by temperature induced axial loading. Also, changes of boundary condition may cause variation in modal properties as well. In model updating, not considering these environmental effects may cause false identification on structural damage, thus compromises the accuracy of the updating results. This study presents a finite element model updating technique which can address the issue of varying environment including temperature variation and boundary condition changes. Temperature and boundary condition information is incorporated into the stiffness formulation of the finite element model. A numerical study on updating a bridge model subjected to damage and environmental changes is presented to demonstrate the effectiveness of the proposed method.

Structural health monitoring enables structural information to be acquired through sensing technology, and is of need to early detect problems and damages in structures. Health monitoring strategies are often realized through a combination of qualitative sensing systems and high-performance structural integrity assessment methods. Structural deviations can be then effectively identified by interpreting the raw sensor measurements using signal processing techniques. The objective of this study is to develop a new structural health monitoring method that applies a matrix factorization algorithm to a time-frequency representation of multi-channel signals measured from a structure. This method processes vibrational input and/or output responses of structures to improve raw data quality, to estimate structural responses, to derive signal features, and to detect structural variations. For example, the proposed method can reduce the signal noise by utilizing first few principle vectors to reconstruct the measured signals. For frequency-domain responses, this method can smooth the phase to obtain a better input-output relationship of a structure. Additionally, the method removes abnormal signals in time series, allowing better understanding of structural behavior. Due to communication loss, this method is able to recover lost data from other channel measurements in a structure. Moreover, the proposed method transforms the signal components into a specific domain and then yield meaningful characteristics. All these features are numerically verified using experimental data, and the proposed method permits more detailed investigation of structural behavior.

The Magneto-rheological (MR) dampers have been widely used in many building and bridge structures against earthquake and wind loadings due to its advantages including mechanical simplicity, high dynamic range, low power requirements, large force capacity, and robustness. However, research about structural damage detection methods for MR damper controlled structures is limited. This paper aims to develop a real-time structural damage detection method for MR damper controlled structures. A novel state space model of MR damper controlled structure is first built by combining the structure’s equation of motion and MR damper’s hyperbolic tangent model. In this way, the state parameters of both the structure and MR damper are added in the state vector of the state space model. Extended Kalman filter is then used to provide prediction for state variables from measurement data. The two techniques are synergistically combined to identify parameters and track the changes of both structure and MR damper in real time. The proposed method is tested using response data of a three-floor MR damper controlled linear building structure under earthquake excitation. The testing results show that the adaptive extended Kalman filter based approach is capable to estimate not only structural parameters such as stiffness and damping of each floor, but also the parameters of MR damper, so that more insights and understanding of the damage can be obtained. The developed method also demonstrates high damage detection accuracy and light computation, as well as the potential to implement in a structural health monitoring system.

Ultrasound generation from an optical fiber, based on the photoacoustic principle, could have broad applications, such as ultrasound nondestructive test (NDT) and biomedical ultrasound imaging. There are many advantages of these fiber-optic ultrasonic transducers, such as small size, light weight, ease of use, and immunity to electromagnetic interference. This paper will demonstrate a novel structure which the ultrasound signal is generated on the sidewall of the fiber. Two experimental configurations of the fiber-optic sidewall ultrasonic transducer are discussed. One is that a photoacoustic material is directly coated on the sidewall of the optical fiber. The other one is that the photoacoustic material is directly coated on an aluminum plate and the sidewall fiber is buried in the material. By using this novel sidewall ultrasound generator, we can effectively generate ultrasound signal at multiple, particular locations along one fiber.

Artificial hair sensors have been developed in the Air Force Research Laboratory for use in prediction of local flow around airfoils and subsequent use in gust rejection applications. The on-going sensor development is based on a micro-sized unmanned vehicle, resulting in a sensor design that is sensitive in that aircraft’s nominal flight condition (speed). However, the active, or operating, region of the artificial hair sensor concept is highly dependent on the geometry and properties of the hair, capillary, and carbon nanotubes that make up the sensor design. This paper aims at expanding the flow measurement concept using artificial hair sensors to UAVs with different dimensions by properly sizing the parameters of the sensors, according to the nominal flight conditions of the UAVs. In this work, the hair, made of glass fiber, will be modeled as a cantilever beam with an elastic foundation, subject to external distributed aerodynamic drag. Hair length, diameter, capillary depth, and carbon nanotube length will be scaled by keeping the maximum strain of the carbon nanotubes constant for different sensors under different working conditions. Numerical studies will demonstrate the feasibility of the scaling methodology by designing artificial hair sensors for UAVs with different dimensions and flight conditions, starting from a baseline sensor design.

Unmanned aerial vehicles (UAVs) can serve as a powerful mobile sensing platform for assessing the health of civil infrastructure systems. To date, the majority of their uses have been dedicated to vision and laser-based spatial imaging using on-board cameras and LiDAR units, respectively. Comparatively less work has focused on integration of other sensing modalities relevant to structural monitoring applications. The overarching goal of this study is to explore the ability for UAVs to deploy a network of wireless sensors on structures for controlled vibration testing. The study develops a UAV platform with an integrated robotic gripper that can be used to install wireless sensors in structures, drop a heavy weight for the introduction of impact loads, and to uninstall wireless sensors for reinstallation elsewhere. A pose estimation algorithm is embedded in the UAV to estimate the location of the UAV during sensor placement and impact load introduction. The Martlet wireless sensor network architecture is integrated with the UAV to provide the UAV a mobile sensing capability. The UAV is programmed to command field deployed Martlets, aggregate and temporarily store data from the wireless sensor network, and to communicate data to a fixed base station on site. This study demonstrates the integrated UAV system using a simply supported beam in the lab with Martlet wireless sensors placed by the UAV and impact load testing performed. The study verifies the feasibility of the integrated UAV-wireless monitoring system architecture with accurate modal characteristics of the beam estimated by modal analysis.

Ultrasonic temperature measurements have been developed and widely applied in non-contact temperature tests in many industries. However, using optical fibers to build ultrasound generators are novel. This paper reports this new fiber optic ultrasonic system based on the generator of gold nanoparticles/polydimethylsiloxane (PDMS) composites. The optical acoustic system was designed to test the change of temperature on the aluminum plate and the temperature of the torch in the air. This paper explores the relationship between the ultrasonic transmission and the change of temperature. From the experimental results, the trend of ultrasonic speed was different in the aluminum plate and air with the change of temperature. Since the system can measure the average temperature of the transmission path, it will have significant influence on simulating the temperature distribution.

A non-destructive technique for inspection of a Bismaleimide (BMI) composite is presented using an optical fiber sensor. High performance BMI composites are used for Aerospace application for their mechanical strength. They are also used as an alternative to toughened epoxy resins. A femtosecond-laser-inscribed Intrinsic Fabry-Perot Interferometer (IFPI) sensor is used to perform real time cure monitoring of a BMI composite. The composite is cured using the out-of-autoclave (OOA) process. The IFPI sensor was used for in-situ monitoring; different curing stages are analyzed throughout the curing process. Temperature-induced-strain was measured to analyze the cure properties. The IFPI structure comprises of two reflecting mirrors inscribed on the core of the fiber using a femtosecond-laser manufacturing process. The manufacturing process makes the sensor thermally stable and robust for embedded applications. The sensor can withstand very high temperatures of up to 850 °C. The temperature and strain sensitivities of embedded IFPI sensor were measured to be 1.4 pm/μepsilon and 0.6 pm/μepsilon respectively.

Fiber Bragg Grating (FBG) sensors measure deviation in a reflected wavelength of light to detect in-situ strain. These sensors are immune to electromagnetic interference, and the inclusion of multiple FBGs on the same fiber allows for a seamlessly integrated sensing network. FBGs are attractive for embedded sensing in aerospace applications due to their small noninvasive size and prospect of constant, real-time nondestructive evaluation. In this study, FBG sensors are embedded in aluminum 6061 via ultrasonic additive manufacturing (UAM), a rapid prototyping process that uses high power ultrasonic vibrations to weld similar and dissimilar metal foils together. UAM was chosen due to the desire to embed FBG sensors at low temperatures, a requirement that excludes other additive processes such as selective laser sintering or fusion deposition modeling. In this paper, the embedded FBGs are characterized in terms of birefringence losses, post embedding strain shifts, consolidation quality, and strain sensing performance. Sensors embedded into an ASTM test piece are compared against an exterior surface mounted foil strain gage at both room and elevated temperatures using cyclic tensile tests.

Metal-core Piezoelectric Fiber (MPF) was shown to have great potential to be a structurally integrated sensor for structural health monitoring (SHM) applications. Compared with the typical foil strain gauge, MPF is more suitable for high frequency strain measurement and can create direct conversion of mechanical energy into electric energy without the need for complex signal conditioners or gauge bridges. In this paper, a MPF-based smart layer is developed as an embedded network of distributed strain sensors that can be surface-mounted on a thin-walled structure. Each pair of the adjacent MPFs divides the entire structure into several “virtual elements (VEs)”. By exciting the structure at the natural frequency of the VE, a “weak” formulation of the previously developed Pseudo-excitation (PE) approach based on sparse virtual element boundary measurement (VEBM) is proposed to detect the damage. To validate the effectiveness of the VEBM based approach, experiments are conducted to locate a small crack in a cantilever beam by using a MPF- based smart layer and a Laser Doppler Vibrometer (LDV). Results demonstrate that the proposed VEBM approach not only inherits the enhanced noise immunity capability of the “weak” formulation of the PE approach, but also allows a significant reduction in the number of measurement points as compared to the original version of the PE approach.

Finite element (FE) model updating is often used to associate FE models with corresponding existing structures for the condition assessment. FE model updating is an inverse problem and prone to be ill-posed and ill-conditioning when there are many errors and uncertainties in both an FE model and its corresponding measurements. In this case, it is important to quantify these uncertainties properly. Bayesian FE model updating is one of the well-known methods to quantify parameter uncertainty by updating our prior belief on the parameters with the available measurements. In Bayesian inference, likelihood plays a central role in summarizing the overall residuals between model predictions and corresponding measurements. Therefore, likelihood should be carefully chosen to reflect the characteristics of the residuals. It is generally known that very little or no information is available regarding the statistical characteristics of the residuals. In most cases, the likelihood is assumed to be the independent identically distributed Gaussian distribution with the zero mean and constant variance. However, this assumption may cause biased and over/underestimated estimates of parameters, so that the uncertainty quantification and prediction are questionable. To alleviate the potential misuse of the inadequate likelihood, this study introduced approximate Bayesian computation (i.e., likelihood-free Bayesian inference), which relaxes the need for an explicit likelihood by analyzing the behavior similarities between model predictions and measurements. We performed FE model updating based on likelihood-free Markov chain Monte Carlo (MCMC) without using the likelihood. Based on the result of the numerical study, we observed that the likelihood-free Bayesian computation can quantify the updating parameters correctly and its predictive capability for the measurements, not used in calibrated, is also secured.

In order to obtain a finite element (FE) model that can more accurately describe structural behaviors, experimental data measured from the actual structure can be used to update the FE model. The process is known as FE model updating. In this paper, a frequency response function (FRF)-based model updating approach is presented. The approach attempts to minimize the difference between analytical and experimental FRFs, while the experimental FRFs are calculated using simultaneously measured dynamic excitation and corresponding structural responses. In this study, the FRF-based model updating method is validated through laboratory experiments on a four-story shear-frame structure. To obtain the experimental FRFs, shake table tests and impact hammer tests are performed. The FRF-based model updating method is shown to successfully update the stiffness, mass and damping parameters of the four-story structure, so that the analytical and experimental FRFs match well with each other.

Advanced composite structures, such as foam core carbon fiber reinforced polymer composites, are increasingly being used in applications which require high strength, high in-plane and flexural stiffness, and low weight. However, the presence of in situ damage due to manufacturing defects and/or service conditions can complicate the failure mechanisms and compromise their strength and reliability. In this paper, the capability of detecting damages such as delaminations and foam-core separations in X-COR composite structures using non-destructive evaluation (NDE) and structural health monitoring (SHM) techniques is investigated. Two NDE techniques, flash thermography and low frequency ultrasonics, were used to detect and quantify the damage size and locations. Macro fiber composites (MFCs) were used as actuators and sensors to study the interaction of Lamb waves with delaminations and foam-core separations. The results indicate that both flash thermography and low frequency ultrasonics were capable of detecting damage in X-COR sandwich structures, although low frequency ultrasonic methods were capable of detecting through thickness damages more accurately than flash thermography. It was also observed that the presence of foam-core separations significantly changes the wave behavior when compared to delamination, which complicates the use of wave based SHM techniques. Further, a wave propagation model was developed to model the wave interaction with damages at different locations on the X-COR sandwich plate.

A superhelix is a helix that is coiled around another helix. Despite its importance for the deformation modeling of various shapes, superhelix has been considerably overlooked, in part because of its complexity and in part for the lack of an analytical formula for its arc length. In this study, we present an exact analytical formula for the arc length of a superhelix. The final expression is given in the form of an infinite sum.

As a consequence of operational efficiency because of rising energy costs, future transport systems need to be mission-adaptive. Especially in aircraft design the limits of lightweight construction, reduced aerodynamic drag and optimized propulsion are pushed further and further. The first two aspects can be addressed by using a morphing leading edge. Great economic advantages can be expected as a result of gapless surfaces which feature longer areas of laminar flow. Instead of focusing on the kinematics, which are already published in a great number of varieties, this paper emphasizes as major challenge, the qualification of a multi-material layup which meets the compromise of needed stiffness, flexibility and essential functions to match the flight worthiness requirements, such as erosion shielding, impact safety, lighting protection and de-icing. It is the aim to develop an gapless leading edge device and to prepare the path for higher technology readiness levels resulting in an airborne application. During several national and European projects the DLR developed a gapless smart droop nose concept, which functionality was successfully demonstrated using a two-dimensional 5 m in span prototype in low speed (up to 50 m/s) wind tunnel tests. The basic structure is made of commercially available and certified glass-fiber reinforced plastics (GFRP, Hexcel Hexply 913). This paper presents 4-point bending tests to characterize the composite with its integrated functions. The integrity and aging/fatigue issues of different material combinations are analyzed by experiments. It can be demonstrated that only by adding functional layers the mentioned requirements such as erosion-shielding or de-icing can be satisfied. The total thickness of the composite skin increases by more than 100 % when required functions are integrated as additional layers. This fact has a tremendous impact on the maximum strain of the outer surface if it features a complete monolithic build-up. Based on experimental results a numerical model can be set up for further structural optimizaton of the multi-functional laminate.

An active area of research in adaptive structures focuses on the use of continuous wing shape changing methods as a means of replacing conventional discrete control surfaces and increasing aerodynamic efficiency. Although many shape-changing methods have been used since the beginning of heavier-than-air flight, the concept of performing camber actuation on a fully-deformable airfoil has not been widely applied. A fundamental problem of applying this concept to real-world scenarios is the fact that camber actuation is a continuous, time-dependent process. Therefore, if camber actuation is to be used in a closed-loop feedback system, one must be able to determine the instantaneous airfoil shape as well as the aerodynamic loads at all times. One approach is to utilize a new type of artificial hair sensors developed at the Air Force Research Laboratory to determine the flow conditions surrounding deformable airfoils. In this work, the hair sensor measurement data will be simulated by using the flow solver XFoil, with the assumption that perfect data with no noise can be collected from the hair sensor measurements. Such measurements will then be used in an artificial neural network based process to approximate the instantaneous airfoil camber shape, lift coefficient, and moment coefficient at a given angle of attack. Various aerodynamic and geometrical properties approximated from the artificial hair sensor and artificial neural network system will be compared with the results of XFoil in order to validate the approximation approach.

The study herein described is aimed at investigating the feasibility of an innovative full-scale camber morphing aileron device. In the framework of the “Adaptive Aileron” project, an international cooperation between Italy and Canada, this goal was carried out with the integration of different morphing concepts in a wing-tip prototype. As widely demonstrated in recent European projects such as Clean Sky JTI and SARISTU, wing trailing edge morphing may lead to significant drag reduction (up to 6%) in off-design flight points by adapting chord-wise camber variations in cruise to compensate A/C weight reduction following fuel consumption. Those researches focused on the flap region as the most immediate solution to implement structural adaptations. However, there is also a growing interest in extending morphing functionalities to the aileron region preserving its main functionality in controlling aircraft directional stability. In fact, the external region of the wing seems to be the most effective in producing “lift over drag” improvements by morphing. Thus, the objective of the presented research is to achieve a certain drag reduction in off-design flight points by adapting wing shape and lift distribution following static deflections. In perspective, the developed device could also be used as a load alleviation system to reduce gust effects, augmenting its frequency bandwidth. In this paper, the preliminary design of the adaptive aileron is first presented, assessed on the base of the external aerodynamic loads. The primary structure is made of 5 segmented ribs, distributed along 4 bays, each splitted into three consecutive parts, connected with spanwise stringers. The aileron shape modification is then implemented by means of an actuation system, based on a classical quick-return mechanism, opportunely suited for the presented application. Finite element analyses were assessed for properly sizing the load-bearing structure and actuation systems and for characterizing their dynamic behavior. Obtained results are reported and widely discussed.

A variety of strategies have been developed over the past few decades to determine controllable damping device forces to mitigate the response of structures and mechanical systems to natural hazards and other excitations. These “smart” damping devices produce forces through passive means but have properties that can be controlled in real time, based on sensor measurements of response across the structure, to dramatically reduce structural motion by exploiting more than the local “information” that is available to purely passive devices. A common strategy is to design optimal damping forces using active control approaches and then try to reproduce those forces with the smart damper. However, these design forces, for some structures and performance objectives, may achieve high performance by selectively adding energy, which cannot be replicated by a controllable damping device, causing the smart damper performance to fall far short of what an active system would provide. The authors have recently demonstrated that a model predictive control strategy using hybrid system models, which utilize both continuous and binary states (the latter to capture the switching behavior between dissipative and non-dissipative forces), can provide reductions in structural response on the order of 50% relative to the conventional clipped-optimal design strategy. This paper explores the robustness of this newly proposed control strategy through evaluating controllable damper performance when the structure model differs from the nominal one used to design the damping strategy. Results from the application to a two-degree-of-freedom structure model confirms the robustness of the proposed strategy.

Seismic records collected from earthquake with large magnitude and far distance may contain long period seismic waves which have small amplitude but with dominant period up to 10 sec. For a general situation, the long period seismic waves will not endanger the safety of the structural system or cause any uncomfortable for human activity. On the contrary, for those far distant earthquakes, this type of seismic waves may cause a glitch or, furthermore, breakdown to some important equipments/facilities (such as the high-precision facilities in high-tech Fab) and eventually damage the interests of company if the amplitude becomes significant. The previous study showed that the ground motion features such as time-variant dominant frequencies extracted using moving window singular spectrum analysis (MWSSA) and amplitude characteristics of long-period waves identified from slope change of ground motion Arias Intensity can efficiently indicate the damage severity to the high-precision facilities. However, embedding a large hankel matrix to extract long period seismic waves make the MWSSA become a time-consumed process. In this study, the seismic ground motion data collected from broadband seismometer network located in Taiwan were used (with epicenter distance over 1000 km). To monitor the significant long-period waves, the low frequency components of these seismic ground motion data are extracted using wavelet packet transform (WPT) to obtain wavelet coefficients and the wavelet entropy of coefficients are used to identify the amplitude characteristics of long-period waves. The proposed method is a timesaving process compared to MWSSA and can be easily implemented for real-time detection. Comparison and discussion on this method among these different seismic events and the damage severity to the high-precision facilities in high-tech Fab is made.

This paper develops an energy-aware ultrasonic sensor network architecture using a Pulse Switching approach for lightweight, through-substrate operation in Structural Health Monitoring applications. Pulse Switching protocols employ single pulses instead of multi-bit packets for information delivery with maximal lightness in event monitoring with binary sensing requirements i.e. where event information transmitted is only a single bit (YES / NO) based on evaluation of structural characteristics. The paper presents a simulation study of the Energy-Aware Through-Substrate Pulse Switching protocol performance for structural monitoring when operated using energy harvested from intermittent vibrations in the structure itself. The paper incorporates an energy harvesting model for simulating memory-less vibration patterns using exponentially distributed random processes at different networked nodes. These nodes are placed inside a rectangular plate structure and the corresponding harvested energy profiles are simulated. The vibration profiles are a function of the position of the node on the plate as well as time. Such spatio-temporal variation leads to interesting dynamics in the energy-aware protocol operation which have been explored in the current paper setting. Through the simulations, it is shown that the proposed Energy-Aware Pulse Switching protocol mechanisms can offer a robust through-substrate network that can be reliably used for Structural Health Monitoring using vibration-harvested energy.

In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.

This paper presents a comparative study through the piezoelectric coupled field analysis mode of finite element method (FEM) on detection of damages of varying magnitude, encompassing three different types of structural materials, using piezo impedance transducers. An aluminum block, a concrete block and a steel block of dimensions 48×48×10 mm were modelled in finite element software ANSYS. A PZT patch of 10×10×0.3 mm was also included in the model as surface bonded on the block. Coupled field analysis (CFA) was performed to obtain the admittance signatures of the piezo sensor in the frequency range of 0-250 kHz. The root mean square deviation (RMSD) index was employed to quantify the degree of variation of the signatures. It was found that concrete exhibited deviation in the signatures only with the change of damping values. However, the other two materials showed variation in the signatures even with changes in density and elasticity values in a small portion of the specimen. The comparative study shows that the PZT patches are more sensitive to damage detection in materials with low damping and the sensitivity typically decreases with increase in the damping.

This paper proposes a new nonlinear ultrasonic technique for fatigue crack detection using a single piezoelectric transducer (PZT). The proposed technique identifies a fatigue crack using linear (α) and nonlinear (β) parameters obtained from only a single PZT mounted on a target structure. Based on the different physical characteristics of α and β, a fatigue crack-induced feature is able to be effectively isolated from the inherent nonlinearity of a target structure and data acquisition system. The proposed technique requires much simpler test setup and less processing costs than the existing nonlinear ultrasonic techniques, but fast and powerful. To validate the proposed technique, a real fatigue crack is created in an aluminum plate, and then false positive and negative tests are carried out under varying temperature conditions. The experimental results reveal that the fatigue crack is successfully detected, and no positive false alarm is indicated.

A novel design of pressure sensor based on piezoelectric bending resonator is described in this paper. The resonator is isolated from and mechanically coupled to the surrounding fluid using a sealed enclosure. The pressure applied to the enclosure induces a compressive stress to the resonator and reduces its resonance frequency. In principle the mechanism allows for achieving large resonance frequency shifts close to 100% of the resonance frequency. A high-pressure sensor based on the mechanism was designed for down-hole pressure monitoring in oil wells. The sensor is potentially remotely-readable via the transmission of an electromagnetic signal down a waveguide formed by the pipes in the oil well. The details of the pressure sensor design and verification by FE analysis and initial test results of a preliminary prototype are presented in this paper.

A newly-developed soft elastomeric capacitor (SEC) strain sensor has shown promise in fatigue crack monitoring. The SECs exhibit high levels of ductility and hence do not break under excessive strain when the substrate cracks due to slippage or de-bonding between the sensor and epoxy. The actual strain experienced by a SEC depends on the amount of slippage, which is difficult to simulate numerically, making it challenging to accurately predict the response of a SEC near a crack. In this paper, a two-step approach is proposed to simulate the capacitance response of a SEC. First, a finite element (FE) model of a steel compact tension specimen was analyzed under cyclic loading while the cracking process was simulated based on an element removal technique. Second, a rectangular boundary was defined near the crack region. The SEC outside the boundary was assumed to have perfect bond with the specimen, while that inside the boundary was assumed to deform freely due to slippage. A second FE model was then established to simulate the response of the SEC within the boundary subject to displacements at the boundary from the first FE model. The total simulated capacitance was computed from the model results by combining the computed capacitance inside and outside the boundary. The performance of the simulation incorporating slippage was evaluated by comparing the model results with the experimental data from the test performed on a compact tension specimen. The FE model considering slippage showed results that matched the experimental findings more closely than the FE model that did not consider slippage.

The state of art in shock resistant MEMS accelerometer design is to reduce the size of the proof-mass, thereby reducing the generated forces and moments due to shock loading. Physical stops are also provided to limit proof-mass motion to prevent damage to various moving components. The reduction of the proof-mass size reduces the sensor sensitivity. In addition, to increase the sensor dynamic response, proof-mass motion needs to be minimally damped, resulting in a significant sensor settling time after experiencing a high shock loading such as those experienced by gun-fired munitions during firing. The settling time is particularly important for accelerometers that are used in gun-fired munitions and mortars for navigation and guidance. This paper describes the development of a novel class of accelerometers that are provided with the means of locking the sensor proof-mass in its “null” position when subjected to acceleration levels above a prescribed threshold, thereby protecting the moving parts of the accelerometer. In munitions applications, the proof-mass is thereby locked in its null position during the firing and is released during the flight to measure flight acceleration with minimal settling time. Details of the design and operation of the developed sensors and results of their prototyping and testing are presented. The application of the developed technology to other types of inertial sensors and devices is discussed.

This paper describes the application of the monolithic UNISA Folded Pendulum, optimized as inertial sensor (seismometer) for low frequency applications for characterization of sites (including underground sites) and structures (e.g. buildings, bridges, historical monuments), but, in general, for applications requiring large band low-frequency performances coupled with high sensitivities. The main characteristics of this class of sensors are high sensitivity, large measurement band, compactness, lightness, scalability, tunability of the resonance frequency, low thermal noise and very good immunity to environmental noises. The horizontal and vertical versions of folded pendulum allow an effective state-of-the-art mechanical implementation of triaxial sensors, configurable both as seismometer and/or as accelerometer.

In this paper, a piezoelectric sensor with a floating element was developed for shear stress measurement. The piezoelectric sensor was designed to detect the pure shear stress, suppressing effects of normal stress components, by applying opposite poling vectors to the piezoelectric elements. The sensor was first calibrated in the lab by applying shear forces where it demonstrated high sensitivity to shear stress (91.3 ± 2.1 pC/Pa) due to the high piezoelectric coefficients of 0.67Pb(Mg_1∕3Nb_2∕3)O₃-0.33PbTiO₃ (PMN-33%PT, d₃₁=-1330 pC/N). The sensor also exhibited negligible sensitivity to normal stress (less than 1.2 pC/Pa) because of the electromechanical symmetry of the device. The usable frequency range of the sensor is up to 800 Hz.

The historical reliance of thermoelastic stress analysis on cooled infrared detection has created significant cost and practical impediments to the widespread use of this powerful full-field stress measurement technique. The emergence of low-cost microbolometers as a practical alternative has allowed for an expansion of the traditional role of thermoelastic stress analysis, and raises the possibility that it may in future become a viable structural health monitoring modality. Experimental results are shown to confirm that high resolution stress imagery can be obtained from an uncooled thermal camera core significantly smaller than any infrared imaging device previously applied to TSA. The paper provides a summary of progress toward the development of an autonomous stress-imaging capability based on this core.

Macro fiber Composite (MFC) finds its application in active control, vibration control and sensing elements. MFC can be laminated to surfaces or embedded in the structures to be used as an actuator and sensors. Due to its attractive properties and applications, it may be subjected to continuous loading, which leads to the deterioration of the properties. This study is focused on the fatigue lifetime of MFC under tensile and compressive loading at room temperature. Experiments were performed using 4 point bending setup, with MFC pasted at the center of the mild steel beam, to maintain constant bending stress along MFC. MFC is pasted using vacuum bagging technique. Sinusoidal loading is given to sample while maintaining R=0.13 (for tensile testing) and R=10 (for compressive testing). For d31 and d33 type of MFC, test was conducted for the strain values of 727 μ strain, 1400 μ strain, 1700 μ strain and 1900 μ strain for fatigue under tensile loading. For fatigue under compressive loading, both d₃₃ and d₃₁, was subjected to minimum strain of -2000 μ strain. Decrease in the slope of dielectric displacement vs. strain is the measure for the degradation. 10 percent decrease in the slope is set as the failure criteria. Experimental results show that MFC is very reliable below 1700 μ strain (R=0.13) at the room temperature.

A suitable defect identification parameter is very important in the field of nondestructive testing (NDT). In this work, we proposed a NDT method which detects the sample’s local contact stiffness (LCS) based on the contact resonance of a piezoelectric cantilever. Firstly, through finite element analysis we showed that LCS is quite sensitive to typical defects including debonding, voids, cracks and inclusions, making it a good identification parameter. Secondly, a homemade NDT system containing a piezoelectric unimorph cantilever was assembled to detect the sample’s LCS by tracking the contact resonance frequency (CRF) of the cantilever-sample system based on strain signals. Testing results indicated that this NDT system could detect the above mentioned defects efficiently. The cantilever-stiffness dependent detection sensitivity was specially investigated and the stiffer cantilevers were found to be more sensitive to small defects. Then, a piezoelectric bimorph cantilever was fabricated and the electromechanical impedance, other than the strain signals, was measured to track the CRF of the cantilever-system. The LCS is then derived by using the equivalent-circuit model. The electromechanical impedance based NDT system is more compact and can be further developed to be a portable device. Finally, a Vicker indenter is fabricated onto the bimorph tip and the contact area is derived from the measured LCS. Thus the NDT system turns to be a hardness tester without any optical devices. It is very useful for in-situ testing or testing on inner surfaces where conventional hardness tester is not applicable.

Dynamic displacement is one of the most important measurands in wind tunnel tests of structures. Laser sensors or optical sensors are usually used in wind tunnel tests to measure displacements. However, these commercial sensors have limitations in its use, cost and installation despite of their good performance in accuracy. RINO (Real-time Image- processing for Non-contact monitoring), an iOS software application for dynamic displacement monitoring, has been developed in the previous study. In this study, feasibility of RINO in practical use for wind tunnel tests is explored. Series of wind tunnel tests show that performances of RINO are comparable with those of conventional displacement sensors.

Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and space-borne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.

This paper investigates energy harvesting from arterial blood pressure via the piezoelectric effect by using a novel streaked cylinder geometry for the purpose of powering embedded micro-sensors in the brain. Initially, we look at the energy harvested by a piezoelectric cylinder placed inside an artery acted upon by blood pressure. Such an arrangement would be tantamount to constructing a stent out of piezoelectric materials. A stent is a cylinder placed in veins and arteries to prevent obstruction in blood flow. The governing equations of a conductor coated piezoelectric cylinder are obtained using Hamilton’s principle. Pressure acting in arteries is radially directed and this is used to simplify the modal analysis and obtain the transfer function relating pressure to the induced voltage across the surface of the harvester. The power harvested by the cylindrical harvester is obtained for different shunt resistances. Radially directed pressure occurs elsewhere and we also look at harvesting energy from oil flow in pipelines. Although the energy harvested by the cylindrical energy harvester is significant at resonance, the natural frequency of the system is found to be very high. To decrease the natural frequency, we propose a novel streaked stent design by cutting it along the length, transforming it to a curved plate and decreasing the natural frequency. The governing equations corresponding to the new geometry are derived using Hamilton’s principle and modal analysis is used to obtain the transfer function.

Corrosion can develop due to adverse environmental conditions during the life cycle of a range of industrial structures, e.g., offshore oil platforms, ships, and desalination plants. Generalized corrosion leading to wall thickness loss can cause the reduction of the strength and thus degradation of the structural integrity. The monitoring of corrosion damage in difficult to access areas can be achieved using high frequency guided waves propagating along the structure from accessible areas. Using standard ultrasonic wedge transducers with single sided access to the structure, guided wave modes were selectively generated that penetrate through the complete thickness of the structure. The wave propagation and interference of the different guided wave modes depends on the thickness of the structure. Laboratory experiments were conducted for wall thickness reduction due to milling of the steel structure. From the measured signal changes due to the wave mode interference the reduced wall thickness was monitored. Good agreement with theoretical predictions was achieved. The high frequency guided waves have the potential for corrosion damage monitoring at critical and difficult to access locations from a stand-off distance.

This paper studies an air-coupled ultrasonic scanning approach for damage assessment in steel-clad concrete structures. An air-coupled ultrasonic sender generates guided plate waves in the steel cladding and a small contact-type receiver measures the corresponding wave responses. A frequency-wavenumber (f-k) domain signal filtering technique is used to isolate the behavior of the fundamental symmetric (S0) mode of the guided plate waves. The behavior of the S0 mode is sensitive to interface bonding conditions. The proposed inspection approach is verified by a series of experiments performed on laboratory-scale specimens. The experimental results demonstrate that hidden disbond between steel cladding and underlying concrete substrate can be successfully detected with the ultrasonic test setup and the f-k domain signal filtering technique.

Over the past decade, a large body of research associated with the addition of microvascular networks to structural composites has been generated. The engineering goal is most often the extension of structural utility to include extended functionalities such as self-healing or improved thermal management and resilience. More recently, efforts to design reconfigurable embedded electronics via the incorporation of non-toxic liquid metals have been initiated. A wide range of planar antenna configurations are possible, and the trade-offs between structural effects, other system costs, and increased flexibility in transmitting and receiving frequencies are being explored via the structurally embedded vascular antenna (SEVA) concept. This work describes for the first time the design of a bowtie-like tunable liquid metal-based antenna for integration into a structural composite for electromagnetic use. The design of both the solid/fluid feed structure and fluid transmission lines are described and analysis results regarding the RF performance of the antenna are provided. Fabrication methods for the SEVA are explained in detail and as-fabricated components are described. Challenges associated with both fabrication and system implementation and testing are elucidated. Results from preliminary RF testing indicate that in situ response tuning is feasible in these novel multifunctional composites.

Recently, the development of hydrophobic nanoporous technologies has drawn increased attention, especially for the applications of energy absorption and impact protection. Although significant amount of research has been conducted to synthesis and characterize materials to protect structures from impact damage, the tradition methods focused on converting kinetic energy to other forms, such as heat and cell buckling. Due to their high energy absorption efficiency, hydrophobic nanoporous particle liquids (NPLs) are one of the most attractive impact mitigation materials. During impact, such particles directly trap liquid molecules inside the non-wetting surface of nanopores in the particles. The captured impact energy is simply stored temporarily and isolated from the original energy transmission path. In this paper we will investigate the energy absorption efficiency of combinations of silica nanoporous particles and with multiple liquids. Inorganic particles, such as nanoporous silica, are characterized using scanning electron microscopy. Small molecule promoters, such as methanol and ethanol, are introduced to the prepared NPLs. Their effects on the energy absorption efficiency are studied in this paper. NPLs are prepared by dispersing the studied materials in deionized water. Energy absorption efficiency of these liquids are experimentally characterized using an Instron mechanical testing frame and in-house develop stainless steel hydraulic cylinder system.

The understanding and the experimental characterization of the evolution of impulsive loading is crucial in several fields in structural, mechanical and ocean engineering, naval architecture and aerospace. In this regards, we developed an experimental methodology to reconstruct the deformed shape of compliant bodies subjected to impulsive loadings, as those encountered in water entry events, starting from a finite number of local strain measurements performed through Fiber Bragg Gratings. The paper discusses the potential applications of the proposed methodology for: i) real-time damage detection and structural health monitoring, ii) fatigue assessment and iii) impulsive load estimation.

An adaptive flow separation control system is designed and implemented as an essential part of a novel high-lift device for future aircraft. The system consists of MEMS pressure sensors to determine the flow conditions and adaptive lips to regulate the mass flow and the velocity of a wall near stream over the internally blown Coanda flap. By the oscillating lip the mass flow in the blowing slot changes dynamically, consequently the momentum exchange of the boundary layer over a high lift flap required mass flow can be reduced. These new compact and highly integrated systems provide a real-time monitoring and manipulation of the flow conditions. In this context the integration of pressure sensors into flow sensing airfoils of composite material is investigated. Mechanical and electrical properties of the integrated sensors are investigated under mechanical loads during tensile tests. The sensors contain a reference pressure chamber isolated to the ambient by a deformable membrane with integrated piezoresistors connected as a Wheatstone bridge, which outputs voltage signals depending on the ambient pressure. The composite material in which the sensors are embedded consists of 22 individual layers of unidirectional glass fiber reinforced plastic (GFRP) prepreg. The results of the experiments are used for adapting the design of the sensors and the layout of the laminate to ensure an optimized flux of force in highly loaded structures primarily for future aeronautical applications. It can be shown that the pressure sensor withstands the embedding process into fiber composites with full functional capability and predictable behavior under stress.

A variety of applications require the mixing and/or heating of a slurry made from a powder/fluid mixture. One of these applications, Sub Critical Water Extraction (SCWE), is a process where water and an environmental powder sample (sieved soil, drill cuttings, etc.) are heated in a sealed chamber to temperatures greater than 200 degrees Celsius by allowing the pressure to increase, but without reaching the critical point of water. At these temperatures, the ability of water to extract organics from solid particulate increases drastically. This paper describes the modeling and experimentation on the use of an acoustic resonant chamber which is part of an amino acid detection instrument called Astrobionibbler [Noell et al. 2014, 2015]. In this instrument we use acoustics to excite a fluid- solid fines mixture in different frequency/amplitude regimes to accomplish a variety of sample processing tasks. Driving the acoustic resonant chamber at lower frequencies can create circulation patterns in the fluid and mixes the liquid and fines, while driving the chamber at higher frequencies one can agitate the fluid and powder and create a suspension. If one then drives the chamber at high amplitude at resonance heating of the slurry occurs. In the mixing and agitating cell the particle levitation force depends on the relative densities and compressibility’s of the particulate and fluid and on the kinetic and potential energy densities associated with the velocity and pressure fields [Glynne-Jones, Boltryk and Hill 2012] in the cell. When heating, the piezoelectric transducer and chamber is driven at high power in resonance where the solid/fines region is modelled as an acoustic transmission line with a large loss component. In this regime, heat is pumped into the solution/fines mixture and rapidly heats the sample. We have modeled the piezoelectric transducer/chamber/ sample using Mason’s equivalent circuit. In order to assess the validity of the model we have built and tested a variety of chambers. This paper describes the experimental results which are in general agreement with theory within the limitations of the modeling.

Aerodynamic buffet is unsteady airflow exerting forces onto a surface, which can lead to premature fatigue damage of aircraft vertical tail structures, especially for aircrafts with twin vertical tails at high angles of attack. In this work, Macro Fiber Composite (MFC), which can provide strain actuation, was used as the actuator for the buffet-induced vibration control, and the positioning of the MFC patches was led by the strain energy distribution on the vertical tail. Positive Position Feedback (PPF) control algorithm has been widely used for its robustness and simplicity in practice, and consequently it was developed to suppress the buffet responses of first bending and torsional mode of vertical tail. However, its performance is usually attenuated by the phase contributions from non-collocated sensor/actuator configuration and plants. The phase lag between the input and output signals of the control system was identified experimentally, and the phase compensation was considered in the PPF control algorithm. The simulation results of the amplitude frequency of the closed-loop system showed that the buffet response was alleviated notably around the concerned bandwidth. Then the wind tunnel experiment was conducted to verify the effectiveness of MFC actuators and compensated PPF, and the Root Mean Square (RMS) of the acceleration response was reduced 43.4%, 28.4% and 39.5%, respectively, under three different buffeting conditions.

A magneto-rheological bearing (MRB) is proposed to improve the vibration isolation performance of a floating slab track system. However, it’s difficult to carry out the test for the full-scale track vibration isolation system in the laboratory. In this paper, the research is based on scale analysis of the floating slab track system, from the point view of the dimensionless of the dynamic characteristics of physical quantity, to establish a small scale test bench system for the MRBs. A small scale MRB with squeeze mode using magneto-rheological grease is designed and its performance is tested. The major parameters of a small scale test bench are obtained according to the similarity theory. The force transmissibility ratio and the relative acceleration transmissibility ratio are selected as evaluation index of system similarity. Dynamics of these two similarity systems are calculated by MATLAB experiment. Simulation results show that the dynamics of the prototype and scale models have good similarity. Further, a test bench is built according to the small-scale model parameter analysis. The experiment shows that the bench testing results are consistency with that of theoretical model in evaluating the vibration force and acceleration. Therefore, the small-scale study of magneto-rheological track vibration isolation system based on similarity theory reveals the isolation performance of a real slab track prototype system.

This paper presents a locking device which achieves in locking the rotary feed structure of a space-borne microwave radiometer during the launch stage. This locking device employs two shape memory alloy (SMA) wires as the actuating elements, and uses a self-locking structure to achieve the locking function. To improve the performance and reliability, a redundant SMA wire and a step structure are employed. To validate the proposed SMA locking device, four prototypes were fabricated and tested. The result shows that the locking device possesses the advantages of remarkable locking performances, wide operation electric current and high survival temperature. Based on the test results, the locking device has a great potential for the application of space rotary structures.

In the present work, wireless sensor network and real-time controlling and monitoring system are integrated for efficient water quality monitoring for environmental and domestic applications. The proposed system has three main components (i) the sensor circuits, (ii) the wireless communication system, and (iii) the monitoring and controlling unit. LabView software has been used in the implementation of the monitoring and controlling system. On the other hand, ZigBee and myRIO wireless modules have been used to implement the wireless system. The water quality parameters are accurately measured by the present computer based monitoring system and the measurement results are instantaneously transmitted and published with minimum infrastructure costs and maximum flexibility in term of distance or location. The mobility and durability of the proposed system are further enhanced by fully powering via a photovoltaic system. The reliability and effectiveness of the system are evaluated under realistic operating conditions.

Application of wireless sensors and sensor networks for Structural Health Monitoring has been investigated for a long time. Key limitations for practical use are energy requirements, connectivity, and integration with existing systems. Current sensors and sensor networks mainly rely on wired connectivity for communication and external power source for energy. This paper presents a suite of wireless sensors that are low-cost, maintenance free, rugged, and have long service life. The majority of the sensors considered were designed by transforming existing, proven, and robust wired sensors into wireless units. In this study, the wireless sensors were tested in laboratory conditions for calibration and evaluation along with wired sensors. The experimental results were also compared to theoretical results. The tests mostly show satisfactory performance of the wireless units. This work is part of a broader Federal Highway Administration sponsored project intended to ultimately validate a wireless sensing system on a real, operating structure to account for all the uncertainties, environmental conditions and operational variability that are encountered in the field.

System identification has been employed in numerous structural health monitoring (SHM) applications. Traditional system identification methods usually rely on centralized processing of structural response data to extract information on structural parameters. However, in wireless SHM systems the centralized processing of structural response data introduces a significant communication bottleneck. Exploiting the merits of decentralization and on-board processing power of wireless SHM systems, many system identification methods have been successfully implemented in wireless sensor networks. While several system identification approaches for wireless SHM systems have been proposed, little attention has been paid to obtaining information on the physical parameters (e.g. stiffness, damping) of the monitored structure. This paper presents a hybrid system identification methodology suitable for wireless sensor networks based on the principles of component mode synthesis (dynamic substructuring). A numerical model of the monitored structure is embedded into the wireless sensor nodes in a distributed manner, i.e. the entire model is segmented into sub-models, each embedded into one sensor node corresponding to the substructure the sensor node is assigned to. The parameters of each sub-model are estimated by extracting local mode shapes and by applying the equations of the Craig-Bampton method on dynamic substructuring. The proposed methodology is validated in a laboratory test conducted on a four-story frame structure to demonstrate the ability of the methodology to yield accurate estimates of stiffness parameters. Finally, the test results are discussed and an outlook on future research directions is provided.

As an important part of new information technology, the Internet of Things(IoT) is based on intelligent perception, recognition technology, ubiquitous computing, ubiquitous network integration, and it is known as the third wave of the development of information industry in the world after the computer and the Internet. And Smart Phones are the general term for a class of mobile phones with a separate operating system and operational memory, in which the third-party service programs including software, games, navigation, et.al, can be installed. Smart Phones, with not only sensors but also actuators, are widely used in the IoT world. As the current hot issues in the engineering area, Structural health monitoring (SHM) is also facing new problems about design ideas in the IoT environment. The development of IoT, wireless sensor network and mobile communication technology, provides a good technical platform for SHM. Based on these facts, this paper introduces a kind of new idea for Structural Health Monitoring using Smart Phones Technique. The system is described in detail, and the external sensor board based on Bluetooth interface is designed, the test based on Smart Phones is finished to validate the implementation and feasibility. The research is preliminary and more tests need to be carried out before it can be of practical use.

Wireless sensor networks promise to deliver low cost, low power and massively distributed systems for structural health monitoring. A key component of these systems, particularly when sampling rates are high, is the capability to process data within the network. Although progress has been made towards this vision, it remains a difficult task to develop and program ’smart’ wireless sensing applications. In this paper we present a system which allows data acquisition and computational tasks to be specified in Python, a high level programming language, and executed within the sensor network. Key features of this system include the ability to execute custom application code without firmware updates, to run multiple users’ requests concurrently and to conserve power through adjustable sleep settings. Specific examples of sensor node tasks are given to demonstrate the features of this system in the context of structural health monitoring. The system comprises of individual firmware for nodes in the wireless sensor network, and a gateway server and web application through which users can remotely submit their requests.

This paper presents an imaging technique to locate damage in plate-like structures by permanently attached piezoelectric transducers (PZT) capable to generate and receive guided ultrasonic waves. The technique is based on a model capable of predicting envelope of scattered waves. Correlations between the estimated scattered waves and experimental data are used for image reconstruction. The approach is validated on an aluminum plate and results are compared with two common imaging algorithms, that is, Delay and Sum (DS) and Minimum Variance (MV). Damage is simulated by placing two magnets on sides of the plate. It is shown that the inclusion of Lamb wave reflections improves the localization accuracy while making use of fewer number of sensors possible.

We present a technique for microcrack modeling in the finite element framework, and numerically investigate the occurrence of nonlinear wave modulation. Typically, fatigue cracks are initiated and developed when structures are exposed to repeated loading; the crack widths of the fatigue cracks are extremely small in the early development stage. As the fatigue cracks grow by combining and coalescing, the overall size increases. Enlarged cracks undermine the safety of the structure. Therefore, fatigue crack detection is very important to ensure the integrity of structures. Although the nonlinear ultrasonic wave modulation technique has been widely used due to its high detecting sensitivity, the basic principle is not fully understood. To reveal the mechanism of nonlinear wave modulation, the movements of the crack surfaces are calculated through numerical simulation. The shape of the crack surface can determine the intensity of the wave modulation. In this study, we investigate the variation of the crack widths due to fatigue failure using microscopic imaging of real fatigue cracks, and use these images to create realistic models of the fatigue cracks.

In ultrasonic structural health monitoring (SHM), environmental and operational conditions, especially temperature, can significantly affect the propagation of ultrasonic waves and thus degrade damage detection. Typically, temperature effects are compensated using optimal baseline selection (OBS) or optimal signal stretch (OSS). The OSS method achieves compensation by adjusting phase shifts caused by temperature, but it does not fully compensate phase shifts and it does not compensate for accompanying signal amplitude changes. In this paper, we develop a new temperature compensation strategy to address both phase shifts and amplitude changes. In this strategy, OSS is first used to compensate some of the phase shifts and to quantify the temperature effects by stretching factors. Based on stretching factors, empirical adjusting factors for a damage indicator are then applied to compensate for the temperature induced remaining phase shifts and amplitude changes. The empirical adjusting factors can be trained from baseline data with temperature variations in the absence of incremental damage. We applied this temperature compensation approach to detect volume loss in a thick wall aluminum tube with multiple damage levels and temperature variations. Our specimen is a thick-walled short tube, with dimensions closely comparable to the outlet region of a frac iron elbow where flow-induced erosion produces the volume loss that governs the service life of that component, and our experimental sequence simulates the erosion process by removing material in small damage steps. Our results show that damage detection is greatly improved when this new temperature compensation strategy, termed modified-OSS, is implemented.

A novel computational framework for reconstructing spatial and temporal profiles of moving acoustic sources from wave responses measured at sparsely distributes sensors is introduced in this paper. This method can be applied to a broad range of acoustic-source inversion (ASI) problems for heterogeneous, complex-shaped coupled dynamic systems. The finite element method (FEM) is used to obtain wave response solutions due guessed moving sources. An adjoint-gradient based optimization technique iteratively improves the guesses so that the guessed moving sources converge on the actual moving sources. To reconstruct acoustic source profiles without a-priori knowledge of sources, we will employ high-resolution discretization of source functions in space and time. Because of such dense discretization, the order of magnitude of number of inversion parameters could range from millions to billions.

Numerical experiments prove the robustness of this method by reconstructing spatial and temporal profiles of multiple dynamic moving body forces in a one-dimensional heterogeneous solid bar. The sources create stress waves propagating through the bar. The guessed source functions are spatially discretized by using linear shape functions with an element size of 1m at discrete times with a time step of 0.001s. Thus, the total number of control parameters in this example is 100,000 (i.e., 100 (in space) by 1000 (in time)). The convergence toward the target in the numerical examples is excellent, reconstructing the spatial and temporal footprints of the sources.

“Clad steel” refers to a thick carbon steel structural plate bonded to a corrosion resistant alloy (CRA) plate, such as stainless steel or titanium, and is widely used in industry to construct pressure vessels. The CRA resists the chemically aggressive environment on the interior, but cannot prevent the development of corrosion losses and cracks that limit the continued safe operation of such vessels. At present there are no practical methods to detect such defects from the exposed outer surface of the thick carbon steel plate, often necessitating removing such vessels from service and inspecting them visually from the interior. In previous research, sponsored by industry to detect and localize damage in pressurized piping systems under operational and environmental changes, we investigated a number of data-driven signal processing methods to extract damage information from ultrasonic guided wave pitch-catch records. We now apply those methods to relatively large clad steel plate specimens. We study a sparse array of wafer-type ultrasonic transducers adhered to the carbon steel surface, attempting to localize mass scatterers grease-coupled to the stainless steel surface. We discuss conditions under which localization is achieved by relatively simple first-arrival methods, and other conditions for which data-driven methods are needed; we also discuss observations of plate-like mode properties implied by these results.

Composite materials are extensively used in a wide array of application markets by virtue of their strength, stiffness and lightness. Many composite structures are replaced today not only after failure but also before, for precautionary reasons. Adding optical sensing intelligence to these structures not only prolongs their lifetime but also significantly reduces the use of raw materials and energy. The use of optical based sensors offer numerous advantages i.e. integrability, high sensitivity, compactness and electromagnetic immunity. Most sensors integrated in composites are based on silica fibers with Bragg gratings. However, polymers are an interesting alternative because they present several advantages. They have high values in the opticalconstants involved in sensing, are cost-effective and allow larger elongations than silica. Moreover, planar optical waveguides represent an interesting approach to be further integrated e.g. in circuits. We present a comparison between Ormocer®-based and epoxy-based polymer waveguide Bragg grating sensors. Both polymers were screened for their compatibility with composite production processes and for their sensitivity to measure temperature and stress. Ormocer®-based sensors were found to exhibit a very high sensitivity (-250 pm/°C) for temperature sensing, while the epoxy-based sensors, although less sensitive (-90 pm/°C) were more compatible with the epoxy-based composite production process. In terms of sensitivity to measure stress, both materials were found to be analogous with measured values of (2.98 pm/μepsilon) for the epoxy-based and (3.00 pm/μepsilon) for Ormocer®-based sensors.

Networks of fiber Bragg grating (FBG) sensors can serve as structural health monitoring (SHM) systems for large-scale structures based on the collection of ultrasonic waves. The demodulation of structural Lamb waves requires a high signal-to-noise ratio because Lamb waves have a low amplitude. This paper investigates the signal transfer between Lamb waves propagating in an aluminum plate collected by an optical fiber containing a FBG. The fiber is bonded to the plate at locations away from the FBG. The Lamb waves are converted into longitudinal and flexural traveling waves propagating along the optical fiber, which are then transmitted to the Bragg grating. The signal wave amplitude is measured for different distances between the bond location and the Bragg grating. Bonding the optical fiber away from the FBG location and closer to the signal source produces a significant increase in signal amplitude, here measured to be 5.1 times that of bonding the Bragg grating itself. The arrival time of the different measured wave coupling paths are also calculated theoretically, verifying the source of the measured signals. The effect of the bond length to Lamb wavelength ratio is investigated, showing a peak response as the bond length is reduced compared to the wavelength. This study demonstrates that coupling Lamb waves into guided traveling waves in an optical fiber away from the FBG increases the signal-to-noise ratio of Lamb wave detection, as compared to direct transfer of the Lamb wave to the optical fiber at the location of the FBG.

This report discusses the guided Lamb wave sensing using polarization-maintaining (PM) fiber Bragg grating (PM-FBG) sensor. The goal is to apply the PM-FBG sensor system to composite structural health monitoring (SHM) applications in order to realize directivity and multi-axis strain sensing capabilities while reducing the number of sensors. Comprehensive experiments were conducted to evaluate the performance of the PM-FBG sensor attached to a composite panel structure under different actuation frequencies and locations. Three Macro-Fiber-Composite (MFC) piezoelectric actuators were used to generate guided Lamb waves that were oriented at 0, 45, and 90 degrees with respect to PMFBG axial direction, respectively. The actuation frequency was varied from 20 kHz to 200 kHz. It was shown that the PM-FBG sensor system was able to detect high-speed ultrasound waves and capture the characteristics under different actuation conditions. Both longitudinal and lateral strain components in the order of nano-strain were determined based on the reflective intensity measurement data from fast and slow axis of the PM fiber. It must be emphasized that this is the first attempt to investigate acouto-ultrasonic sensing using PM-FBG sensor. This could lead to a new sensing approach in the SHM applications.

Optical strain gauges, such as Fiber Bragg Gratings (FBG), have a great potential for smart structures, thanks to their small transversal size and the possibility to make an array of many sensors. They can be embedded in composite structures and their effect on the structure is nearly negligible. These advantages make them very interesting in the field of active vibration suppression. Unfortunately their low reliability is an obstacle to their use in such applications. For this reason, this paper introduces a fault identification algorithm to identify online those sensors which are not working correctly. The algorithm is based on the use of a sliding mode observer to estimate the coherence of measurements, and then to highlight possible faults. Once identified, the corresponding sensors can be excluded from the feedback loop of the control algorithm to avoid unwanted behaviors or instabilities. Numerical and experimental tests have been carried out on a carbon fiber structure considering different fault conditions. Results show it is possible to identify the faulty sensors and thus improve the signals used in the feedback loop.

Lamb-wave based structural health monitoring (SHM) approaches are typically constrained to operate below the first cut-off frequency to simplify the interpretation of the wave field in the time-domain. However from a diagnostic perspective, it is desirable to unlock the additional information encoded in the higher-order Lamb wave spectrum. Wave-mode decomposition is necessary for the extraction of useful information from multi-modal acoustic wave fields, which requires spatially dense sampling over the field. The instrument of choice for this task is the laser Doppler vibrometer, which is capable of producing detailed spectral decompositions. However vibrometry is not suited to in-situ measurement for SHM. Fibre Bragg gratings (FBGs) are capable of sensing Lamb waves and detection of higher order modes using FBGs has been previously demonstrated. The ability to multiplex multiple short-length gratings along a single fibre to create an FBG array gives rise to an in-situ sensor with sufficiently dense spatial sampling of an acoustic wave field to perform useful wave-mode decomposition. This paper explores some of the fundamental limits to modal decomposition resolution and bandwidth that exist for such sensors. Potential sources of noise and distortion encountered due to limitations of the sensor fabrication and interrogation methods are also discussed. In addition, modal decomposition of Lamb waves with frequencies up to 1.25 MHz is demonstrated in a laboratory experiment using an array of sixteen ~1 mm long gratings bonded to an aluminium plate. At least four modes are distinguishable in the resulting spectral decomposition.

Many building structures due to complex geometry and nonlinear material properties are difficult to be analyzed with FEM methods. A good example is a self-supporting metal plates structure. Considering uncommon geometry and material characteristic of a metal plate (due to plastic deformations, cross section of a trough, a goffer pattern), the local loss of stability can occur in unexpected regions. Therefore, the hybrid experimental-numerical methodology of analysis and optimization of metal plates structures has been developed. The methodology is based on three steps of development and validation of a numerical model with utilization of Digital Image Correlation measurements. In each step, the measurements are performed in different environments, with different accuracies and different scales. In this paper, the results of analysis performed with Digital Image Correlation, that enabled development and validation of FEM model are presented. The performed modification of a measurement setup is also described.

Dome structures have been built as roofs for venues where many people convene. Failure of this type of structure may jeopardize the safety of hundreds or even thousands people. For this type of structure, snap-through buckling may occur in a local area and gradually expand to the entire structure, leading to a failure of the overall structure. Although numerous structural health monitoring techniques and damage detection approaches have been developed, no research on the detection of a snap-through buckling has been reported. The objective of this study is to find a signature that is sensitive to snap-through buckling in dome structures and can be used to detect snap-through buckling. Considering that a snap-through buckling results in a significant deformation in a local area, which can be reflected by the change in tilting angles of members in that local area, the change in tilting angles of members will be proposed to be a signature to detect snap-through buckling. To verify the proposed instability signature, a reticulated dome structure will be investigated. Both an eigenvalue buckling analysis and a nonlinear buckling analysis will be conducted. The significant changes in tilting angles of members in the buckled regions have demonstrated the efficacy of the proposed instability signature. This research will bridge the research gap between structural health monitoring and structural stability research.

Progressive collapse is of rising importance within the structural engineering community due to several recent cases. The alternate path method is a design technique to determine the ability of a structure to sustain the loss of a critical element, or elements, and still resist progressive collapse. However, the alternate path method only considers the removal of the critical elements. In the event of a blast, significant damage may occur to nearby members not included in the alternate path design scenarios. To achieve an accurate assessment of the current condition of the structure after a blast or other extreme event, it may be necessary to reduce the strength or remove additional elements beyond the critical members designated in the alternate path design method. In this paper, a rapid model updating technique utilizing vibration measurements is used to update the structural model to represent the real-time condition of the structure after a blast occurs. Based upon the updated model, damaged elements will either have their strength reduced, or will be removed from the simulation. The alternate path analysis will then be performed, but only utilizing the updated structural model instead of numerous scenarios. After the analysis, the simulated response from the analysis will be compared to failure conditions to determine the buildings post-event condition. This method has the ability to incorporate damage to noncritical members into the analysis. This paper will utilize numerical simulations based upon a unified facilities criteria (UFC) example structure subjected to an equivalent blast to validate the methodology.

Pipeline Inspection Gauges (PIGs) are used for internal corrosion inspection of oil pipelines every 3-5 years. However, between inspection intervals, rapid corrosion may occur, potentially resulting in major accidents. The motivation behind this research project was to develop a safe distributed corrosion sensor placed inside oil pipelines continuously monitoring corrosion. The intrinsically safe nature of light provided motivation for researching fiber optic sensors as a solution. The sensing fiber's cladding features polymer plastic that is chemically sensitive to hydrocarbons within crude oil mixtures. A layer of metal, used in the oil pipeline's construction, is deposited on the polymer cladding, which upon corrosion, exposes the cladding to surrounding hydrocarbons. The hydrocarbon's interaction with the cladding locally increases the cladding's refractive index in the radial direction. Light intensity of a traveling pulse is reduced due to local reduction in the modal capacity which is interrogated by Optical Time Domain Reflectometery. Backscattered light is captured in real-time while using time delay to resolve location, allowing real-time spatial monitoring of environmental internal corrosion within pipelines spanning large distances. Step index theoretical solutions were used to calculate the power loss due changes in the intensity profile. The power loss is translated into an attenuation coefficient characterizing the expected OTDR trace which was verified against similar experimental results from the literature. A laboratory scale experiment is being developed to assess the validity of the model and the practicality of the solution.

Presented here is a new metamaterial panel based on multi-frequency vibration absorbers for broadband vibration absorption. The proposed metamaterial panel consists of a uniform isotropic panel and small two-mass spring-mass-damper subsystem many locations along the panel to act as multi-frequency vibration absorbers. The existence of two stopbands is demonstrated using a model based on averaging material properties over a cell length and a model based on finite element modeling and the Bloch-Floquet theory for periodic structures. For a finite metamaterial panel, because these two idealized models can not be used for finite panels and/or elastic waves having short wavelengths, a finite-element method is used for detailed modeling and analysis. The concepts of negative effective stiffness is explained in detail. For an incoming wave with a frequency in one of the two stopbands, the absorbers are excited to vibrate in their optical modes to create shear forces to straighten the panel and stop the wave propagation. For an incoming wave with a frequency outside of but between the two stopbands, it can be efficiently damped out by the damper with these mass of each absorber. Hence, the two stopbands are connected in to a wide stopband. Numerical examples validate the concept and show that the structures boundary conditions do not have significant influence on the absorption of high-frequency waves. However, for absorption of low-frequency waves, the structures boundary conditions and resonance frequencies and the location and spatial distribution of absorbers need to be considered in design, and it is better to use heavier masses for absorbers.

An instrumented composite wing is described. The wing is designed to meet the load and ruggedness requirements for a fixed-wing unmanned aerial vehicle (UAV) in search-and-rescue applications. The UAV supports educational systems development and has a 2.1-m wingspan. The wing structure consists of a foam core covered by a carbon-fiber, laminate composite shell. To quantify the wing characteristics, a fiber-optic strain sensor was surface mounted to measure distributed strain. This sensor is based on Rayleigh scattering from local index variations and it is capable of high spatial resolution. The use of the Rayleigh-scattering fiber-optic sensors for distributed measurements is discussed.

A gusset plate is a structural element that is commonly used to provide moment connections between steel members. Despite their importance, the performance of gusset plates in field structures can be poorly understood making them susceptible to failure. A well-known example is the catastrophic collapse of the I-35W Bridge in Minneapolis, MN on August 1, 2007 caused by a gusset plate failure. To prevent this type of failure, it is necessary to better predict and understand the stress and strain distribution in a plate element during field conditions. This work approaches the problem by using a numerical model combined with a linear recursive state estimation algorithm, known as the Kalman Filter, to update the model-based prediction with real time measurements taken on the structure. The finite element model was developed using the Mindlin plate theory which incorporates bending and shear deformations of the plate in the out-of-plane direction. The strain responses at arbitrary locations are estimated throughout the plate, including unmeasured locations, using limited sensor information and in the presence of noise and model errors. The results show how the different combinations of sensor data impact strain estimation accuracy under various loading conditions. The different combinations considered are: strain only, acceleration only, and acceleration and strain. The numerical studies demonstrate that the most accurate estimations are provided with the multi-metric combination of acceleration and strain. This opens future paths of development for force estimation, finding stress concentrations and buckling prediction in plate elements and potential expansion to shell elements.

During the last years, continuum robots have been used in many applications. They are smart structures with continuous curving, similar to a worm or an elephant trunk, characterized by a very high number of sub-actuated degrees of freedom (dof). They need a robust control system, aiming at both positioning the robot and suppressing induced vibrations. The idea is to adopt such a robot on a construction machine for the concrete distribution, substituting the reinforced rubber hose with the robotic smart solution. Particular attention has been paid to a control strategy able to reduce vibrations induced by the pumping procedure.

This paper describes a unique sensing method to apply an InSb Hall element that enables simultaneous sensing device to detect thickness of insulating film on an iron plate and temperature. We made a trial thickness-temperature sensor consists of an InSb Hall element and a small permanent magnet. Here, the film thickness is detected by the variation in distance between the Hall element with the magnet and the iron plate. The temperature characteristic of an InSb Hall element depends on the drive circuit to generate the Hall voltage. Therefore, the Hall element is driven using a constant voltage source and a constant current source by time-division to obtain two kinds of Hall output voltages. Two output Hall voltages driven by two kinds of bias circuits are measured in the film thickness range from 0 to 500 μm, and for a temperature range of -10 to 70 °C. The inverse response surfaces that are used to identify the thickness of insulating film and temperature are formulated using experimental results. The results obtained show that it is possible to detect film thickness and temperature by obtaining two kinds of Hall voltages.

This paper presents a novel two-layer spectral finite element model, consisting of PZT wafer and host structure, to simulate PZT-induced Lamb wave propagation in beam-like and plate-like structures. Based on the idea of equal displacement on the interface between PZT wafer and host structure, the one-dimensional spectral beam element of PZT-host beam and two-dimensional spectral plate element of PZT-host plate are considered as one hybrid element, respectively. A novel approach is proposed by taking the coupling effect of piezoelectric transducers in the thickness direction into account. The dynamic equation of the two-layer spectral element is derived from Hamilton’s principle. Validity of the developed spectral finite element is verified through numerical simulation. The result indicates that, compared with the conventional finite element method (FEM) based on elasticity, the proposed spectral finite element is proved to have a high accuracy in modeling Lamb wave propagation, meanwhile, significantly improve the calculation efficiency.

In recent years, smart phone develops very fast, and it has been the most popular tool in daily life of the public. Smart phones, with powerful operating systems, data storage and processing function, varieties of high-performance sensors and easily data transmission when connected to network, are the good choice for structures status monitoring in some occasion. One kind of hoisting monitoring method was proposed in this paper based on smartphone and Monitoring App developed. Firstly, one monitoring App was designed and developed, which can monitor the acceleration and inclination information using MEMS sensors embedded in smartphone. Secondly, typical operation status model of crane hoisting was studied. Then one validation test of hoisting was designed and conducted to monitor the acceleration and inclination of different elements during the operation procedure of one crane. The test results show the feasibility of the crane hoisting safety monitoring method using smartphone.

The long-term objective of our research is to develop sensor systems for detection of gear failure signs. As a very first step, this paper proposes a new method to create sensors directly printed on gears by a printer and conductive ink, and shows the printing system configuration and the procedure of sensor development. The developing printer system is a laser sintering system consisting of a laser and CNC machinery. The laser is able to synthesize micro conductive patterns, and introduced to the CNC machinery as a tool. In order to synthesize sensors on gears, we first design the micro-circuit pattern on a gear through the use of 3D-CAD, and create a program (G-code) for the CNC machinery by CAM. This paper shows initial experiments with the laser sintering process in order to obtain the optimal parameters for the laser setting. This new method proposed here may provide a new manufacturing process for mechanical parts, which have an additional functionality to detect failure, and possible improvements include creating more economical and sustainable systems.

In this paper we describe the characteristics and performances of mono-axial monolithic sensors aimed to low frequency motion measurement and control of spacecrafts and satellites. The mechanical part of these sensors is based on the UNISA Folded Pendulum, a mechanical architecture developed for ground-based applications. The unique features of the UNISA class of folded pendulum sensors (compactness, lightness, scalability, low resonance frequency and high quality factor) are based on the action of the gravitational force on the the moving part of the sensor. In this paper we show how to extend the application of ground-based folded pendulum also to space, in absence of gravity, still keeping all their peculiar features and characteristics. Tests on a prototype confirm the feasibility of this application, demonstrating also that interesting performances can be relatively easily obtained.

As the weakest part in the bridge system, traditional bridge bearing is incapable of isolating the impact load such as earthquake. A magneto-rheological elastomeric bearing (MRB) with adjustable stiffness and damping parameters is designed, tested and modeled. The developed Bouc-Wen model is adopted to represent the constitutive relation and force-displacement behavior of an MRB. Then, the lead rubber bearing (LRB), passive MRB and controllable MRB are modeled by finite element method (FEM). Furthermore, two typical seismic waves are adopted as inputs for the isolation system of bridge seismic response. The experiments are carried out to investigate the different response along the bridge with on-off controlled MRBs. The results show that the isolating performance of MRB is similar to that of traditional LRB, which ensures the fail-safe capability of bridge with MRBs under seismic excitation. In addition, the controllable bridge with MRBs demonstrated the advantage of isolating capacity and energy dissipation, because it restrains the acceleration peak of bridge beam by 33.3%, and the displacement of bearing decrease by 34.1%. The shear force of the pier top is also alleviated.

In order to identify multiple damage in the structure, a method of multiple damage identification and imaging based on the effective Lamb wave response automatic extraction algorithm is proposed. In this method, the detected key area in the structure is divided into a number of subregions, and then, the effective response signals including the structural damage information are automatically extracted from the entire Lamb wave responses which are received by the piezoelectric sensors. Further, the damage index values of every subregion based on the correlation coefficient are calculated using the effective response signals. Finally, the damage identification and imaging are performed using the reconstruction algorithm for probabilistic inspection of damage (RAPID) technique. The experimental research was conducted using an aluminum plate. The experimental results show that the method proposed in this research can quickly and effectively identify the single damage or multiple damage and image the damages clearly in detected area.

In the field of civil engineering, analyzing dynamic response was main concern for a long time. These analysis methods can be divided into moving load analysis method and moving mass analysis method, and formulating each an equation of motion has recently been studied after dividing vehicles and bridges. In this study, the numerical method is presented, which can consider the various train types and can solve the equations of motion for a vehicle-bridge interaction analysis by non-iteration procedure through formulating the coupled equations for motion. Also, 3 dimensional accurate numerical models was developed by KTX-vehicle in order to analyze dynamic response characteristics. The equations of motion for the conventional trains are derived, and the numerical models of the conventional trains are idealized by a set of linear springs and dashpots with 18 degrees of freedom. The bridge models are simplified by the 3 dimensional space frame element which is based on the Euler-Bernoulli theory. The rail irregularities of vertical and lateral directions are generated by PSD functions of the Federal Railroad Administration (FRA).

Damage to infrastructure is a real concern at present, caused primarily by worldwide climate anomalies, global warming, and natural disasters. Korea has begun research to develop a high precision patch/implant system using new IT techniques since 2011 and technologies which must be developed for this research are those which measure and evaluate the soundness and safety of structures based on the measurements of an attached sensor. During the research period since 2011, optical fiber sensor patches and wireless sensor capsule implants along with various sensor technologies, stress sensing and structural condition evaluation algorithm have been developed effectively for network hardware technologies as prototype version. Similarly high precision image processing for automatic crack extraction have been developed along with radiation sensor application technologies, combined management/control technologies for developed systems, and practical technologies for building and large scale structure. Through the results, it is expected that we acquire higher sensor system performance with a measurement scope (for precision, etc.) goal at least 200% better than conventional sensor systems.

The Earth Orientation Parameters (EOP), i.e. the spin axis of the Earth, is influenced by the mass redistribution inside and on the surface of the Earth. On the Earth surface, global ice melting, sea level change and atmospheric circulation are the prime contributors. Recent studies have unraveled the majority of the mysteries behind the Chandler wobble, the annual motion and the secular motion of the pole. The differences from the motion of a pole for a rigid Earth is indeed due to the mass redistribution and transfer of angular momentum among the atmosphere, the oceans and solid Earth. The technique of laser ranging and the use of laser ranged satellites such as LARES along with other techniques such Very Long Baseline Interferometry (VLBI) allow to measure the EOP with accuracies at the level of ~200 μas which correspond to few millimeters at the Earth’s surface, while the use of Global Navigation Satellite System (GNSS) data can reach an accuracy even below 100 μas. At these unprecedented high levels of accuracy, even tiny anomalous behavior in EOP can be observed and thus correlated to global environmental changes such as ice melting on Greenland and the polar caps, and extreme events that involve strong ocean-atmosphere coupling interactions such as the El Niño. The contribution of Satellite Laser Ranging (SLR) data such as from the LARES mission and similar satellites to this area is outlined in this paper.

A design tool is proposed for lighting control system that reflects histories of residents’ past life using a genetic mechanism. There are many previous researches which show the preference of lighting design differs depending on people and their behaviors. And recently, due to the appearance of LED which can change light color easily, the number of lighting scenes have drastically increased. It is difficult for residents to grasp all patterns of lighting and understand what pattern of lighting design fits for their behaviors. So if we can extract lighting preferences and demands of each resident from histories of past life and reflect these information in next lighting control, it’s possible to make living space more comfortable. An evolutionally adaptation mechanism learnt from living organisms is proposed in this research to extract the information from lifelog, especially focusing on methylation and mutation. Methylation is one of the epigenetic algorithms making a difference in phenotype without changing DNA sequence. Mutation is one of the genetic algorithms making a difference in phenotype by changing DNA sequence. Those two mechanisms are applied in the system. First, the lifelog of residents and using hysteresis of lighting equipment are collected. Then the lifelog is converted into the genetic information and stored. When the lifelog is stored enough, the superior genes will be picked up from the stored genetic information to be reflected in lighting control in next generation. Simulations to verify the versatility of the system were conducted.

Like any other technology, morphing has to demonstrate system level performance benefits prior to implementation onto a real aircraft. The current status of morphing structures research efforts (as the ones, sponsored by the European Union) involves the design of several subsystems which have to be individually tested in order to consolidate their general performance in view of the final integration into a flyable device. This requires a fundamental understanding of the interaction between aerodynamic, structure and control systems. Important worldwide research collaborations were born in order to exchange acquired experience and better investigate innovative technologies devoted to morphing structures. The “Adaptive Aileron” project represents a joint cooperation between Canadian and Italian research centers and leading industries. In this framework, an overview of the design, manufacturing and testing of a variable camber aileron for a regional aircraft is presented. The key enabling technology for the presented morphing aileron is the actuation structural system, integrating a suitable motor and a load-bearing architecture. The paper describes the lab test campaign of the developed device. The implementation of a distributed actuation system fulfills the actual tendency of the aeronautical research to move toward the use of electrical power to supply non-propulsive systems. The aileron design features are validated by targeted experimental tests, demonstrating both its adaptive capability and robustness under operative loads and its dynamic behavior for further aeroelastic analyses. The experimental results show a satisfactory correlation with the numerical expectations thus validating the followed design approach.

Civil infrastructures such as buildings, bridges, roads and pipelines are the integral part of people’s lives and their failure can have large public safety and economic consequences. Early detection of flaws in civil infrastructures and their appropriate retrofitting will aid in preventing this failure. Flaws such as cracks and impact damages initially occur on the surface and propagate inside the materials causing further degradation. There is a need to develop systems that can detect these surface flaws. Developing a system with one sensing technique which can detect the flaws is a challenging task since infrastructures are made up of diverse materials such as concrete, metal, plastics, composite and timber that have different electrical and mechanical properties. It is also desired that non-plain surfaces with complex profiles can be interrogated and surface flaws can be detected. We have proposed an imaging system capable of interrogating structures with complex surface profiles for the purpose of detection and evaluation of surface flaws such as cracks and impact damages using laser displacement sensor (LDS). The developed system consists of LDS mounted on the scanner which is able to perform raster scan over the specimen under test. The reading of displacement from the sensor head to the laser spot on the surface of the test material is then used to generate images which can be used to detect the surface flaws. The proof of concept is given by testing specimens made of metal, concrete and plastics with complex surface profiles.

In seasonally frozen soil regions, the frost heaving problem made it difficult to monitor or evaluate the pile safety for long term. So far, no mature tool can be utilized to monitor the frost heaving force, which was unevenly distributed along the pile. In this paper, a piezoceramic sensing based transient excitation response approach was proposed to monitor the frost heaving force in real time. Freeze-thaw cycles can result in great changes of soil engineering properties, including the frost heaving force. So, the freeze-thaw cycle was repeated fourth to study its effect. In the experiment, transient horizontal shock on the top of the pile will be detected by the 6 PZT sensors glued on the pile. The signal data received by the 6 PZT sensors can be used to illustrate the frost heaving force distribution along the pile. Moisture content effect is also one of the important reasons that cause the variation of soil mechanical properties. So three different moisture content (6%, 12%, 18%) testing soil were used in this experiment to detect the variance of the frost heaving force. An energy indicator was developed to quantitatively evaluate the frost heaving force applied on the pile. The experimental results showed that the proposed method was effective in monitoring the uneven distribution of frost heaving force along the pile.

This paper describes development of a smart breakwater or river bank using honeycomb-like structure to be adaptive to change of water level. A designed cell is deformed using a tensile test machine, and the results show that the honeycomb cell can deploy up to double of is original height without plastic deformation and the deformation is reproducible. It is stacked up to twelve layers and similar performance can be found. In addition, a six-layer and double-row deployable model is prepared and it became clear that the model can change its height in proportion to the water height in the experimental range and successfully block the water.

The target of the research activity presented in this paper is to design, to realize and to test an autonomous sensor node able to measure the accelerations in correspondence of the axle box of a freight train. The final goal of the sensor is to identify the derailment conditions by observing the variations in the spectra of the box accelerations, around the frequencies associated to the wheel revolution and its multiples.

The sensor node embeds an accelerometer, a microprocessor, a transmission system, a piezoelectric bimorph energy harvester and an integrated circuit for managing the power distribution to each component of the node.

In particular, a mechanical filter to be applied to the node was specifically designed to increment the energy recovered by the harvester and to filter out the high frequency components of the axle-box acceleration, allowing the use of a more sensitive accelerometer. The harvesting system was setup by means of laboratory tests carried out with an electromechanical shaker and the sensor node was finally tested through field tests on freight trains.

Recent years have witnessed a number of collapses of civil space structures, which have severely jeopardized the safety of the general public. Therefore, it is imperative to propose an approach that can localize damage to exact members at an early stage for space structures. Then, the obtained damage location results can be used by the maintenance/repair crew for taking timely actions. For most space structures, the member configuration possesses a regular pattern. For this type of structure, before damage occurs, the regular pattern in the member configuration is maintained. After damage occurs, the regular pattern around the damaged member will be destroyed. In this study, the proposed approach to locate damage is based on the change in the status of regularity of member configuration. Herein the difference in an angle of the triangular shape in the related region of a space structure is used to indicate whether the regularity of member configuration is maintained. To validate the proposed approach, a reticulated shell space structure will be investigated.

One of the most severe damage modes in modern wind turbines is the failure of the adhesive joints in the trailing edge of the large composite blades. The geometrical shape of the blade and current manufacturing techniques make the trailing edge of the wind turbine blade more sensitive to damage. Failure to timely detect this damage type may result in catastrophic failures, expensive system downtime, and high repair costs. A novel sensing system called the In-situ Triboluminescent Optical Fiber (ITOF) sensor has been proposed for monitoring the initiation and propagation of disbonds in composite adhesive joints. The ITOF sensor combines the triboluminescent property of ZnS:Mn with the many desirable features of optical fiber to provide in-situ and distributed damage sensing in large composite structures like the wind blades. Unlike other sensor systems, the ITOF sensor does not require a power source at the sensing location or for transmitting damage-induced signals to the hub of the wind turbine. Composite parts will be fabricated and the ITOF integrated within the bondline to provide in-situ and real time damage sensing. Samples of the fabricated composite parts with integrated ITOF will be subjected to tensile and flexural loads, and the response from the integrated sensors will be monitored and analyzed to characterize the performance of the ITOF sensor as a debonding damage monitoring system. In addition, C-scan and optical microscopy will be employed to gain greater insights into the damage propagation behavior and the signals received from the ITOF sensors.

To monitor 3D deformations of structural vibration response, a stereovision-based 3D deformation measurement method is proposed in paper. The world coordinate system is established on structural surface, and 3D displacement equations of structural vibration response are acquired through coordinate transformation. The algorithms of edge detection, center fitting and matching constraint are developed for circular target. A shaking table test of a masonry building model under Taft and El Centro earthquake at different acceleration peak is performed in lab, 3D displacement time histories of the model are acquired by the integrated stereovision measurement system. In-plane displacement curves obtained by two methods show good agreement, this suggests that the proposed method is reliable for monitoring structural vibration response. Out-of-plane displacement curves indicate that the proposed method is feasible and useful for monitoring 3D deformations of vibration response.

This study focuses on the system identification and the damage detection of reinforced concrete bridges using neural network algorithm, eigenvalue analysis and parallel computing. First, autoregressive coefficients (ARCs) of both temporal output and forced input of the real structure are computed. The ARCs are used for the eigen-system realization algorithm (ERA) to obtain the modal parameters of the structure. Second, the ARCs are utilized as the input variable of the neural network algorithm while the outputs are the submatrix scaling factors that contain information about the degeneration of each element and each mode within the element. However, the neural network algorithm requires training to output reliable results. The training is the most challenging task of this study and finite element analysis is used to compute the modal parameters of the model built around the neural network outputs. The model is compared with the ERA results to update the neural network coefficients. Due to the scale of the neural network used parallel computing is necessary to reduce the computational time to a reasonable amount.

In this paper, a sensor to monitor stress-strain signals in a granular medium is used to detect seismic precursory information. Compared with the widely used sensors of borehole stress in the rock, the sensor has more convenient operation, higher output sensitivity, compactness and farther propagation effect. The stress and strain changes before Pu’er Ms6.4 earthquake in China are recorded by Beijing and Xinmin stations, and its corresponding fault activities are analyzed. Study indicates anomalous amplitude of strain signal reaches 10 times higher than that of ordinary background, and compressive oscillation and extensional oscillation occurred constantly before the earthquake. The method and results presented in the paper provide a new way for investigating seismic precursors for shallow-source earthquakes.