This PDF file contains the front matter associated with SPIE Proceedings Volume 9437, including the Title Page, Copyright information, Table of Contents, Authors, and Conference Committee listing.

Ceramics bonded to metal joints may develop flaws due to residual stresses that develop during the cooling process. Scanning acoustic microscopy is a well-recognized tool for charactering elastic properties and can be applied to materials with elastic discontinuities such as debonding at the ceramic/metal interface. Acoustic information is obtained using the V (z) curve method, which measures the output voltage signal of a transducer as a function of the distance between the transducer and a specimen. The velocity of the surface acoustic waves, V_saw, can be calculated from the V (z) curve. In this work, a simulation of the V (z) curve was updated. The pupil-function splitting method was combined with the angular-spectrum approach of V (z) theory in order to obtain the V (z) curve for interfaces between different materials. The V_saw values at the interface were calculated from the simulated V (z) curve. A series of experiments were performed to measure the V_saw values at the interface of a Si₃N₄/Cu joint using the scanning acoustic microscope. By comparing the measured values with the calculated values, the reliability of this simulation was verified. The simulation can be used to test the boundary conditions of bimaterial samples.

FRP is a new material with good mechanical properties, such as high strength of extension, low density, good corrosion resistance and anti-fatigue. FRP and steel composite has gotten a wide range of applications in civil engineering because of its good performance. As the FRP/steel composite get more and more widely used, the monitor of its damage is also getting more important. To monitor this composite, acoustic emission (AE) is a good choice. In this study, we prepare four identical specimens to conduct our test. During the testing process, the AE character parameters and mechanics properties were obtained. Damaged properties of FRP/steel composite were analyzed through acoustic emission (AE) signals. By the growing trend of AE accumulated energy, the severity of the damage made on FRP/steel composite was estimated. The AE sentry function has been successfully used to study damage progression and fracture emerge release rate of composite laminates. This technique combines the cumulative AE energy with strain energy of the material rather than analyzes the AE information and mechanical separately.

Nondestructive evaluation (NDE) is essential for the detection of defects in the externally bonded fiber reinforced polymer (FRP) concrete, especially such bonded system can be readily found in strengthened and retrofitted structures nowadays. Among all the current NDE methods, acoustic-laser technique is a non-contact methodology with a high applicability to detect near-surface defect in composite structures, which is very suitable to be used for detecting defect in FRP retrofitted and strengthened concrete structures. The methodology is based on the acoustic excitation on the target surface and the measurement of its vibration using laser beam. To our best knowledge, no comprehensive study has been conducted to examine how the acoustic location and other related parameters affect the measurement sensitivity. In fact, several operational parameters affecting the performance of the test system are discussed here including (i) distance between the acoustic source and the object, (ii) sound pressure level (SPL), (iii) angle of the laser beam incidence and (iv) angle of the acoustic incidence. Here, we perform a series of parametric studies against these four operational parameters. Based on our experimental measurements, all parameters show significant effects on the measurement sensitivity of the acoustic-laser technique. Recommendations on an optimal range of each concerned parameter are provided.

Nonlinear ultrasonic non-destructive evaluation (NDE) methods can be used for the identification of defects within adhesive bonds as they rely on the detection of nonlinear elastic features for the evaluation of the bond strength. In this paper the nonlinear content of the structural response of a single lap joint subjected to ultrasonic harmonic excitation is both numerically and experimentally evaluated to identify and characterize the defects within the bonded region. Different metallic samples with the same geometry were experimentally tested in order to characterize the debonding between two plates by using two surface bonded piezoelectric transducers in pitch-catch mode. The dynamic response of the damaged samples acquired by the single receiver sensor showed the presence of higher harmonics (2^nd and 3^rd) and subharmonics of the fundamental frequencies. These nonlinear elastic phenomena are clearly due to nonlinear effects induced by the poor adhesion between the two plates. A new constitutive model aimed at representing the nonlinear material response generated by the interaction of the ultrasonic waves with the adhesive joint is also presented. Such a model is implemented in an explicit FE software and uses a nonlinear user defined traction-displacement relationship implemented by means of a cohesive material user model interface. The developed model is verified for the different geometrical and material configurations. Good agreement between the experimental and numerical nonlinear response showed that this model can be used as a simple and useful tool for understanding the quality of the adhesive joint.

SMArt Thermography exploits the electrothermal properties of multifunctional smart structures, which are created by embedding shape memory alloy (SMA) wires in traditional carbon fibre reinforced composite laminates (known as SMArt composites), in order to detect the structural flaws using an embedded source. Such a system enables a built-in, fast, cost-effective and in-depth assessment of the structural damage as it overcomes the limitations of standard thermography techniques. However, a theoretical background of the thermal wave propagation behaviour, especially in the presence of internal structural defects, is needed to better interpret the observations/data acquired during the experiments and to optimise those critical parameters such as the mechanical and thermal properties of the composite laminate, the depth of the SMA wires and the intensity of the excitation energy. This information is essential to enhance the sensitivity of the system, thus to evaluate the integrity of the medium with different types of damage. For this purpose, this paper aims at developing an analytical model for SMArt composites, which is able to predict the temperature contrast on the surface of the laminate in the presence of in-plane internal damage (delamination-like) using pulsed thermography. Such a model, based on the Green’s function formalism for one-dimensional heat equation, takes into account the thermal lateral diffusion around the defect and it can be used to compute the defect depth within the laminate. The results showed good agreement between the analytical model and the measured thermal waves using an infrared (IR) camera. Particularly, the contrast temperature curves were found to change significantly depending on the defect opening.

Portland Cement Concrete (PCC) loses the evaporable water at about 100 °C, decomposes C-S-H at about 200 °C, and dehydrates CH at about 500 °C, and deconstruct C-S-H at about 900°C. The concrete degradation or cracks are caused by several possible parameters, such as vapor pressure in pores, thermal gradient, and varied expansion rates of cement pastes and aggregates. The objective of the exploration study was to assess the porosity before and after conditioning of high temperature in the laboratory with the medical X-ray computed tomography. The experimental program was determined to identify the mineral properties of the aggregates used and determine the consensus properties of compressive, splitting tensile, and flexural strengths. Concrete cylinders were subject with one temperature conditioning, namely 400°C, but two different heat conditioning time namely four and eight hours. The X-ray CT, before and after high temperature conditioning, was administrated on the concrete cylinders to inspect the depth of the damage zone, which shall consist of more porosity than undamaged one. The damage zone will be examined and identified through the changes in porosity of concrete paste and aggregates within a concrete cylinder. The significance of the exploration study was to provide an in-depth insight to define the damaged zone for a better understanding of the following repairing and reinforced work.

Composite aircraft structures such as fuselage and wings are subject to impact from many sources. Ground service equipment (GSE) vehicles are regarded as realistic sources of blunt impact damage, where the protective soft rubber is used. With the use of composite materials, blunt impact damage is of special interest, since potential significant structural damage may be barely visible or invisible on the structure’s outer surface. Such impact can result in local or non-local damage, in terms of internal delamination in skin, interfacial delamination between stiffeners and skin, and fracture of internal reinforced component such as stringers and frames. The consequences of these events result in aircraft damage, delays, and financial cost to the industry. Therefore, it is necessary to understand the criticality of damage under this impact and provide reliable recommendations for safety and inspection technologies. This investigation concerns a composite-metallic 4-hat-stiffened and 5-frame panel, designed to represent a fuselage structure panel generic to the new generation of composite aircraft. The test fixtures were developed based on the correlation between finite element analyses of the panel model and the barrel model. Three static tests at certain amount of impact energy were performed, in order to improve the understanding of the influence of the variation in shear ties, and the added rotational stiffness. The results of this research demonstrated low velocity high mass impacts on composite aircraft fuselages beyond 82.1 kN of impact load, which may cause extensive internal structural damage without clear visual detectability on the external skin surface.

The layer wise construction of laminated composites offers the potential to embed sensors within composite structures. One possible solution is the embedding of sensors that are inductively coupled to an external probe; which allows for the efficient contactless transfer of electrical signals to the sensor. Embedding sensors within structures is an attractive option, due to the physical protection offered to the sensor by the host structure. However, for embedding sensors to be viable, sensor integration must result in minimal degradation of the laminates mechanical performance. This work focuses on designing embedded inductively coupled sensors for structural performance. A suitable sensor coating for the sensor unit was identified using interlaminar shear strength testing. Sensors were then embedded into quasi-isotropic four-point bend flexural strength specimens, and different embedding strategies demonstrated. In addition to providing the sensor with physical protection, embedding sensors within a composite host offers the additional benefit of monitoring the curing process of the surrounding composite. A single inductively coupled sensor was embedded into a large glass fiber epoxy plate, and the measured guided wave pulse echo response used to monitor the curing process. This novel cure monitoring technique was then benchmarked against direct scanning calorimetry.

Impact damage has been identified as a critical form of defect that constantly threatens the reliability of composite structures, such as those used in aircrafts and naval vessels. Low energy impacts can introduce barely visible damage and cause structural degradation. Therefore, efficient damage detection and risk assessment methods, which can accurately detect, quantify, and localize impact damage in complex composite structures, are required. In this paper a novel damage detection methodology is demonstrated for monitoring and quantifying the impact damage propagation. Statistical outlier analysis, composed of features extracted from the time and frequency domains, are developed. Autoregression with exogenous is used to classify the statistical feature and estimate the structural risk. The developed methodology has been validated using low velocity impact experiments with a sandwich composite wing.

Thin concrete panels reinforced with alloy polymer macro-synthetic fibers have recently been introduced to rapidly and cost-effectively improve the driving condition of existing roadways by laying down a fabric sheet on the roadways, casting a thin layer of concrete, and then cutting the layer into panels. This study is aimed to understand the strain distribution and potential crack development of concrete panels under three-point loading. To this end, six full-size 6ft×6ft×3in concrete panels were tested to failure in the laboratory. They were instrumented with three types of single-mode optical fiber sensors whose performance and ability to measure the strain distribution and detect cracks were compared. Each optical fiber sensor was spliced and calibrated, and then attached to a fabric sheet using adhesive. A thin layer of mortar (0.25 ~ 0.5 in thick) was cast on the fabric sheet. The three types of distributed sensors were bare SM-28e+ fiber, SM-28e+ fiber with a tight buffer, and concrete crack cable, respectively. The concrete crack cable consisted of one SM-28e+ optical fiber with a tight buffer, one SM-28e+ optical fiber with a loose buffer for temperature compensation, and an outside protective tight sheath. Distributed strains were collected from the three optical fiber sensors with pre-pulse-pump Brillouin optical time domain analysis in room temperature. Among the three sensors, the bare fiber was observed to be most fragile during construction and operation, but most sensitive to strain change or micro-cracks. The concrete crack cable was most rugged, but not as sensitive to micro-cracks and robust in micro-crack measurement as the bare fiber. The ruggedness and sensitivity of the fiber with a tight buffer were in between the bare fiber and the concrete crack cable. The strain distribution resulted from the three optical sensors are in good agreement, and can be applied to successfully locate cracks in the concrete panels. It was observed that the three types of fibers were functional until the concrete panels have experienced inelastic deformation, making the distributed strain sensing technology promising for real applications in pavement engineering.

Inspection of bridge decks is of primary importance in the field of bridges maintenance since, unless other structural components, they are more subjected to degradation and traffic-induced deterioration phenomena. Among the various deterioration mechanisms, delaminations are generally difficult to detect because no visible effects are usually observed on the deck surface. Since the entity of the damage progressively increase during time, methodologies able to effectively detect delaminations are needed in order to design appropriate solutions and reduce maintenance costs. In this work, the results obtained using two different nondestructive techniques, namely the impact echo (IE) method and the infrared thermography (IR), are compared. Experimental tests have been performed on a 20cm thick concrete slab containing delaminations of various extensions and on a small 60cm×60cm×20cm concrete specimen. Impact echo tests have been performed, with ultrasonic waveforms collected on an orthogonal grid of points spaced 30cm apart. Spacing was reduced to 5 cm for IE data collection in the small block. Leveraging different features extracted from IE, delaminations have been located. The results obtained using the impact echo test have been compared with those extracted using the infrared thermography. The main concept behind the use of the IR is that embedded horizontal interfaces behave as heat traps, resulting in different temperature areas on the slab surface. A discussion on the pro and cons of the two methodologies is provided and the paper ends with a preliminary attempt to perform data fusion, combining the results from the 2 different nondestructive techniques.

There is a growing need to characterize unknown foundations and assess substructures in existing bridges. It is becoming an important issue for the serviceability and safety of bridges as well as for the possibility of partial reuse of existing infrastructures.

Within this broader contest, this paper investigates the possibility of identifying, locating and quantifying changes of boundary conditions, by leveraging a simply supported grid structure with a composite deck. Multi-reference impact tests are operated for the grid model and modification of one supporting bearing is done by replacing a steel cylindrical roller with a roller of compliant material. Impact based modal analysis provide global modal parameters such as damped natural frequencies, mode shapes and flexibility matrix that are used as indicators of boundary condition changes. An updating process combining a hybrid optimization algorithm and the finite element software suit ABAQUS is presented in this paper. The updated ABAQUS model of the grid that simulates the supporting bearing with springs is used to detect and quantify the change of the boundary conditions.

Today, many large-scale civil engineering structures are permanently monitored to provide early warnings and to initiate counter actions from structural failure. Total station measurements are commonly used to determine 3D movements of selected points with measurement intervals of several minutes or hours. However, these measurements do not provide information on the vibration behavior of the structures. For this purpose, other sensors like accelerometers have to be installed on the object. In this paper, we present a monitoring system based on a state of the art image assisted total station (IATS) suitable for the measurement of absolute 3D coordinates and the determination of the structure’s natural frequencies. The 3D coordinates can be determined with an accuracy of a few millimeters using conventional total station measurements. For analyzing the structure’s natural frequencies, the telescope camera of the IATS is used in combination with dedicated image processing techniques optimized for artificial and natural targets. While the determination of 3D coordinates based on total station measurements is common practice, the idea of using the total station’s image data for frequency analysis is new. Consequently, investigations on the achievable performance are pending for commercially available products. We therefore evaluate the potential of this technique at a life-size footbridge by comparison with accelerometer measurements. We demonstrate that with our developed monitoring concept and state of the art hardware, accelerometer measurements can be replaced in several monitoring situations by IATS measurements and image processing techniques.

In this study, a noncontact main cable NDE method has been developed. This cable NDE method utilizes the direct current (DC) magnetization and a searching coil-based total flux measurement. A total flux sensor head prototype was fabricated that consists of an electro-magnet yoke and a searching coil sensor. To obtain a B-H loop, a magnetic field was generated by applying a cycle of low frequency direct current to the electro-magnet yoke. During the magnetization, a search coil sensor measures the electromotive force from magnetized cable. During the magnetization process, a search coil sensor was measured the magnetic flux density. Total flux was calculated by integrating the measured magnetic flux using a fluxmeter. A B-H loop is obtained by using relationship between a cycle of input DC voltage and measured total flux. The B-H loop can reflect the property of the ferromagnetic materials. Therefore, the cross-sectional loss of cable can be detected using variation of features from the B-H curve. To verify the feasibility of the proposed steel cable NDE method, a series of experimental studies using a main-cable mock-up specimen has been performed in this study.

Advances in wireless sensor technology have enabled low cost and extremely scalable sensing platforms prompting high density sensor installations. High density long-term monitoring generates a wealth of sensor data demanding an efficient means of data storage and data processing for information extraction that is pertinent to the decision making of bridge owners. This paper reports on decision making inferences drawn from automated data processing of long-term highway bridge data. The Telegraph Road Bridge (TRB) demonstration testbed for sensor technology innovation and data processing tool development has been instrumented with a long-term wireless structural monitoring system that has been in operation since September 2011. The monitoring system has been designed to specifically address stated concerns by the Michigan Department of Transportation regarding pin and hanger steel girder bridges. The sensing strategy consists of strain, acceleration and temperature sensors deployed in a manner to track specific damage modalities common to multigirder steel concrete composite bridges using link plate assemblies. To efficiently store and process long-term sensor data, the TRB monitoring system operates around the SenStore database system. SenStore combines sensor data with bridge information (e.g., material properties, geometry, boundary conditions) and exposes an application programming interface to enable automated data extraction by processing tools. Large long-term data sets are modeled for environmental and operational influence by regression methods. Response processes are defined by statistical parameters extracted from long-term data and used to automate decision support in an outlier detection, or statistical process control, framework.

We present results of an ongoing study of structural health monitoring of concrete elements by means of arrays of telecommunications-grade optical fibers embedded in such elements. In this work, we show a possibility of using this technique for monitoring the transverse displacement of the reinforced concrete beams under flexural loading. We embedded a number of multimode silica-core/polymer-clad/polymer-coated optical fibers in a mold with preinstalled reinforcing steel bars and fresh concrete mix. Then the concrete was compacted and cured. Some optical fibers were broken during the fabrication process. The fiber survival rate varied with concrete grade, compacting technique and optical fiber type. The fibers that survived the fabrication process were employed for the monitoring. They were connected to the optical transmitter and receiver that formed a part of a larger measurement system. The system continuously measured the optical transmission of all optical fibers while the reinforced concrete beams were subjected to incremental transverse loading. We observed a quasi-linear decrease in optical transmission in all optical fibers of the array vs. the applied load and respective flexural displacement. Although the underlying phenomena that lead to such a variation in optical transmission are not clear yet, the observed behavior might be of interest for assessing the transverse displacement of the reinforced concrete beams under flexural loading.

More economical management of bridges can be achieved through early problem detection and mitigation. The paper describes development and implementation of two fully automated (robotic) systems for nondestructive evaluation (NDE) and minimally invasive rehabilitation of concrete bridge decks. The NDE system named RABIT was developed with the support from Federal Highway Administration (FHWA). It implements multiple NDE technologies, namely: electrical resistivity (ER), impact echo (IE), ground-penetrating radar (GPR), and ultrasonic surface waves (USW). In addition, the system utilizes advanced vision to substitute traditional visual inspection. The RABIT system collects data at significantly higher speeds than it is done using traditional NDE equipment. The associated platform for the enhanced interpretation of condition assessment in concrete bridge decks utilizes data integration, fusion, and deterioration and defect visualization. The interpretation and visualization platform specifically addresses data integration and fusion from the four NDE technologies. The data visualization platform facilitates an intuitive presentation of the main deterioration due to: corrosion, delamination, and concrete degradation, by integrating NDE survey results and high resolution deck surface imaging. The rehabilitation robotic system was developed with the support from National Institute of Standards and Technology-Technology Innovation Program (NIST-TIP). The system utilizes advanced robotics and novel materials to repair problems in concrete decks, primarily early stage delamination and internal cracking, using a minimally invasive approach. Since both systems use global positioning systems for navigation, some of the current efforts concentrate on their coordination for the most effective joint evaluation and rehabilitation.

In the present study an attempt has been made to identify various bearing faults using machine learning algorithm. Vibration signals obtained from faults in inner race, outer race, rolling element and combined faults are considered. Raw vibration signal cannot be used directly since vibration signals are masked by noise. To overcome this difficulty combined time frequency domain method such as wavelet transform is used. Further wavelet selection criteria based on minimum permutation entropy is employed to select most appropriate base wavelet. Statistical features from selected wavelet coefficients are calculated to form feature vector. To reduce size of feature vector information gain attribute selection method is employed. Modified feature set is fed in to machine learning algorithm such as random forest and self-organizing map for getting maximize fault identification efficiency. Results obtained revealed that attribute selection method shows improvement in fault identification accuracy of bearing components.

To obtain high piezoelectric properties, PZT/cement composites with kaolin were fabricated and polarized by 1.5kV/mm electric field for 40 min, where lead zirconate titanate (PZT) inclusion with 50% by volume was used. After the polarization, piezoelectric properties of the composite were measured daily till 100 days. Results indicated that relative dielectric constant (ε_r) and piezoelectric strain constant (d₃₃) increase with aging day, and approach to asymptotic values after 70 days. Temperature treatment to the composite is a dominate factor to enhance piezoelectric properties. The d₃₃ and ε_r values of PZT/cement composites treated at the ambient temperature (23℃) were 57pC/N and 275 at the 70th aging day respectively, and then reached 106pC/N and 455 in turn with 150℃ treatment. The composite contains 4% kaolin having the highest value of d₃₃=111pC/N and ε_r=500 at 90 days because the porosity is the less than the others. Cement piezoelectric composites containing kaolin own the higher d₃₃ and ε_r value, compared with the other reported composites with 50% PZT. The porosity, the electromechanical coupling factor and impedance-frequency spectra of the cement piezoelectric composites were also discussed.

The aerospace industry continues to increase the use of adhesives for structural bonding due to the increased joint efficiency (reduced weight), even distribution of the load path and decreases in stress concentrations. However, the limited techniques for verifying the strength of adhesive bonds has reduced its use on primary structures and requires an intensive inspection schedule. This paper discusses a potential structural health monitoring (SHM) technique for the detection of disbonds through the in situ inspection of adhesive joints. This is achieved through the use of piezoelectric wafer active sensors (PWAS), thin unobtrusive sensors which are permanently bonded to the aircraft structure. The detection method discussed in this study is electromechanical impedance spectroscopy (EMIS), a local vibration method. This method detects disbonds from the change in the mechanical impedance of the structure surrounding the disbond. This paper will discuss how predictive modeling can provide valuable insight into the inspection method, and provide better results than empirical methods alone. The inspection scheme was evaluated using the finite element method, and the results were verified experimentally using a large aluminum test article, and included both pristine and disbond coupons.

The paper is an overview of work by the author in measuring and monitoring loads in bolts using an ultrasonic extensometer. A number of cases of bolted joints are covered. These include, a clamped joint with clearance fit between the bolt and hole, a clamped joint with bolt in an interference fit with the hole, a flanged joint which allows the flange and bolt to bend; and a shear joint in a clevis and tang configuration. These applications were initially developed for measuring and monitoring preload in National Aeronautics and Space Administration (NASA) Space Shuttle Orbiter critical joints but are also applicable for monitoring loads in other critical bolted joints of structures such as transportation bridges and other aerospace structures. The papers cited here explain how to set-up a model to estimate the ultrasonic load factor and accuracy for the ultrasonic preload application in a clamped joint with clearance fit. The ultrasonic preload application for clamped joint with bolt in an interference fit can also be used to measure diametrical interference between the bolt shank and hole, as well as interference pressure on the bolt shank. Results of simulation and experimental data are given to demonstrate use of ultrasonic measurements in a shear joint. A bolt in a flanged joint experiences both tensile and bending loads. This application involves measurement of bending and tensile preload in a bolt. The ultrasonic beam bends due to bending load on the bolt. Results of a numerical technique to compute the trace of ultrasonic ray are presented.

Three different ultrasonic nonlinearity parameter measurement methods are available: the capacitive detection method to measure absolute values of nonlinearity parameters; the laser interferometry detection as a non-contact method; the contact piezoelectric transducer based relative measurement method. Among all these three methods, the contact piezoelectric transducer detection method has been used as the most practical approach due to its operational simplicity for materials damage assessments. One of the main drawbacks of this technique, however, has been the low sensitivity of the receiving transducers, especially for the second harmonic signals, causing a high uncertainty in measurements. In this work, it is demonstrated with a copper [100] single crystal that a couple of high Q-value band-pass filters and a low-noise preamplifier introduced in the system not only improve the measurement accuracy but also make it possible to determine absolute values of nonlinearity parameters without using the complex capacitive detection method.

Structural health monitoring techniques using smart materials are on rise to meet the ever ending demand due to increased construction and manufacturing activities worldwide. The civil-structural components such as slabs, beams and columns and aero-components such as wings are constantly subjected to some or the other forms of external loading. This article thus focuses on condition monitoring due to loading/unloading cycle for a simply supported aluminum beam using multiple smart materials. On the specimen, fibre optic polarimetric sensor (FOPS) and fibre Bragg grating (FBG) sensors were glued. Piezoelectric wafer active sensor (PWAS) was also bonded at the centre of the specimen. FOPS and FBG provided the global and local strain measurements respectively whereas, PWAS predicted boundary condition variations by electromechanical admittance signatures. Thus these multiple smart materials together successfully assessed the condition of structure for loading and unloading tests.

The paper provides simulation data of previous work by the author in developing a model for estimating detectability of crack-like flaws in radiography. The methodology is developed to help in implementation of NASA Special x-ray radiography qualification, but is generically applicable to radiography. The paper describes a method for characterizing the detector resolution. Applicability of ASTM E 2737 resolution requirements to the model are also discussed. The paper describes a model for simulating the detector resolution. A computer calculator application, discussed here, also performs predicted contrast and signal-to-noise ratio calculations. Results of various simulation runs in calculating x-ray flaw size parameter and image contrast for varying input parameters such as crack depth, crack width, part thickness, x-ray angle, part-to-detector distance, part-to-source distance, source sizes, and detector sensitivity and resolution are given as 3D surfaces. These results demonstrate effect of the input parameters on the flaw size parameter and the simulated image contrast of the crack. These simulations demonstrate utility of the flaw size parameter model in setting up x-ray techniques that provide desired flaw detectability in radiography. The method is applicable to film radiography, computed radiography, and digital radiography.

The paper provides information on a new infrared (IR) image contrast data post-processing method that involves converting raw data to normalized contrast versus time evolutions from the flash infrared thermography inspection video data. Thermal measurement features such as peak contrast, peak contrast time, persistence time, and persistence energy are calculated from the contrast evolutions. In addition, simulation of the contrast evolution is achieved through calibration on measured contrast evolutions from many flat bottom holes in a test plate of the subject material. The measurement features are used to monitor growth of anomalies and to characterize the void-like anomalies. The method was developed to monitor and analyze void-like anomalies in reinforced carbon-carbon (RCC) materials used on the wing leading edge of the NASA Space Shuttle Orbiters, but the method is equally applicable to other materials. The thermal measurement features relate to the anomaly characteristics such as depth and size. Calibration of the contrast is used to provide an assessment of the anomaly depth and width which correspond to the depth and diameter of the equivalent flat bottom hole (EFBH) from the calibration data. An edge detection technique called the half-max is used to measure width and length of the anomaly. Results of the half-max width and the EFBH diameter are compared with actual widths to evaluate utility of IR Contrast method. Some thermal measurements relate to gap thickness of the delaminations. Results of IR Contrast method on RCC hardware are provided. Keywords: normalized contrast, flash infrared thermography.

Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors’ best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.

Recently damage detection methods based on measured vibration data for structural health monitoring (SHM) have been intensively studied. In order to decrease the number of required sensors, however, most of their methods focus only on single dimensional systems, in spite that there are some cases that torsional vibration greatly affect for structural damage. Although some studies consider multiple dimensional systems using frame structures, usually they need lots of sensors and calculation is time-consuming. Therefore, the balance between the cost and the particularity is very important for SHM system. In this paper, a method to localize the damaged area of multi-story buildings considering torsional components is proposed to detect the damage simply and particularly. This method focuses on shift in the center of rigidity caused by induced damage. The damaged quadrant of a certain story is identified comparing story eccentric distances of before and after damage-inducing seismic events. An adaptive extended Kalman filter (AEKF) is utilized to identify unknown structural parameters. Using a model which has four columns in each floor, several cases are considered in the verification study to disclose the capability of our proposed method.

Failures of aging light poles can jeopardize the safety of residents and damage adjacent structures. The need for reliable and efficient damage detection methods is raised. Any change in structural properties (e.g., mass, stiffness and damping) can lead to differences in the dynamic response of structures (i.e., modal frequencies). As a result, changes in dynamic responses can be used as indicators for damage detection. In this study, relationships between artificial damages and modal frequencies are determined by investigating the modal frequencies of intact and damaged light pole models using the finite element method (FEM). Finite element (FE) models were built with 5,529 C3D8R elements in ABAQUSR. New parameters (sensitive and insensitive modes) were defined and used to evaluate the sensitivity of the first ten modes of FE models. It is found that combinations of sensitive and insensitive modes are unique for each damage location and can be used to locate artificial damages in light pole models. Empirical equations are proposed to quantify damage level and damage size.

By utilizing a general purpose finite element (FE) code, the dynamic response of a rotor system was numerically studied in order to assess physical effects that are typically not taken into account using traditional rotordynamic codes. This included the allowance for disk flexibility as well as conducting a simultaneous heat transfer analysis that resulted in varying temperatures in the axial and radial directions. The numerical study utilized a generic, multi-disk model with a flexible hollow shaft. The Campbell diagrams and the mode shapes showed that neglecting any of the additional influences may cause errors regarding the predicted rotor dynamic response. By increasing the fidelity of the rotor model and accounting for the various effects, the slight signal modifications due to damage can be more easily recognized allowing for increased accuracy during rotor health monitoring.

Jeffcott rotor is employed to study the nonlinear vibration characteristics of breathing cracked rotor system and explore the possibility of further damage identification. This paper is an extension work of prior study based on 4 degree-of-freedom Jeffcott rotor system. With consideration of disk tilting and gyroscopic effect, 6-dof EOM is derived and the crack model is established using SERR (strain energy release rate) in facture mechanics. Same as the prior work, the damaged stiffness matrix is updated by computing the instant crack closure line through Zero Stress Intensity Factor method. The breathing crack area is taken as a variable to analyze the breathing behavior in terms of eccentricity phase and shaft speed. Furthermore, the coupled vibration among lateral, torsional and longitudinal d.o.f is studied under torsional/axial excitation. The final part demonstrates the possibility of using vibration signal of damaged system for the crack diagnosis and health monitoring.

It represents the information about the complex work for the prolongation of the operational life cycle for a Nuclear Power plant (NPP). The analysis of the impact of the following types of the finite elements, their size and form on the accuracy of the obtained values of the characteristics of the stressed-deformed state of the analyzed sealed pipe penetration is given (as an example). The methodology of experimental researches is considered and solutions with the additional support of equipment is offered.

This paper presents a framework for nonlinear finite element (FE) model updating, in which state-of-the-art nonlinear structural FE modeling and analysis techniques are combined with the maximum likelihood estimation method (MLE) to estimate time-invariant parameters governing the nonlinear hysteretic material constitutive models used in the FE model of the structure. The estimation uncertainties are evaluated based on the Cramer–Rao lower bound (CRLB) theorem. A proof-of-concept example, consisting of a cantilever steel column representing a bridge pier, is provided to verify the proposed nonlinear FE model updating framework.

Data models have been increasingly used in recent years for documenting normal behavior of structures and hence detect and classify anomalies. Large numbers of machine learning algorithms were proposed by various researchers to model operational and functional changes in structures; however, a limited number of studies were applied to actual measurement data due to limited access to the long term measurement data of structures and lack of access to the damaged states of structures. By monitoring the structure during construction and reviewing the effect of construction events on the measurement data, this study introduces a new approach to detect and eventually classify anomalies during construction and after construction. First, the implementation procedure of the sensory network that develops while the bridge is being built and its current status will be detailed. Second, the proposed anomaly detection algorithm will be applied on the collected data and finally, detected anomalies will be validated against the archived construction events.

A novel vibration-based damage identification methodology for the truss system with mass and stiffness uncertainties is proposed and demonstrated. This approach utilizes the damaged-induced changes of frequency response functions (FRF) to assess the severity and location of the structural damage in the system. The damage identification algorithm is developed basing on the least square and Newton-Raphson methods. The dynamical model of system is built using finite element method and Lagrange principle while the crack model is based on fracture mechanics. The method is synthesized via numerical examples for a truss system to demonstrate the effectiveness in detecting both stiffness and mass uncertainty existed in the system.

In the structural health monitoring (SHM) of civil infrastructure, dynamic methods using mass, damping, and stiffness for characterizing structural health have been a traditional and widely used approach. Changes in these system parameters over time indicate the progress of structural degradation or deterioration. In these methods, capability of predicting system parameters is essential to their success. In this paper, research work on the development of a dynamic SHM method based on perturbation analysis is reported. The concept is to use externally applied mass to perturb an unknown system and measure the natural frequency of the system. Derived theoretical expressions for mass and stiffness prediction are experimentally verified by a building model. Dynamic responses of the building model perturbed by various masses in free vibration were experimentally measured by a mobile device (cell phone) to extract the natural frequency of the building model. Single-degreeof- freedom (SDOF) modeling approach was adopted for the sake of using a cell phone. From the experimental result, it is shown that the percentage error of predicted mass increases when the mass ratio increases, while the percentage error of predicted stiffness decreases when the mass ratio increases. This work also demonstrated the potential use of mobile devices in the health monitoring of civil infrastructure.

Transient vibrations of the tower supporting a horizontal-axis wind turbine were recorded using a microwave interferometer. Variations in dominant frequencies have been reported in the previous study. Signal analyses aiming to uncouple different frequency components were performed using reassigned spectrogram, a time-frequency representation based on time-corrected short time Fourier transform. Optimal resolutions in both time and frequency domains were first investigated using synthetic signals. The goal was to seek out the favorable combinations of window size and overlapping portions of adjacent windows for a data sequence at a given sampling rate. The dominant frequency found in reassigned spectrogram agrees with that obtained using Fourier spectrum of the same transient measurements of the wind turbine tower under investigation.

Interpretation of Lamb waves signals can rise serious difficulties due to their multi-modal nature. Different modes propagating with different velocities can be misleading with damage reflected components. As a solution to this problem we propose a technique capable of modes separation based on a polarization filter. Both S0 and A0 Lamb modes exhibit elliptical polarization, however, their polarization parameters, i.e. the ratios of in-plane and out-of-plane displacements and phase-shifts between these components are different. Furthermore, these parameters can be considered constant in a narrow frequency band. Therefore, if the vertical and horizontal components of the wave motion are available, it is possible to apply signal processing technique referred to as oblique polarization filter. This operation is based on phase-shifts and amplifications of the in- and out-of-plane components, which results in orthogonal, linearly polarized A0 and S0 waves signals. In this paper the proposed technique will be illustrated using both numerical simulations and experimental data. The simulations of wave propagation were performed using local interaction simulation approach (LISA) assuming isotropic material. The experiments were performed using 3D laser scanning Doppler vibrometer that allowed to capture the in-plane and out-of-plane wave components.

This study presents a nondestructive evaluation method based on guided ultrasonic waves (GUW) to estimate corrosion in steel strands. Steel strands are one of the main components in constructing prestressed structures. Hidden corrosion in these structures has become a concern for designers, owners and regulators as it can eventuate in disastrous failure. In this study, a reference-free algorithm is proposed to quantify the extent of corrosion through estimating the cross section loss using dispersion curves and the velocity of certain frequency components in the waveform. Experimental test setups were designed to accelerate corrosion on two similarly loaded steel strands. One strand was embedded in concrete (to simulate a prestressed concrete beam) and the other was free (to resemble a prestressed cable). Visual inspection, halfcell potential, and mass loss measurements were employed as supporting evidences for the state of corrosion. An uncertainty analysis was also carried out to investigate how close this method can estimate the diameter of wires in a strand. The method could reasonably estimate the diameter of the wires without a reference baseline.

Inevitably, rail squats and studs are continuing to be a serious problem for railway organisations around the world in the 21st century. They are typically classified as the growth of any cracks that have grown longitudinally through the subsurface and some of the cracks propagating to the bottom of rails transversely, and have branched from initial longitudinal cracks with a depression of rail surface. The horizontal crack, which results in a depression of rail surface, induces increased maintenance level, more frequent monitoring, compromised rail testing (as the crack shields the signal echoes), and possible broken rails. This paper presents field investigations using a magnetised-rail testing device developed by MRX Technologies to identify and prioritise the rail squats. Most of the in situ squats were found on the high rail of the transition (variable-radius curved track), which is associated with rolling contact fatigue (RCF). This investigation highlights the field performance of the MRX’s surface crack detection technology in comparison with the traditional ultrasonic method and detailed walking inspection. Visually, it was found in the field that the size of the RCF squats varies from very small to moderate. The predicted crack data were obtained by scanning the magnitised rails. The comparison of the actual crack depths (ultrasonic) and the predicted crack depths (MRX device) shows: • A possible correlation for small RCF/ squat cracks. • Poor interpretation of larger defects and welds. The field assessment also suggests some practical issues required for further development, including the detection of rail spalling, deep transverse crack, welding, and so on.

Due to the urbanization in China, some building construction sites are planned on areas above abandoned underground mines, which pose a concern for the stability of these sites and a critical need for the use of reliable site investigations. The array-based surface wave method has the potential for conducting large-scale field surveys at areas above underground mines. However, the dense deployment of conventional geophones requires heavy digital cables. On the other hand, the bulky and expensive standard stand-alone seismometers limit the number of stations for the array-based surface wave measurements. Therefore, this study developed a low-cost cableless geophone system for the array-based surface wave survey. A field case study using this novel cableless geophone system was conducted at an abandoned underground mine site in China to validate its functionality.

Shield tunnel SHM is in the path of rapid development currently while massive monitoring data processing and quantitative health grading remain a real challenge, since multiple sensors belonging to different types are employed in SHM system. This paper addressed the fuzzy cluster method based on fuzzy equivalence relationship for the health evaluation of shield tunnel SHM. The method was optimized by exporting the FSV map to automatically generate the threshold value. A new holistic health score(HHS) was proposed and its effectiveness was validated by conducting a pilot test. A case study on Nanjing Yangtze River Tunnel was presented to apply this method. Three types of indicators, namely soil pressure, pore pressure and steel strain, were used to develop the evaluation set U. The clustering results were verified by analyzing the engineering geological conditions; the applicability and validity of the proposed method was also demonstrated. Besides, the advantage of multi-factor evaluation over single-factor model was discussed by using the proposed HHS. This investigation indicated the fuzzy cluster method and HHS is capable of characterizing the fuzziness of tunnel health, and it is beneficial to clarify the tunnel health evaluation uncertainties.

Among existing strategies for non-destructive evaluation of coating thickness, ultrasonic methods based on the measurement of the Time-of-Flight (ToF) of high frequency bulk waves propagating through the thickness of a structure are widespread. However, these methods only provide a very localized measurement of the coating thickness and the precision on the results is largely affected by the surface roughness, porosity or multi-layered nature of the host structure. Moreover, since the measurement is very local, inspection of large surfaces can be time consuming. This article presents a robust methodology for coating thickness estimation based on the generation and measurement of guided waves. Guided waves have the advantage over ultrasonic bulk waves of being less sensitive to surface roughness, and of measuring an average thickness over a wider area, thus reducing the time required to inspect large surfaces. The approach is based on an analytical multi-layer model and intercorrelation of reference and measured signals. The method is first assessed numerically for an aluminum plate, where it is demonstrated that coating thickness can be measured within a precision of 5 micrometers using the S0 mode at frequencies below 500 kHz. Then, an experimental validation is conducted and results show that coating thicknesses in the range of 10 to 200 micrometers can be estimated within a precision of 10 micrometers of the exact coating thickness on this type of structure.

This paper proposes a novel and effective method to identify the damage in the 2-D beam via Lamb wave. Two problems in the structural damage identification: damage location and damage severity are solved based on the theory of compressive sampling (CS) which indicates that sparse or compressible signals can be reconstructed using just a few measurements. Because of the sparsity nature of the damage, a database of damage features is established via a sparse representation for damage identification and assessing. Specifically, this proposed method consists of two steps: damage database establishing and feature matching. In the first step, the features database of both the healthy structure and the damaged structure are represented by the Lamb wave which propagates in the 2-D beam. Then in the matching step, expressing the test modal feature as a linear combination of the bases of the over-complete reference feature database which is constructed by concatenating all modal features of all candidate damage locations builds a highly underdetermined linear system of equations with an underlying sparse representation, which can be correctly recovered by ℓ1-minimization based on CS theory; the non-zero entry in the recovered sparse representation directly identifies the damage location and severity. In addition, numerical simulation is conducted to verify the method. This method of identifying damage location and assessing damage severity, using limited Lamb wave features, obtains good result.

The purpose of this article is to numerically quantify the stress state of complex loaded thick steel plates using the fundamental theory of acoustoelasticity, which is the relationship with stress and ultrasonic velocity in the nonlinear regime. The normal and shear stresses of a thick plate can be measured using a phased array placement of ultrasonic sensors and Rayleigh ultrasonic waves. Three measurement angles (i.e., 0 45 and 90 degrees) are selected since three measurements are needed to solve the stress tensor in an isotropic plate. The ultrasonic data is influenced significantly by the frequency of the Rayleigh waves as well as the thickness of the plate being examined; consequently the overall experimental process is influenced by the measurement parameters. In this study, a numerical demonstration is implemented to extract the nonlinearity coefficients using a 3D structural geometry and Murnaghan material model capable of examining the effects of various plate thicknesses and ultrasonic frequencies on the shear stress measurement. The purpose is that as the thickness becomes smaller, the shear stress becomes negligible at the angled measurement. For thicker cross section, shear stress becomes influential if the depth of penetration of Rayleigh wave is greater than the half of the thickness. The correlation between the depth of penetration and shear stress is then obtained. The numerical results are compared with 1 MHz ultrasonic frequency and a 3/8 inch thick steel plate loaded uniaxially while the measurement direction is angled to have the presence of shear stress in the measurement direction.

Multiple ultrasonic guided-wave modes propagating along a pipe travel with different velocities which are themselves a function of frequency. Reflections from the features of the structure (e.g., boundaries, pipe welding, damage, etc.), and their complex superposition, adds to the complexity of guided-waves. Guided-wave based damage diagnosis of pipelines becomes even more challenging when environmental and operational conditions (EOCs) vary (e.g., temperature, flow rate, inner pressure, etc.). These complexities make guided-wave based damage diagnosis of operating pipelines a challenging task. This paper reviews the approaches to-date addressing these challenges, and highlights the preferred characteristics of a method that simplifies guided-wave signals for damage diagnosis purposes. A method is proposed to extract a sparse subset of guided-wave signals in time-domain, while retaining optimal damage information for detection purpose. In this paper, the general concept of this method is proved through an extensive set of experiments. Effects of temperature variation on detection performance of the proposed method, and on discriminatory power of the extracted damage-sensitive features are investigated. The potential of the proposed method for real-time damage detection is illustrated, for wide range of temperature variation scenarios (i.e., temperature difference between training and test data varying between -2°C and 13°C).

Health monitoring of wind turbines is typically performed using conventional sensors (e.g. strain-gages and accelerometers) that are usually mounted to the nacelle or gearbox. Although many wind turbines stop operating due to blade failures, there are typically few to no sensor mounted on the blades. Placing sensors on the rotating parts of the structure is a challenge due to the wiring and data transmission constraints. Within the current work, an approach to monitor full-field dynamic response of rotating structures (e.g. wind turbine blades or helicopter rotors) is developed and experimentally verified. A wind turbine rotor was used as the test structure and was mounted to a block and horizontally placed on the ground. A pair of bearings connected to the rotor shaft allowed the turbine to freely spin along the shaft. Several optical targets were mounted to the blades and a pair of high-speed cameras was used to monitor the dynamics of the spinning turbine. Displacements of the targets during rotation were measured using three-dimensional point tracking. The point tracking technique measured both rigid body displacement and flexible deformation of the blades at target locations. While the structure is rotating, only flap displacements of optical targets (displacements out of the rotation plane) were used in strain prediction process. The measured displacements were expanded and applied to the finite element model of the turbine to extract full-field dynamic strain on the structure. The proposed approach enabled the prediction of dynamic response on the outer surface as well as within the inner points of the structure where no other sensor could be easily mounted. In order to validate the proposed approach, the predicted strain was compared to strain measured at four locations on the spinning blades using a wireless strain-gage system.

This paper proposes a non-contact measurement technique for health monitoring of wind turbine blades using acoustic beamforming techniques. The technique works by mounting an audio speaker inside a wind turbine blade and observing the sound radiated from the blade to identify damage within the structure. The main hypothesis for the structural damage detection is that the structural damage (cracks, edge splits, holes etc.) on the surface of a composite wind turbine blade results in changes in the sound radiation characteristics of the structure. Preliminary measurements were carried out on two separate test specimens, namely a composite box and a section of a wind turbine blade to validate the methodology. The rectangular shaped composite box and the turbine blade contained holes with different dimensions and line cracks. An acoustic microphone array with 62 microphones was used to measure the sound radiation from both structures when the speaker was located inside the box and also inside the blade segment. A phased array beamforming technique and CLEAN-based subtraction of point spread function from a reference (CLSPR) were employed to locate the different damage types on both the composite box and the wind turbine blade. The same experiment was repeated by using a commercially available 48-channel acoustic ring array to compare the test results. It was shown that both the acoustic beamforming and the CLSPR techniques can be used to identify the damage in the test structures with sufficiently high fidelity.

Structural temperature has been widely recognized as one of the most negative environmental effect on bridge. In this study, the temperature distribution of a large rigid-continuous concrete box girder bridge is investigated combining the numerical simulation and the field measurements. A temperature sensor system has be installed on the bridge for field monitoring the structural temperature. For simulation study, the fine tow-dimensional finite element (FE) model of box girder section is first constructed. Then, the time-dependent thermal boundary conditions are determined to extensively take account of environmental factors resulting of thermal effects on bridge. At last, transient heat transfer analysis is implemented on FE model and corresponding time-dependent temperature distribution is obtained. The analytical results are compared with the measurements for validation of the thermal analysis method. The results have very good agreements with the measurements, and the temperature variations exactly explicate the changes of environmental conditions such as solar radiation and ambient temperature of daily. The temperature simulation provides a foundation for the structural analysis of temperature induced effects.

In structural health monitoring, Lamb waves are employed extensively to examine and monitor thin structures, such as plates and shells. Typically, a network of piezoelectric transducers is attached to the structural plate member and used for both transmission and reception of the Lamb waves. The signals scattered from defects in the plate are recorded by employing the transducers in pitch-catch pairings. In this paper, we propose a multi-frequency, multi-modal sparse reconstruction approach for localizing defects in thin plates. We simultaneously invert Lamb wave based scattering models for both fundamental propagating symmetric and anti-symmetric wave modes, while exploiting the inherent sparsity of the defects. Dictionaries are constructed for both fundamental wave modes, which account for associated dispersion and attenuation as a function of frequency. Signals are collected at two independent frequencies; one at which the fundamental symmetric mode is dominant, and the other at which only the fundamental anti-symmetric wave mode is present. This provides distinct and separable multi-modal contributions, thereby permitting sparse reconstruction of the region of interest under the multiple measurement vector framework. The proposed defect localization approach is validated using simulated data for an aluminum plate.

Structural Health Monitoring (SHM) systems require integration of non-destructive technologies into structural design and operational processes. Modeling and simulation of complex NDE inspection processes are important aspects in the development and deployment of SHM technologies. Ray tracing techniques are vital simulation tools to visualize the wave path inside a material. These techniques also help in optimizing the location of transducers and their orientation with respect to the zone of interrogation. It helps in increasing the chances of detection and identification of a flaw in that zone. While current state-of-the-art techniques such as ray tracing based on geometric principle help in such visualization, other information such as signal losses due to spherical or cylindrical shape of wave front are rarely taken into consideration. The problem becomes a little more complicated in the case of dispersive guided wave propagation and near-field defect scattering. We review the existing models and tools to perform ultrasonic NDE simulation in structural components. As an initial step, we develop a ray-tracing approach, where phase and spectral information are preserved. This enables one to study wave scattering beyond simple time of flight calculation of rays. Challenges in terms of theory and modelling of defects of various kinds are discussed. Various additional considerations such as signal decay and physics of scattering are reviewed and challenges involved in realistic computational implementation are discussed. Potential application of this approach to SHM system design is highlighted and by applying this to complex structural components such as airframe structures, SHM is demonstrated to provide additional value in terms of lighter weight and/or longevity enhancement resulting from an extension of the damage tolerance design principle not compromising safety and reliability.

Ultrasonic guided-waves propagating in pipes with varying environmental and operational conditions (EOCs) are usually the results of complex superposition of multiple modes travelling in multiple paths. Among all of the components forming a complex guided-wave signal, the arrivals scattered by damage (so called scatter signal) are of importance for damage diagnosis purposes. This paper evaluates the potentials of nonlinear decomposition methods for extracting the scatter signal from a multi-modal signal recorded from a pipe under varying temperatures. Current approaches for extracting scatter signal can be categorized as (A) baseline subtraction methods, and (B) linear decomposition methods. In this paper, we first illustrate, experimentally, the challenges for applying these methods on multi-modal signals at varying temperatures. To better analyze the experimental results, the effects of temperature on multi-modal signals are simulated. The simulation results show that different wave modes may have significantly different sensitivities to temperature variations. This brings about challenges such as shape distortion and nonlinear relations between the signals recorded at different temperatures, which prevent the aforementioned methods to be extensible to wide range of temperatures. In this paper, we examine the potential of a nonlinear decomposition method, namely nonlinear principal component analysis (NLPCA), for removing the nonlinear relation between the components of a multi-modal guided-wave signal, and thus, extracting the scatter signal. Ultrasonic pitch-catch measurements from an aluminum pipe segment in a thermally controlled laboratory are used to evaluate the detection performance of the damage-sensitive features extracted by the proposed approach. It is observed that NLPCA can successfully remove nonlinear relations between the signal bases, hence extract scatter signal, for temperature variations up to 10℃, with detection accuracies above 99%.

In spite of their many advantages, real-world application of guided-waves for structural health monitoring (SHM) of pipelines is still quite limited. The challenges can be discussed under three headings: (1) Multiple modes, (2) Multipath reflections, and (3) Sensitivity to environmental and operational conditions (EOCs). These challenges are reviewed in the authors’ previous work. This paper is part of a study whose objective is to overcome these challenges for damage diagnosis of pipes, while addressing the limitations of the current approaches. That is, develop methods that simplify signal while retaining damage information, perform well as EOCs vary, and minimize the use of transducers. In this paper, a supervised method is proposed to extract a sparse subset of the ultrasonic guided-wave signals that contain optimal damage information for detection purposes. That is, a discriminant vector is calculated so that the projections of undamaged and damaged pipes on this vector is separated. In the training stage, data is recorded from intact pipe, and from a pipe with an artificial structural abnormality (to simulate any variation from intact condition). During the monitoring stage, test signals are projected on the discriminant vector, and these projections are used as damage-sensitive features for detection purposes. Being a supervised method, factors such as EOC variations, and difference in the characteristics of the structural abnormality in training and test data, may affect the detection performance. This paper reports the experiments investigating the extent to which the differences in damage size and damage location, as well as temperatures, can influence the discriminatory power of the extracted damage-sensitive features. The results suggest that, for practical ranges of monitoring and damage sizes of interest, the proposed method has low sensitivity to such training factors. High detection performances are obtained for temperature differences up to 14°C. The findings reported in this paper suggest that although the proposed method is a supervised approach, labeling of the training data does not require prior knowledge about the damage characteristics (e.g., size, location). Moreover, the potential of the proposed method for online monitoring is illustrated, for wide range of temperature variations and different damage scenarios.

Non-destructive testing methods based on ultrasonic waves are one of the most popular methods for damage detection in structures. Of these ultrasonic waves Lamb waves are of particular interest for the inspection of large structures for various reasons. Therefore scattering of Lamb waves from flaws has generated a considerable amount of research over last couple of decades. Most of the work has been done using computational tools like Finite Element Methods and experimental technique. In this paper an analytical approach is presented to develop a fundamental understanding of the scattering of Lamb waves from geometric discontinuities in 2 dimensions. We have considered simplest of all geometric discontinuity – a step, as this fundamental understanding can easily be extended to corrosion or crack.

Around 3,000,000 million vehicle miles are annually traveled utilizing the US transportation systems alone. In addition to the road traffic safety, maintaining the road infrastructure in a sound condition promotes a more productive and competitive economy. Due to the significant amounts of financial and human resources required to detect surface cracks by visual inspection, detection of these surface defects are often delayed resulting in deferred maintenance operations. This paper introduces an automatic system for acquisition, detection, classification, and evaluation of pavement surface cracks by unsupervised analysis of images collected from a camera mounted on the rear of a moving vehicle. A Hessian-based multi-scale filter has been utilized to detect ridges in these images at various scales. Post-processing on the extracted features has been implemented to produce statistics of length, width, and area covered by cracks, which are crucial for roadway agencies to assess pavement quality. This process has been realized on three sets of roads with different pavement conditions in the city of Brockton, MA. A ground truth dataset labeled manually is made available to evaluate this algorithm and results rendered more than 90% segmentation accuracy demonstrating the feasibility of employing this approach at a larger scale.

Prestressed concrete experiences low to no tensile stresses, which results in limiting the occurrence of cracks in prestressed concrete structures. However, the nature of construction of these structures requires the concrete not to be subjected to the compressive force from the prestressing tendons until after it has gained sufficient compressive strength. Although the structure is not subjected to any dead or live load during this period, it is influenced by shrinkage and thermal variations. Thus, the concrete can experience tensile stresses before the required compressive strength has been attained, which can result in the occurrence of “pre-release” cracks. Such cracks are visually closed after the transfer of the prestressing force. However, structural capacity and behavior can be impacted if cracks are not sufficiently closed. This paper researches a method for the verification of the status of pre-release cracks after transfer of the prestressing force, and it is oriented towards achievement of Level IV Structural Health Monitoring (SHM). The method relies on measurements from parallel long-gauge fiber optic sensors embedded in the concrete prior to pouring. The same sensor network is used for the detection and characterization of cracks, as well as the monitoring of the prestressing force transfer and the determination of the extent of closure of pre-release cracks. This paper outlines the researched method and presents its application to a real-life structure, the southeast leg of Streicker Bridge on the Princeton University campus. The application structure is a curved continuous girder that was constructed in 2009. Its deck experienced four pre-release cracks that were closed beyond the critical limits based on the results of this study.

Accurate pavement management systems are essential for states’ Department Of Transportation and roadway agencies to plan for cost-effective maintenance and repair (M and R) strategies. Pavement deterioration model is an imperative component of any pavement management system since the future budget and M and R plans would be developed based on the predicted pavement performance measures. It is crucial for the pavement deterioration models to consider the factors that significantly aggravate the pavement condition. While many studies have highlighted the impact of different environmental, load, and pavement’s structure on the life cycle of the pavement, effect of extreme weather events such as Floods and Snow Storms have often been overlooked. In this study, a pavement deterioration model is proposed which would consider the effect of traffic loads, climate conditions, and extreme weather events. Climate, load and performance data has been compiled for over twenty years and for eight states using the Long Term Pavement Performance (LTPP) and National Oceanic and Atmospheric Administration (NOAA) databases. A stepwise regression approach is undertaken to quantify the effect of the extreme weather events, along with other influential factors on pavement performance in terms of International Roughness Index (IRI). Final results rendered more than 90% correlation with the quantified impact values of extreme weather events.

Cementitious-based strain sensors can be used as robust monitoring systems for civil engineering applications, such as road pavements and historic structures. To enable large-scale deployments, the fillers used in creating a conductive material must be inexpensive and easy to mix homogeneously. Carbon black (CB) particles constitute a promising filler due to their low cost and ease of dispersion. However, a relatively high quantity of these particles needs to be mixed with cement in order to reach the percolation threshold. Such level may influence the physical properties of the cementitious material itself, such as compressive and tensile strengths. In this paper, we investigate the possibility of utilizing a polymer to create conductive chains of CB more quickly than in a cementitious-only medium. This way, while the resulting material would have a higher conductivity, the percolation threshold would be reached with fewer CB particles. Building on the principle that the percolation threshold provides great sensing sensitivity, it would be possible to fabricate sensors using less conducting particles. We present results from a preliminary investigation comparing the utilization of a conductive paint fabricated from a poly-Styrene-co-Ethylene-co-Butylene-co-Styrene (SEBS) polymer matrix and CB, and CB-only as fillers to create cementitious sensors. Preliminary results show that the percolation threshold can be attained with significantly less CB using the SEBS+CB mix. Also, the study of the strain sensing properties indicates that the SEBS+CB sensor has a strain sensitivity comparable to the one of a CB-only cementitious sensor when comparing specimens fabricated at their respective percolation thresholds.

The use of radar non-destructive evaluation (NDE) technique for condition assessment of deteriorated civil infrastructure systems is an effective approach for preserving the sustainability of these systems. Radar NDE utilizes the interaction between radar signals (electromagnetic waves) and construction materials for surface and subsurface sensing based on dielectric properties and geometry. In the success of radar inspection, it is imperative to develop models capable of predicting the dielectric properties of the materials under investigation. The dielectric properties (dielectric constant and loss factor) of oven-dried cement paste specimens with water-to-cement (w/c) ratios (0.35, 0.40, 0.45, 0.50, 0.55) in the frequency range of 1.02 GHz to 4.50 GHz were studied and modeled using modified Debye's models. An open-ended coaxial probe and a network analyzer were used to measure dielectric properties. Forward models are proposed and inversed for predicting the w/c ratio of a given oven-dried cement paste specimen. Modeling results agreed with the experimental data. The proposed models can be used for predicting the dielectric properties of oven-dried cement paste specimens. Also, the modeling approach can be applied to other cementitious materials (e.g., concrete) with additional modification.

This paper presents a new ground penetrating radar (GPR) design approach using orthogonal frequency division multiplexing (OFDM) and compressive sensing (CS) algorithms. OFDM technique is applied to leverage GPR operating speed with multiple frequency tones transmission and receiving concurrently, and CS technique allows utilizing reduced frequency tones without compromising data reconstruction accuracy. Combination of OFDM and CS boosts the radar operating efficiency. For GPR image reconstruction, a synthetic aperture radar (SAR) technique is implemented.

In this paper, a method using the instantaneous phase information of the reflection ground penetrating radar (GPR) signal to detect the variation of sand moisture is developed. The moisture changes the permittivity of the medium, which results in different speed when the GPR electromagnetic (EM) wave propagates in the medium. In accordance to this principle, we develop an analytical method to extract GPR reflection signal’s instantaneous phase parameters utilizing Hilbert Transform for sand moisture characterization. For test evaluation, Finite Difference Time Domain (FDTD) numerical simulations using a 3rd party open source program GprMax V2.0, and laboratory experiments on sand samples are conducted using a commercial GPR (2.3 GHz Mala CX) as the data acquisition system.

Safety, performance, economy and durability are essential items to qualify materials for the manufacturing of structures used in different areas. Generally, the materials used for this purpose are formed by composites and sometimes they can present failure during the manufacturing process. Such failures can also occur during use due to fatigue and wear, causing damage often difficult to be visually detected. In these cases, the use of non destructive testing (NDT) has proven to be a good choice for assessing the materials quality. The objective of this work was the electromechanical impedance evaluation of massive aluminum structures using ultrasonic transducers to detect discontinuities in the material. The tests have been done using an impedance analyzer (Agilent 4294A), an ultrasound transducer (1.6 MHz of central frequency), two types of PZT ceramics (0.267 mm and 1 mm thickness) and four aluminum samples (250 x 50 x 50 mm) with the transducer placed at three different regions. One sample was kept intact (reference) and the others were drilled in three positions with different sizes of holes (5 mm. 8 mm and 11 mm). The electromechanical impedance was recorded for each sample. The root mean square deviation index (RMSD) between the impedance magnitude of the reference and damaged samples was calculated and it was observed an increase in the RMSD due to the increase of the diameter of the holes (failures) in the samples completely drilled. The results show that the proposed methodology is suitable for monitoring the integrity of aluminum samples. The technique may be evaluated in characterizing other materials to be used in the construction of prostheses and orthoses.

Seismic intensity map which reflects the actual situation of destruction in a certain area after the earthquake, and it is of great significance in guiding relief work and assessing damage loss. Based on cloud monitoring method proposed, we developed software, which can quickly investigate the seismic intensity distribution and draw the intensity map after the earthquake using the big data collected by individual smart phone questionnaire in earthquake zone. According to seismic attenuation law, we generated some seismic intensity values to test our system and successfully drawn out of the seismic intensity map.

After a blast event, it is important to quickly quantify the structural damage for emergency operations. In order improve the speed, accuracy, and efficiency of condition assessments after a blast, the authors have previously performed work to develop a methodology for rapid assessment of the structural condition of a building after a blast. The method involved determining a post-event equivalent stiffness matrix using vibration measurements and a finite element (FE) model. A structural model was built for the damaged structure based on the equivalent stiffness, and inter-story drifts from the blast are determined using numerical simulations, with forces determined from the blast parameters. The inter-story drifts are then compared to blast design conditions to assess the structures damage. This method still involved engineering judgment in terms of determining significant frequencies, which can lead to error, especially with noisy measurements. In an effort to improve accuracy and automate the process, this paper will look into a similar method of rapid condition assessment using subspace state-space identification. The accuracy of the method will be tested using a benchmark structural model, as well as experimental testing. The blast damage assessments will be validated using pressure-impulse (P-I) diagrams, which present the condition limits across blast parameters. Comparisons between P-I diagrams generated using the true system parameters and equivalent parameters will show the accuracy of the rapid condition based blast assessments.

Material degradation due to corrosion and corrosion fatigue has been recognized to significantly affect the airworthiness of civilian and military aircraft, especially for the current fleet of airplanes that have served beyond their initial design life. The ability to predict the corrosion damage development in aircraft components and structures, therefore, is of great importance in managing timely maintenance for the aging aircraft vehicles and in assisting the design of new ones. The assessment of aircraft corrosion and its influence on fatigue life relies on appropriate quantitative models that can evaluate the initiation of the corrosion as well as the accumulation during the period of operation. Beyond the aircraft regime, corrosion has also affected the maintenance, safety and reliability of other systems such as nuclear power systems, steam and gas turbines, marine structures and so on. In the work presented in this paper, we reviewed and studied several physics based pitting corrosion models that have been reported in the literature. The classic work of particle induced pitting corrosion by Wei and Harlow is reviewed in detail. Two types of modeling, a power law based simplified model and a microstructure based model, are compared for 2024-T3 alloy. Data from literatures are used as model inputs. The paper ends with conclusions and recommendations for future work.

The paper addresses some on-orbit nondestructive evaluation (NDE) needs of NASA for International Space Station (ISS). The presentation gives NDE requirements for inspecting suspect damage due to micro-meteoroids and orbital debris (MMOD) impact on the pressure wall of the ISS. This inspection is meant to be conducted from inside of the ISS module. The metallic wall of the module has a fixed wall thickness but also has integral orthogrid ribs for reinforcement. Typically, a single MMOD hit causes localized damage in a small area causing loss of material similar to pitting corrosion, but cracks may be present too. The impact may cause bulging of the wall. Results of the ultrasonic and eddy current demonstration scans on test samples are provided. The ultrasonic technique uses shear wave scans to interrogate the localized damage area from the surrounding undamaged area. The scanning protocol results in multiple scans, each with multiple "vee" paths. A superimposition and mosaic of the three-dimensional ultrasonic data from individual scans is desired to create C-scan images of the damage. This is a new data reduction process which is not currently implemented in state-of-art ultrasonic instruments. Results of ultrasonic scans on the simulated MMOD damage test plates are provided. The individual C-scans are superimposed manually creating mosaic of the inspection. The resulting image is compared with visibly detected damage boundaries, X-ray images, and localized ultrasonic and eddy current scans for locating crack tips to assess effectiveness of the ultrasonic scanning. The paper also discusses developments needed in improving ergonomics of the ultrasonic testing for on-orbit applications.

This paper proposes a novel and effective method in the field of Non-Destructive Evaluation (NDE). Traditional ultrasonic computerized tomography (UCT) is a heavy task to detect the damages in the object for the numerous measuring times and the huge cost of manual labor. However, utilizing the method proposed in this paper can effectively overcome this great disadvantage, the essence of the application of Compressive Sampling(CS) in the detection of the object is to selectively choose a small quantity of measuring path in the huge number of total measurements. Due to the sparsity of damages in concrete structure, the usage of CS is available. Firstly, we divide the object entirely into numerous grids in order to image the internal situation of the object respectively. Secondly, a measurement matrix to massively decline the quantity of the measuring time should be computed. Thirdly, the travel time of each path we selected according to the matrix should be acquired, utilizing these travel time by adopting the l1-minimization program can we consequently obtained the slowness of the elements inside the object, thus reconstruct the internal situation of the object clearly and effectively. Furthermore, by applying this method we proposed in this paper into the simulation we can not only determine the damage location but also figure the size of it out. Because of the massive decline of the measuring times and accurate reconstruction, we substantiate CS method applied into the monitoring of concrete structure proves to be a shortcut in the field of NDE.

The present study investigated crack visualization in metallic structures using time-domain reflectometry with a two-dimensional microstrip line. 2D inspection was enabled by covering the inspected structure surface with the microstrip conductor to compensate for the lack of information in the transverse direction. Crack visualization experiments were conducted using the proposed TDR with single ended of 2D MSL for different crack length. The experimental results demonstrated that crack propagation could be clearly visualized. However, false cracks appeared at the same position regardless of the crack position. The electromagnetic field simulation results clarified that the false cracks observed in the experiments were caused by cross talk. The problem can be eliminated by arranging the microstrip conductor appropriately.

In civil engineering, health monitoring and damage detection are typically carry out by using a large amount of sensors. Typically, most methods require global measurements to extract the properties of the structure. However, some sensors, like LVDT, cannot be used due to in situ limitation so that the global deformation remains unknown. An experiment is used to demonstrate the proposed algorithms: a one-story 2-bay reinforce concrete frame under weak and strong seismic excitation. In this paper signal processing techniques and nonlinear identification are used and applied to the response measurements of seismic response of reinforced concrete structures subject to different level of earthquake excitations. Both modal-based and signal-based system identification and feature extraction techniques are used to study the nonlinear inelastic response of RC frame using both input and output response data or output only measurement. From the signal-based damage identification method, which include the enhancement of time-frequency analysis of acceleration responses and the estimation of permanent deformation using directly from acceleration response data. Finally, local deformation measurement from dense optical tractor is also use to quantify the damage of the RC frame structure.

The Ground Penetrating Radar (GPR) is being recognized as an effective nondestructive evaluation technique to improve the inspection process. However, data interpretation and complexity of the results impose some limitations on the practicality of using this technique. This is mainly due to the need of a trained experienced person to interpret images obtained by the GPR system. In this paper, an algorithm to classify and assess the condition of infrastructures utilizing image processing and pattern recognition techniques is discussed. Features extracted form a dataset of images of defected and healthy slabs are used to train a computer vision based system while another dataset is used to evaluate the proposed algorithm. Initial results show that the proposed algorithm is able to detect the existence of defects with about 77% success rate.