This PDF file contains the front matter associated with SPIE Proceedings Volume 12048, including the Title Page, Copyright information, Table of Contents and Conference Committee lists.

SHM technologies have evolved since the first IWSHM Conference, held in Stanford in September 1997, reaching in some cases a significant level of maturity and economic importance. The focus during the initial years was set on developing and demonstrating systems with a damage detection capability; proof of reliability is becoming a key theme in recent years, translating procedures already in usage in conventional NDE, as a requirement for SHM industrialization. For civil engineering applications, Operational Modal Analysis (OMA) and related items continues to be the dominant technique; whilst for thin stiffened shells, like aircraft structures, Guided Waves is the most notorious technology. Nevertheless, other technologies such as distributed fiber optic sensing, acoustic emission and nanoparticles-doped resins have demonstrated a significant potential. Looking into the near future, new algorithms and data processing techniques are most likely to deliver relevant advancements; drastic changes in sensor technologies seem unlikely. As in any engineering field, the development of simulation tools will remain critical to enrich the variety of experiments without prohibitive costs. The development of standards is a key factor for the acceptance and widespread usage by industry.

Ultrasonic guided waves have been investigated as a class of powerful tool for Structural Health Monitoring (SHM) and Nondestructive Evaluation (NDE). The key towards a highly sensitive structural sensing system resides in whether it can take full advantage of the favorable features of the interrogative wavefield. This paper reports recent research progress in SHM and wave mechanics from Active Materials and Intelligent Structures (AMIS) Lab at Shanghai Jiao Tong University. It addresses two major aspects in this regard: (1) effective and efficient methodology of exploring guided wave characteristics for damage detection and quantification; (2) recent progress on manipulating guided waves for enhanced SHM/NDE performance. In particular, the first aspect presents efficient modeling strategies for understanding linear and nonlinear guided wave signatures, including semi-analytical finite element method, local interaction simulation approach, and small-size regional numerical models. Examples of fatigue crack evaluation will be demonstrated with the extracted guided wave information in both linear and nonlinear regions. The second aspect puts forward the concept of engaging elastic metamaterials for inspection wave field control. It will demonstrate four different wave manipulation case studies: frequency component filtering, selective wave mode transmission, complete mode conversion, as well as tunable wave control with active elastic metamaterials. The paper finishes with summary, concluding remarks, and suggestions for future work.

We investigate curved surfaces operating as geodesic lenses for elastic waves. Consistently with findings in optics, we show that wave propagation occurs along rays that correspond to the geodesics of the curved surfaces, and we establish the geometric equivalence between Gaussian curvature and refractive index. This equivalence is formulated for flexural waves in curved shells by showing that, in the short wavelength limit, the ray equation corresponds to the classical equation of geodesics. We leverage this result to identify a non-Euclidean transformation that maps the geometric profile of a isotropic curved waveguide into a spatially varying refractive index distribution for a planar waveguide. These theoretical predictions are validated first through numerical simulations, and subsequently through experiments on 3D printed curved membranes with different curvature distributions. Numerical and experimental findings confirm that focal regions and caustic networks are correctly predicted based on geodesic evaluations. Our results form the basis for the design of curved profiles that correspond to spatial distributions of the refractive index and induce focal points by forcing waves to propagate along predefined trajectories. The findings of this study also suggest curvature as an attractive alternative to strategies based on the local tailoring of material properties and geometrical patterns that have gained in popularity for gradient-index lens design.

This paper studies the actuation and sensing of Lamb waves in thin metal plates, including those made of nuclear grade Zircaloy cladding material and carbon fiber reinforced polymer (CFRP) composite. The non-contact system consists of a pulsed laser (PL) for excitation and scanning laser Doppler vibrometer (SLDV) for sensing. The PL works in the thermoelastic regime to excite Lamb waves and SLDV provides high resolution multidimensional wavefield signals for evaluation. Two experimental setups are explored: excitation and sensing on the same side of the plate and each on opposing sides. Sensing parameters and surface enhancements are explored to obtain high frequency Lamb waves. The results show that the system can produce Lamb waves in higher frequency (<600 kHz) in plates thinner than 1 mm and are effective in thickness measurement.

Fiber optical Bragg Grating (FBG) sensors are of small size, maybe embedded into structures, and hence are widely used in structural health monitoring (SHM) in various fields. Due to their passive nature, the FBGs have to be used with piezo actuators in a hybrid SHM system. The directional sensitivity makes the signal processing a challenge, also, due to the directionality the damage mapping techniques developed for the omnidirectional PZT sensors cannot be directly applied to FBG sensors. Hence, this paper shows a damage mapping technique that overcomes the directional sensitivity of the FBG sensors. The methodology makes use of quadrant cosine area to obtain damage propagation paths. The damage localization approach works to identify the damage locations by taking advantage of the symmetry of the proposed circular network of PZT actuator-FBG arrangements. It aims at reducing the calculation time by introducing quadrant sector-based calculations of the damage indices.

To perform active structural health monitoring (SHM), guided waves (GW) have received great interest as they can inspect large areas with a few sensors and are sensitive to barely-visible structural damages. Fiber Bragg grating (FBG) sensors offer several advantages such as small size, low weight and ability to be embedded but their use has been limited for GW sensing due to their limited sensitivity while using spectrometers. FBG sensors in the edge-filtering configuration have overcome this issue with reasonable sensitivity and there is a renewed interest in their use. It is well known that when subjected to a transverse strain, the circular cross-section of the fiber deforms into an elliptical shape generating the birefringence phenomenon. This deformation, influences the coupling mode of the light inside the FBG and hence, modifies the resulting reflectivity spectrum. This paper investigates how controlled changes in the reflectivity spectrum can be introduced using different transverse loads. The effect of the modified spectrum on the sensitivity of the FBG for GW measurements is then studied. The study also investigates the effect of the transverse strain on the coupling of the GW from the structure into the fiber.

This paper presents the numerical study of the piezoelectric composite transducers for active sensing of concrete structures. A three-dimensional coupled field finite element model is initially constructed to capture the electro-mechanical impedance features of the piezoelectric composite transducers. The elaborated transducer takes the shape of a cube filled with the piezoelectric material. The spatially interdigitated electrodes are integrated to evenly separate the entire piezoelectric medium, forming the stacked piezoelectric units with opposite poling directions. Subsequently, the proposed transducers are embedded in a concrete beam, serving as the transmitter and the receiver, respectively. The electro mechanical impedance approach enabled by the proposed piezoelectric composite sensor is numerically conducted for crack detection. In addition, a pitch-catch active sensing procedure in concrete structures is realized via the transient analysis, modeling ultrasonic wave generation by the transmitter, propagation inside the concrete beam, interaction with the crack, and reception by the receiver. The developed piezoelectric composite transducer possesses tremendous potential for health monitoring of concrete structures. The paper finishes with discussion, concluding remarks, and suggestions for future work.

his paper presents results from an investigation of localized geometrical effects on crack-induced acoustic emission in welded plate-like structural components. The aim is to better understand the influence of weld geometry and precracking on the radiation patterns of guided waves. Attention is confined to the fundamental symmetric (S₀), antisymmetric (A₀), and shear-horizontal (SH0) guided modes. Numerical modeling is used to study cases of butt welds, T-joint welds, and finite-length full-penetration precracks. The numerical model is referenced against an analytical model which neglects these geometrical effects. Butt welds are shown to have a relatively small, and perhaps negligible, influence on radiation. T-joint welds exert a much stronger influence, especially behind the joint. Ahead of the joint, the S0 and SH0 modes are less affected. In the precrack case, mode conversion from a Rayleigh mode into the SH0 mode is shown to enhance primary radiation behind the precrack. An SH0 metric is therefore introduced, which shows crack-sizing potential up to about five plate-thicknesses.

Ultrasonic guided waves are commonly used in aerospace, civil, and mechanical industries for inspecting the health of a structure non-destructively. The excellent efficiency whilst sensing ultrasonic waves and the single lead-in and lead-out wire prove advantageous while using Fibre Bragg Grating (FBG) sensors as transducers. To overcome high signal-to-noise ratio demand of FBGs directly bonded to structures, researchers have recently proposed use of edge reflection approach as well as the remote bonding configuration. In order to maximise the efficiency of such FBGs, it is essential to understand the wave propagation behaviour in the FBG and the nature of strain transfer from the structure to the FBG. In this work, these aspects are studied using experiments and numerical model based on spectral finite element method (SFEM). The paper discusses the physics of the wave propagation from the structure to the fibre and the directional sensitivity of the FBG sensors in a computationally efficient way.

Detecting incipient damage in structures is an important challenge for the engineering community. The design of structural health monitoring (SHM) systems usually involves strategic definition of the excitation signal, selecting the best frequencies to avoid false alarms. In this sense, the present article introduces an approach to determine the optimal frequencies for asymmetrical damage detection in plates considering perpendicular incidence of longitudinal and flexural waves incoming in the damage. Numerical simulations are carried out by considering an aluminum plate, and the results show that the approach contributes to the establishment of more efficient SHM systems.

Standard machine learning algorithms traditionally address isolated tasks and require customization every time for a new kind of dataset. Thus, such a machine learning-based structural health monitoring approach lacks flexibility for any alteration in real-time field application. Most of the published articles have neither demonstrated nor discuss such capabilities. The present study attempts to overcome this by developing an Autoencoder based two-step diagnostic approach; In the first step, a 1D-CNN stacked auto-encoder (1D-CAE) is developed and trained to learn and extract the dominating features present in the Lamb waves. In the second step, the classification module is constructed, which inputs scaled features from the encoder and performs the classification between pristine and damaged conditions. The approach requires relatively minor training datasets than the conventional deep learning counterparts. The 1D-CAE is capable of auto-tuning, offers flexibility with different sizes of the 1D-input dataset. The study utilizes publicly available benchmark datasets collected by “Open guided waves”, which comprises Lamb wave signals recorded for a range of frequencies and artificial defects. The setup consists of arrays of transducers bonded to the 500mm × 500mm and 2mm thin CFRP plate in “Pitch-Catch” configuration, which excites five cycles modulated Hann-filtered sine wave signal with an amplitude of ±100 V. Both the 1D-CAE and the classifier module is trained under the supervision of Adam optimization algorithm with varying learning rate. The fully trained classifier can detect the damage in the thin CFRP plate with 95% accuracy. In the later stage, the performance is evaluated against the unseen samples with defects generated from the experimental setup. The proposed algorithm achieves a high level of generalization.

In order to support the continued trend of increased use of composite materials especially in aviation, efficient testing systems need to be developed. The anisotropic material properties of composites allow for high specific stiffnesses and strengths. However, some failure modes in composite structures cannot be identified through visual inspection and to ensure the health of the structures, a time-consuming and costly inspection approach must be taken. Often, this approach includes disassembly and premature part replacements. Thus, complete nondestructive inspection (NDI) and monitoring of composite structures in aviation is virtually nonexistent. Hence, there is a need to introduce an autonomous inspection method to reduce time and cost, while increasing aircraft reliability. To this end, several recent advancements of a mobile robotic platform and related algorithms for Lamb wave-based inspection of aircraft surfaces are presented here. The robots are envisioned to be operated in a low cardinality swarm, where each robot employs guided ultrasound technology to collaboratively inspect plate-like components. For the purpose of implementing a fully autonomous platform, simultaneous localization and mapping (SLAM) methods are combined with Lamb wave-based NDI techniques. Specifically, it is demonstrated that a novel Lamb wave-based edge seeking and tracing methodology can contribute to increasing testing efficiency, with the overarching goal of creating a full map of the tested structure including all potential flaws.

Composite structures are widely used in many industries. The impact based damages in such structures are the most important disadvantage as they lead to fiber breakage followed by cracks in the structure. The paper analyses the impact damage conditions in the carbon fiber reinforced polymer composite structures. The condition of the structure after the impact was studied. The structure is assessed with the help of guided wave signal processing methods to analyze the structure in both healthy and damage conditions. A higher impact energy-based study was performed to study the growth of the damage. A combined nondestructive based testing was performed on the composite structure to monitor the health of the structure.

In this paper results of damage assessment of composite panels using guided wave propagation phenomenon are presented. Elastic waves excitation is based on an air-coupled transducer (ACT) while the waves sensing is based on scanning laser Doppler vibrometry. It thus forms the full non-contact diagnostic approach. Thin panels made of fibre-reinforced polymer are investigated. The problem of optimal slope angle of ACT and the possibility of symmetric and antisymmetric elastic wave modes excitation is investigated. Research is related to the detection and localisation of artificial damage (additional mass, Teflon inserts). Moreover, the influence of single and multiple acoustic wave sources on artificial damage localization results and the problem of panel coverage area by elastic waves with large amplitudes to improve damage sensitivity are investigated. Different locations of ACTs and their influence on damage detection results are investigated. Two damage imaging algorithms based on elastic waves have been proposed, namely root mean square (RMS) energy maps and wavefield irregularity mapping (WIM). Moreover, results of simulations of elastic wave generation using ACT in CFRP plate based on a combination of FEM method and spectral element method (SEM) are presented. For this purpose COMSOL software and in-house MATLAB code are utilized, respectively

Carbon fiber laminates, consisting of highly anisotropic fiber-matrix ply-layers, are widely used in aerospace applications due to their good strength to weight ratio. However, poor interlaminar strength makes them prone to barely visible impact damage (BVID), significantly reducing the load bearing capacity of aircraft components. Guided ultrasonic waves have been widely used for structural health monitoring (SHM) of composite structures. Guided wave propagation and scattering at circular delaminations in a quasi-isotropic laminate was modelled using full three-dimensional (3D) Finite Element (FE) simulations in ABAQUS. Non-contact laser measurements were performed to obtain the scattered wavefield at a film insert delamination. The influence of ply layer anisotropy and incident wave direction were investigated both numerically and experimentally. Scattering directivity patterns were calculated using a baseline subtraction method and 2D scattering matrices were obtained for all incident wave directions. Circular magnets were used as a scattering target and numerical and measured scattering patterns were compared with those of the insert delamination. Strong directional dependency was observed for incident and scattered waves around both delamination and magnets, indicating energy focusing along the outer ply layers of the laminate. For the delamination a strong forward wave was observed, with low amplitude in other directions, whereas the magnet blocked forward transmission of the wave, demonstrating distinct scattering behavior. The anisotropic effects and different scattering patterns should be considered for guided wave sparse array SHM to ensure the robustness of imaging algorithms.

Structural Health Monitoring (SHM) systems are proposed as a method to reduce cost and improve flight safety by monitoring environmental and vehicle conditions during space travel. For this experiment, a real-time SHM data acquisition system was designed, developed and implemented as an addition to a flight recorder on a suborbital spaceflight. The flight recorder, provided by a commercial partner, Immortal Data Incorporated, collects flight information and distributes it to several units for improved data survivability. The aim of this flight experiment was to demonstrate real time data acquisition and storage by the flight recorder and the integration of the SHM experiment into the flight recording process. The parameters of the flight environment were acquired using various sensors in the payload. The SHM experiment used a small cantilevered beam with attached piezoelectric sensor and utilized a miniaturized Canary impedance analyzer to collect the electro-mechanical impedance signatures of the beam in the particular frequency range. The Canary unit was designed to collect electro-mechanical impedance data, store it on a SD card, process stored data in real time and communicate diagnostic data features to the flight recorder during flight. An algorithm was developed to extract a small set of diagnostic features (impedance peak amplitudes and frequencies) from raw impedance data and communicate this data set to the flight recorder. The paper discusses development of the SHM experiment for a suborbital flight, describes and validates the associated analytical model for impedance signature and provides analysis of the post flight electro-mechanical impedance data. The results obtained in the sub-orbital flight experiment indicate the utility of SHM for space vehicles.

Piezoelectric transducers (PZTs) bonded to the surface of structural members introduces electromechanical coupling. By connecting tunable circuitry across the electrodes, this coupling can be used to realize local resonances and bandgaps which alter the dynamics of the vibrating structure. The objective of this paper is to present the results of an experimental method used to acquire the phase-shifts of elastic waves propagating through these locally resonant structures. In this study, inductive (LC) shunts are used to form the local resonances and implement elastic phase-shifts. We first analyze the dispersive properties of the piezoelectric unit cell analytically, utilizing the transformation matrix method. Then the experimental tuning procedures are described in detail, and phase-shift results are demonstrated.

Acoustoelastic metamaterials are widely used as composite cores in sandwich beams. However, discussions on the application of metamaterials in face sheets have been sporadic. In this work, we parametrically explore the dynamic behaviors of sandwich beams with metamaterials as face sheets. In addition to the capability of phonon dispersion modulation using periodic face sheets, the relative positions of top and bottom face sheets can further break their spatial symmetry and thus opens more bandgaps that are not achievable with one-dimensional periodic beams. Our study has implications for the design of sandwich beams to mitigate damages induced by vibrations in various engineering and industrial applications

Liquid-constituted metamaterials are burgeoning due to their adaptive features compared with their solid counterparts. The steerable characteristics would intensively benefit the active metamaterial designs for controlling elastic guided waves. In this paper, a magnetic fluid-solid interactive metamaterial is elaborately designed to achieve the stopband switching for the manipulation of ultrasonic waves. It is revealed that fluid-structure interaction phenomenon plays the indispensable role for the bandgap formation and translation scenario. The tunable mechanism stems from the variation of the interplay circumstance arising from liquid redistribution during the magnetic field variation procedure. The stop-passing-band-opening effectiveness of the proposed metamaterial would be explicitly validated through both analytical predication and numerical simulations. Such an active design may possess enabling application potential for future highly flexible wave control, e.g., selective-tunnel waveguiding and adaptive mechanical frequency filtering.

Additive manufacturing (AM) techniques can be applied for constructing three-dimensional (3D) objects with embedded fiber Bragg grating (FBG) sensors. The goal of the paper is to compare the influence of FBG sensors embedded into AM polymeric samples on structure durability. The samples will be manufactured from two polymers with different material properties. The analyzes will be focused on strain determined by FBG sensors due to thermal loading. Additionally, the tensile strength values of the polymeric samples will be determined. The experimental investigation will be performed for the samples, without /with sensors, after manufacturing and after temperature treatment.

Additive manufacturing (AM) of metallic components allows for the fabrication of functional metallic components with complex geometries. During AM, unexpected variations in the process parameters may lead to microscale defects which compromise the product functionality. We investigate the use of phased array and guided wave ultrasonic testing as cost-effective and safe quality assurance techniques to detect typical defects generated in selective laser melting (SLM) components. In a typical SLM process, a powdered material is deposited layer by layer then fused together using a laser source to create the desired part geometry. A variation in the laser power or speed can lead to lack-of-fusion or gas porosity defects which might not be detectable during manufacturing. In this work, typical defects are generated in SLM components with thick and thin geometries by deliberately reducing the laser power below the normal values at prespecified locations of the AM samples. The density and shape of the generated defects are first identified using X-ray computed tomography and optical microscopy. A phased array ultrasonic testing probe is then used for imaging pin shaped defects in thick rectangular components. The defect images are also compared to that obtained from numerical simulations using the finite element method. Partially fused defects down to 0.25 mm diameter are detected using this approach. Additionally, a scanning laser Doppler vibrometer is used to image guided waves generated by piezoelectric transducers bonded to thin SLM components. The guided waves are used to detect powder filled cylindrical defects down to 1 mm in size.

Ultrasonic testing (UT) - is one of the most widely used nondestructive evaluation methods. A renewed interest in the ultrasonic methods is prompted by the needs of the quality control of additively manufactured products. In additive manufacturing (AM), challenges of the ultrasonic evaluation include considerations of elaborate geometry of AM parts, small size of specimens, coupling issues and complex material compositions. Application of ultrasonic testing in AM promises to improve reliability of printed parts and to reduce the number of printing cycles which, consequently, could reduce the manufacturing costs. In this contribution, the authors suggest an approach to determine the mechanical properties of the additively manufactured dog-bone specimens. Small-scale specimens complied to ASTM standard were fabricated from aluminum alloys. An ultrasonic testing methodology was developed to assess elastic properties of the specimens. Frequencies of the excitation were selected to best match geometrical constraints and address the ultrasonic attenuation. Elastic wave propagation characteristics were measured at various locations yielding spatially distributed properties. A signal analysis algorithm was developed and implemented to extract elastic properties from the ultrasonic data. Conclusions regarding applicability of the developed methodology to additive manufacturing are suggested and future work is discussed.

Lightweight complex-shaped parts are imposing themselves as inevitable in modern industry. This has induced the improvement of additive manufacturing (AM) processes and, hence, their transformation from the prototyping state into real industrial production. Such a transformation necessitates the establishment of reliable structural health monitoring (SHM) techniques for AM structures, to ensure their safe use and extend their lifetime. Research contributions over the last few decades have shown a significant potential of ultrasonic Lamb waves (LWs) for SHM of both metallic and composite structures, thanks to their favorable propagation characteristics and sensitivity to various types of structural damage. The current work investigates the propagation characteristics of LWs and examines their potential for damage imaging and localization in AM structures. To this end, pristine and damaged plates were manufactured using different materials and printing techniques/layouts. LWs of a range of typical central frequencies (50, 100, and 150 kHz) were excited at the surface of the plates using PZT and MFC transducers. Area scans were performed, using a scanning laser vibrometer, to receive the propagating waves. The influence of printing patterns on the propagation velocities of the fundamental LW modes was scrutinized, as compared to the theoretical velocities in the printing materials, assumed uniform and isotropic. Further, various damage imaging techniques were explored to detect and localize damage in the AM plates. The obtained results are considered an important step towards the application of LW-based techniques for SHM of additively manufactured structures.

Additive manufacturing (AM) techniques can be applied for the production of carbon fiber reinforced polymer (CFRP) elements with embedded fiber Bragg grating (FBG) sensors. The goal of the paper is to analyze the influence of elevated temperature on the AM CFRP samples with FBG sensors embedded into and attached to the surfaces. It allows comparing the results in relation to the locations of the sensors. The samples structures will be analyzed using an optical microscope. Their tensile strength will be determined using the tensile test. The achieved results will be compared with the results for similar samples without fiber optics.

High-intensity focused ultrasound (HIFU) has been studied for the purpose of developing a variety of medical therapies. Numerical and laboratory work has led to many clinical trials as well as first approved therapies, such as in the case of prostate cancer. However, little research has been performed to validate numerical simulations and in-vivo HIFU treatments in the presence of bones. To this end, recent advancements on visualization and optical measurements using schlieren techniques are presented in this work. In laboratory experiments, HIFU is induced in a tank filled with distilled water, and the incident waves are scattered at a bone phantom plate. Advanced filtering and computer vision techniques are adopted and their general feasibility is demonstrated for unobstructed and partially obstructed HIFU wave fields. In particular, it is shown that low-amplitude reflected wave peaks can be tracked despite their superposition with high-amplitude incident waves.

Endovascular medical devices such as catheters are composed of multiple thermal plastic components joined by butt-welding. High quality joining of components is required to ensure patient safety. The current practice for assuring quality is limited to process validation and destructive testing. An ultrasonic system for endovascular weld inspection was developed to detect faults (porosity and contamination) post welding in polyamide (Pebax-72D). Results from angled ultrasonic measurements were compared to micro-CT and found to correlate well with weld quality as a potential non destructive index for 100% in-line verification of the joining process.

Application of the Terahertz time-domain spectroscopy (THz-TDS) in the pharmaceutical industry is well-known, however the Terahertz in the medical device packaging industry for the non-destructive testing application is not yet explored in detail. Polymers such as polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) are commonly used in medical packaging and they are less absorptive in the terahertz regime. Individual-polymer-wrap packages are suitable for single use medical devices such as syringes and needles. The packaging of the medical devices usually takes place in a clean room after undergoing through the sterilization process. It is important to maintain the medical device sterile until it is used. But this type of package is easy to damage during the transportation and (or) the storage. Therefore, it is necessary to monitor the quality of the package continuously to avoid the use of non-sterile medical devices. Terahertz radiation can be used to quickly and non-destructively identify the damaged packages. Because the Terahertz can see through the polymer cover of the medical package and is changed by the humidity within the package. We show that the THz time-domain spectroscopy is able to classify the damaged and the non-damaged individual-warp medical packages. We take the advantage of various water absorption frequency windows from 300 GHz to 1000 GHz to compare the THz-TDS images of the damaged and the non-damaged packages to prove the humidity changes within the package.

Today, total knee arthroplasty (TKA) is one of the most common surgeries in the United States and is expected to grow rapidly over the next 30 years. While many TKA procedures are successful, some implants fail after primary surgery, leading to severe knee pain and costly revision surgery. As the number of TKA surgeries grows, the number of implant failures also continues to rise. In order to reduce the percentage of patients who have to undergo revision surgery, there has been a search for early detection methods to identify and treat damaged implants. Previous work has demonstrated the use of structural health monitoring techniques for detecting mechanical failure in simulated TKA implants using electromechanical impedance techniques and machine learning algorithms. However, current methods lack the accuracy necessary for medical implementation and only consider classical machine learning methods. This work aims to expand on previous methods by implementing convolutional neural networks to detect aseptic loosening and aseptic debonding in simulated TKA implants using root-mean-square-difference impedance maps. The results of the experiments show that the algorithm is able to predict damage category, damage severity, and a combination of damage category and severity with an accuracy of 90.9%, 90.9%, and 83.3%, respectively, demonstrating improvements over previous methods.

A nondestructive testing approach capable of evaluating damage accumulation in cast iron with a microstructure of nodular graphite in a matrix of iron is presented. The approach, involving non-collinear wave mixing of ultrasonic waves, is applied to a test sample damaged in a three-point fatigue damage. Results show that the non-collinear ultrasonic approach has the potential of being capable to detect and assess damage in cast iron components. Nonlinear ultrasonic results also show that damage accumulation is proportion with the maximum strain induced at the point. Results also show the importance of using frequencies as low as possible and to also minimize the travel path lengths of both the two primary waves and of the nonlinearly generated scattered wave.

In steel construction, for both plate and tubular structures, welding is commonly used for joining two or more parts together. In welded joints, defects such as cracks, pores, and slag inclusion can be present from the beginning or generated while in service. Such defects are the weak spots that can lead to structural failures. Therefore, early detection of these defects in welded joints is important. One way of detecting these defects is using ultrasonic non-destructive testing (NDT) techniques. Guided acoustic wave-based techniques have been proven to be effective for damage detection and many studies have been tried out before for damage detection in tubes. Earlier attempts mostly focused on conventional linear ultrasonic techniques. In this research, a newly developed nonlinear ultrasonic technique, called Sideband Peak Count- Index (SPC-I) technique, is carried out. For this investigation, two steel tubes are welded together and four point bending test is conducted under fatigue loading. The welded joint is continuously inspected in real time using strain gages and Lead Zirconate Titanate (PZT) transducers which is used to generate and receive the guided acoustic waves. The signal is propagated through the specimen in a single sided transmission mode setup. During the test, both strain gage values and the nonlinear ultrasonic parameter, SPC-I values are continuously monitored simultaneously. The results obtained from the nonlinear ultrasonic NDT measurements are compared with the experimental data obtained from the strain gage to check if the technique is robust and reliable enough for qualitative inspection of welds.

This paper presents a Nonlinear Electro-Mechanical Impedance Spectroscopy (NEMIS) methodology for fatigue crack monitoring. Different from the conventional Electro-Mechanical Impedance Spectroscopy (EMIS) implemented in frequency domain, the current work employs a temporal chirp-based interrogative excitation to obtain the impedance spectrum, and simultaneously captures the Contact Acoustic Nonlinearity (CAN) arising from fatigue crack interfaces. To develop an insight into the mechanism behind the chirp-based impedance method, a comparative investigation between the conventional EMIS and the chirp-based NEMIS algorithm is conducted. Numerical studies are carried out on a transitional-bilinear CAN model to illustrate the chirp-induced higher harmonics and nonlinear mixed-frequency response features. Furthermore, finite element simulations are conducted to demonstrate the feasibility of the chirp-based NEMIS. Finally, experimental validation of the NEMIS method is performed. The chirp-based impedance spectra are verified against results from the impedance analyzer. Fatigue cracks are nucleated and grown on the MTS testing machine with cyclic loadings. Higher harmonics and wave modulation features can be successfully captured to manifest the existence of the fatigue crack. Quantification on the severity of the crack is conducted using the nonlinear damage index. The paper finishes with summary, concluding remarks, and suggestions for future work.

Nowadays Fiber Reinforced Cementitious Matrix (FRCM) composites are considered as a primary strengthening technique for reinforced concrete and masonry constructions, especially for historic buildings. Historic structures exhibit a pronounced seismic vulnerability, entailing the risk of losing important parts of the world's cultural heritage. A vast literature is available on issues like the interactions, the adhesion, and the delamination strength between FRCM composites and masonry. However, much fewer studies concern the detection of possible defects in the adhesion between FRCM and masonry, formed during the application of the reinforcement or during the service life of the construction, for example, from exceptional loads like earthquake, fire, etc. These defects can strongly undermine the effectiveness of the strengthening intervention, and thus the structural safety of the reinforced construction. Here, an innovative nonlinear ultrasonic technique called Side-band Peak Count (SPC) is proposed for detecting defects in the adhesion between FRCM composite layers and masonry substrates. The SPC technique reprocesses the results of the ultrasonic guided wave tests by relating the level of the non-linearity of the ultrasonic response due to the damage, to the appearance of additional secondary components in the spectrum of the received signal. Experiments are conducted on masonry tuff specimens reinforced with FRCM mortars and basalt fibers grid embedment. Specimens with known artificial defects are tested. Defects are fabricated both at the FRCM-tuff interface and within the FRCM layer, i.e., at the interface mortar-reinforcement fiber grid. The effectiveness of the proposed approach is investigated and discussed.

This paper introduces an enhanced ultrasonic sonar-based ranging technique developed by the Experimental Mechanics, NDE & SHM Laboratory at UC San Diego to estimate the deflections of railroad ties. The deflection profile of the ties can be subsequently inspected to evaluate their performance and prevent unwanted events, like train derailments. The proposed sensing layout is comprised of an array of air-coupled capacitive transducers to perform pulse-echo ultrasonic tests in multiple points along the tie, and a high frame-rate camera to capture images of the objects probed by the array. A machine learning-based image processing technique is developed to classify the tie/ballast images based on the texture signature of the visible objects in the camera’s field of view. Next, the relative deflection profile of the ties is reconstructed by tracking the Time of Flight (ToF) of the received waveforms at the points flagged as a tie. A series of field tests was carried out at the Rail Defect Testing Facility of UC San Diego as well as a BNSF yard in San Diego, CA, by mounting the sensing prototype on a car moving at walking speed. The obtained results confirm the potential of the proposed airborne ranging technique for in-motion measurement of the deflections of railroad ties.

Three-dimensional digital image correlation (3D-DIC) has become a strong alternative to traditional contact-based techniques for structural health monitoring. 3D-DIC can extract the full-field displacement of a structure from a set of synchronized stereo images. Before performing 3D-DIC, a complex calibration process must be completed to obtain the stereovision system’s extrinsic parameters (i.e., cameras’ distance and orientation). The time required for the calibration depends on the dimensions of the targeted structure. For example, for large-scale structures, the calibration may take several hours. Furthermore, every time the cameras’ position changes, a new calibration is required to recalculate the extrinsic parameters. The approach proposed in this research allows determining the 3D-DIC extrinsic parameters using the data measured with commercially available sensors. The system utilizes three Inertial Measurement Units with a laser distance meter to compute the relative orientation and distance between the cameras. In this paper, an evaluation of the sensitivity of the newly developed sensor suite is provided by assessing the errors in the measurement of the extrinsic parameters. Analytical simulations performed on a 7.5 x 5.7 m field of view using the data retrieved from the sensors show that the proposed approach provides an accuracy of ~10-6 m and a promising way to reduce the complexity of 3D-DIC calibration.

An automatic lightweight feature detection algorithm is developed to perform real-time structural health monitoring (SHM) of large structures. The algorithm works on the specified region of interest (ROI) and applies canny edge detection with k-means clustering for identifying the displaced pixel location in an image sequence. The location of detected edges (white pixels) in the selected ROI is first validated and then given as input to the k-means clustering algorithm for centroid calculation. The pixel movement tracing method is validated by image simulation, indoor digital micrometer experiment and then an outdoor field experiment on wind turbine. The image simulation experiment was performed to generate sample data and ground truth values. In this experiment, the algorithm was able to detect the defined pixel translations. With this validation, other two experiments were conducted. The indoor experiment was implemented for experimental verification where it successfully identifies the moving bar’s 20mm displacement. Likewise, it also accurately measures the natural frequency of the tower of a utility-scale wind turbine. Hence, the algorithm was built on parallel processing with multi-ROI selection to optimize the space and time complexity for real-time vibration analysis. The present study proclaims that the developed algorithm can be used to perform real-time SHM of large-scale structures.

In many structures, most of the destruction and damage are caused by torsion. At the same time, in many cases, the torsion effect plays a controlling role in architectural design. Therefore, the monitoring of structural torsion has become more and more important, but there is less research at present. Based on machine vision technology, this paper proposes a monitoring method for the torsional deformation of building structure. Firstly, the theoretical basis of the scheme and the composition of the monitoring system are introduced. Then the feasibility of the scheme is proved through free rotation and elastic torsion experiments. It is verified that the scheme has the advantages of high precision and low cost, and can realize the real-time monitoring of structural torsional deformation, It can avoid the large deformation of the structure due to torsion, affect the use and even damage, and make a certain contribution to the safety evaluation and health evaluation of the structure.

Strain measuring sensors have been increasingly employed in strain modal analysis for structural health monitoring. Fiber bragg grafts, piezoelectric sensors, and conventional strain gauges are readily used strain gauges. Conventional strain gauges lack the dynamic strain measuring capability and fiber bragg sensors prove to be uneconomical for large structures. Piezoelectric or piezo sensors, though very efficient for dynamic strain monitoring and cost-effective, needs to be bonded on the structure like other strain gauges. This study investigates a non-bonded configuration of ceramic-based piezo sensors for experimental modal analysis (EMA). Artificial damage was created by cutting out a hole in the center of the plate. Non bonded piezo sensors (NBPS) and an accelerometer (ACC) are used to measure the dynamic response of a steel plate specimen. Single input single output (SISO) technique was adopted for the excitation and measurements from the specimen. Vibration frequencies and mode shape from all three sensors are compared in pristine and damaged state. NBPS was found to be as effective as accelerometers for damage detection and, being strain oriented were more sensitive towards damage than accelerometer. No observable change was observed in the vibrational frequencies after damage. Modal assurance criteria was adopted to quantify the change in the mode shapes due to the damage. Strain based displacement mode shapes from NBPS were more sensitive towards damage than displacement mode shapes from the accelerometer.

The electromechanical impedance (EMI) method is a high frequency based local structural damage detection technique. The recent advances in sensor network study have led to study of direct-coupled mechanical impedance (DCMI) signals based damage indices using a modified probability weight function for damage imaging in the structure. Further, a novice fused data which combines the information of sensor resistance (R) and conductance (G) is also studied in robust damage detection in structural health monitoring (SHM) due to its capabilities of extracting multiple information. RMSD is the popular damage metrics in describing the behaviour of signal in damage quantification in the frequency domain based EMI method. This work implements a modified probability weight function using the optimal radius of sensing region for the large and small steps/intervals of radius. Further, a comparative damage imaging study done among F, DCMI and filtered detrend (FD) data based RMSD damage index. The proposed methodology is implemented for the glass fiber reinforced polymer (GFRP) composite material with impact damage.

From parachutes to orbiting systems, webbing structures play a crucial role on the safety of civil and aerospace systems. The mechanical strength of a webbing can be severely undermined by the ultra-violet (UV) component of sunlight. In this work, we introduce a photochromic webbing structure that exhibits color variation upon UV irradiation. The photochromic webbing demonstrates sensitivity under a wide range of realistic field settings, including extended UV exposures and cyclic UV irradiations. Our analysis could help set the stage for the integration of photochromic webbings in aerospace systems.

In this work, a statistical damage diagnosis scheme using stochastic time series models in the context of acousto ultrasound guided wave-based structural health monitoring (SHM) has been proposed and its performance has been assessed experimentally. Three different methods of damage diagnosis were employed, namely: i) standard autoregressive (AR)-based method, ii) singular value decomposition (SVD)-based method, and iii) principal component analysis-based method. For estimating the AR model parameters, the asymptotically efficient weighted least squares (WLS) method was used. The estimated model parameters were then used to estimate a statistical characteristic quantity that follows a chi-squared distribution. A statistical threshold derived from the chi-squared distribution that depends on the number of degrees of freedom was used instead of a user-defined margin to facilitate automatic damage detection. The method’s effectiveness is assessed via multiple experiments under various damage scenarios using damage intersecting as well as non-intersecting paths.

The purpose of health monitoring of building materials is to localize the defect during its formation to give early warnings for avoiding catastrophic failures. Here, an acoustic source localization technique for building materials is proposed using the time difference of arrival at six sensors without knowing the acoustic wave speed in the material. The proposed technique does not require solving a system of nonlinear equations; hence, greatly reduces the complexity of calculation. Finite element models of different building materials were created to verify the proposed defect localization technique. The results of numerical simulation prove the reliability of the proposed technique.

Small sample size (e.g. 6-30) poses risk in results of probability of detection (POD) analysis using tolerance intervals. This method is also called as the limited sample or LS POD. The analysis is performed either during NDE procedure qualification or for assessment of reliability of an NDE procedure. The risk is primarily due to sampling error. Smaller samples are not likely to be random to the population or representative of the population. The small samples are likely to be biased. Biased samples have smaller standard deviation compared to the population. POD analysis with small biased sample can lead to overestimation of POD. Many sampling schemes are available in statistics to mitigate sampling risk. Primary objective of POD analysis is to determine a decision threshold from signal response measurements of a sample such that it is less than or equal to population decision threshold for 90% POD. Sampling error implies that this NDE reliability condition is violated. One of sampling types is called a representative sample. Representative samples reduce variance in POD estimates but also reduce magnitude of the error. Sampling sensitivity analysis for some sampling types is performed here using repetitive random sampling or Monte Carlo method. Six sampling types are considered for comparison. Some of the sampling types are similar to drawing a representative sample. LS POD model assumes random sampling. Therefore, random sampling is used as a basis for comparison with each sampling type. The sampling types used in the analysis are, A. Nominal and worst-case sampling, B. Worst-case sampling, C. Nominal case sampling, D. Random sampling, E. Random target, and sub-target sampling. F. Nominal target and sub-target sampling. Results of Monte Carlo simulation indicate that type F sampling can mitigate sampling risk and is also more practical to implement. Type A sampling may also mitigate the sampling risk, but it may be less practical to implement.

With the increase in population, surface transportation has significantly increased, highlighting the need to maintain reliable and safe civil infrastructures. Bridges can be considered the most critical element in transportation infrastructure. Accordingly, they require periodic inspection to prevent any failure caused by aging and environmental impacts. Over the years, visual inspection has been practiced the most due to its simplicity. However, detection of small and internal cracks and defects requires more advanced techniques. To address this, various non-destructive testing (NDT) methods have been developed and implemented in recent decades. With the advance of technology, opportunities for developing more advanced and effective NDT methods have been created. To achieve the development of such advanced techniques, the knowledge of available methods and their future trends becomes necessary. This paper gathers in one place all the relevant information regarding existing non-destructive testing for steel bridges. Furthermore, the future direction and recent innovations in this field, including the application of robots, sensors, and drones for fast and efficient evaluation of steel bridges are discussed. The parameters for selecting the most appropriate NDT method for specific cases are explored. The methodology, and pros and cons of each technique are also presented. It is expected that the results of this study pave the way for development of new methods and improvement of currently practiced NDT techniques for steel bridges.

Considering the fact that the United States has more than 600,000 highway bridges, 46.4% of which are rated fair and 7.6% are rated poor, the installation of reliable bridge health monitoring systems is strategically essential in this country to reduce repair and rehabilitation costs as well as to prevent failures. This review paper presents a synthesis of the scientific literature on the Structural Health Monitoring (SHM) systems installed in some US bridges over the last 20 years. The goal of this paper is to provide a view of the recent and current state-of-the-art in bridge health monitoring systems, as well as to conclude a "general paradigm" that is shared by many real-world structures. The review, which was carried out through a thorough search of peer-reviewed documents available in the scientific literature, discusses a bunch of monitored bridges in the US in terms of the utilized instrumentation, monitoring scope, and the significant outcomes. Finally, a brief overview of a bridge health monitoring program in the state of Pennsylvania will be discussed.

To mitigate infrastructure deterioration, structural health monitoring (SHM) has been employed for more than half a century, gaining momentum with recent advancements in information, communication, and sensing technologies. In particular, wireless sensor networks have gradually been incorporated into SHM, offering new opportunities towards enhanced flexibility and scalability, as compared to cable-based SHM systems. However, wireless sensor nodes are installed at fixed locations and, causing high installation costs, need to be employed at high density to reliably monitor large infrastructure. This feasibility study proposes quadruped robots for wireless SHM of civil infrastructure, leveraging advantages regarding cost-efficiency and maneuverability. Aiming at cost-efficiency, the quadruped robots are implemented using off-the-shelf components. The robots are equipped with sensors to collect acceleration data relevant to SHM of civil infrastructure, with cameras for navigation, and with embedded algorithms, facilitating autonomous data processing, analysis, synchronization, and communication. The accuracy of the quadruped robots is validated in laboratory tests on a shear-frame structure by comparing the SHM data collected and analyzed by the quadruped robots with SHM data collected by a high-precision cable-based SHM system. Furthermore, the maneuverability and efficiency of the quadruped robots is demonstrated through field tests conducted on a road bridge by comparing the sensor information collected by the robots with the respective sensor information collected by a comprehensive benchmark SHM system. The results confirm that the quadruped robots, as compared to stationary wireless sensor nodes, require a smaller number of nodes to achieve the same sensor information and, as compared to wheeled robots, offer better maneuverability, as critical parts of civil infrastructure may be hard to reach. In summary, this feasibility study represents a first step towards robotic fleets employed for autonomous SHM.

At present, the buildings under damage assessment are actually with minor or serious damages, so the damage indices evaluated accordingly may not be adequate. To solve the problem, the experimental method is used to simulate different states of damage, followed by identifying the parameters of the associated damaged structure. Then the damage indices describing the damage degree can be computed using the identified parameters, and the threshold value of different damage state can be reached. In this paper, the technique of system identification is used to analyze the Benchmark Model D. The model is three-dimensional steel structure without diagonal bracings, which is 3m in the longitudinal (x axis) direction and 2m in the transverse (y axis) direction. A series of experiments have been conducted on the structure which can be divided into three stages, including the first-stage linear test, the second-stage linear test and the third-stage nonlinear test. The first-stage linear test is a series of linear shaking table tests; the second-stage linear test is also a series of linear shaking table tests, but the bottom of the two columns of the first floor were tapered to simulate the weak section; the model of the third-stage non-linear test is the same model as that of the second-stage linear test, and a series of non-linear shaking table tests with earthquake of high intensity were conducted. The analysis of this paper is first based on the identified results of first-stage linear test, and then the degree of damage is determined by the identified results of the second-stage linear test. Finally, a comparison is made between the first-stage linear test and the third-stage nonlinear test to determine the degree of damage and the location or floor of damage.

This study proposes a noninvasive method to determine the axial load in continuous welded rails using numeric models and field test data. A general finite element model under varying boundary conditions and axial stresses was formulated to predict the natural frequencies of vibration. The model was then validated experimentally by testing a real track in the field. During the experiment, the rail was subjected to the impact of an instrumented hammer and the triggered vibrations were recorded with conventional accelerometers. The accelerations were processed to extract the frequency of the excited modes. Using both empirical data and numeric predictions a loss-function was formulated to estimate the neutral temperature of the rail. Obtained results showed great agreement with measurements conducted by a third independent party that used an approach based on strain gages.

In order to analyze the static and dynamic characteristics of an under-supported truss bridge, the laboratory truss bridge model is established, and the displacement and strain data are measured by the sensors laid on the bridge model. Then two different finite element model software are used to create the bridge element by nodes, and two finite element models of the under-supported truss bridge are established respectively. The static analysis of the model is carried out to obtain the internal force and deformation of the bridge model under different working conditions, and the stress of the bridge is understood. After comparing the finite element model with the laboratory model data, the static characteristics of the truss bridge are obtained, and the reliability of the finite element models simulated by different software is analyzed and verified. Finally, the dynamic characteristics of the model are analyzed by finite element analysis method.

Temperature has significant effects on the mechanical properties of bridge. This paper investigated the temperature effects on dynamic properties of a long-span suspension bridge with steel box girder. The correlation between the air temperature and the natural frequencies of the bridge is analyzed based on field measurements. The finite element (FE) models for thermal analysis and structural analysis are developed separately using ANSYS software. The structural temperatures of the bridge’s components are calculated. The calculated temperatures are applied to the structural FE model, and then the bridge’s natural frequencies are obtained using model analysis. The calculated structural temperatures and natural frequencies are compared with the field measurements to verify the reliability and accuracy of the numerical analysis method. The calculated temperatures have good agreement with the measurements. The value and variation of the low order natural frequencies of the suspension bridge can be properly simulated with acceptable reliability and accuracy.

Real-time unsupervised condition monitoring of civil infrastructures has gained a great deal of attention during the past decade. This practice has been challenged by several factors such as the lack of a robust feature extraction strategy, scarcity of baseline data collected from the intact structure, the lack of information from missing data, and the hardship of specifying a dynamic threshold strategy. Thanks to the advances in deep learning techniques, the condition monitoring practice of civil infrastructures benefits largely from the strength of deep learning for feature extraction, amending missing information, and developing dynamic threshold settings. This survey studies some of the recent advances in real-time unsupervised condition monitoring of civil infrastructures. As such, it has been noted that the variational auto-encoder and generative adversarial networks are two main deep learning models that can address the aforementioned challenges. Therefore, a possible future path for research in this field can be towards mixing these deep learning models to address all the challenges of real-time unsupervised condition monitoring of civil infrastructures at once.