This PDF file contains the front matter associated with SPIE Proceedings Volume 12047, including the Title Page, Copyright information, Table of Contents, and Conference Committee listings.

The aim of this work is the study of the adhesion integrity of metallic Single Lap Joints (SLJs) through the assessment of the MUL² CODE, software developed by the MUL² Research Group - Department of Mechanical and Aerospace Engineering of Politecnico di Torino. The MUL² CODE is implemented through the Carrera Unified Formulation (CUF) for 2D structures based on Hierarchical Legendre Expansion (HLE) polynomials. An efficient method for the Structural Health Monitoring (SHM) of bonded joints is simulated and verified by CUF approach, in order to reduce the computational cost of analyses: by using transient excitations (toneburst signals), the structural health of damaged SLJ can be numerically evaluated. The interaction mechanism between the waves traveling through the investigated specimens is numerically modeled with a simple Finite Elements (FE) model and it is solved via MUL² CODE and commercial software Ansys Workbench, respectively. Experimental campaigns data are compared with CUF and Ansys results demonstrating the consistence of the MUL² formulation that is computationally simpler, but very efficient for the joint analysis. The presented and discussed CUF application is able to quantify with a high accuracy the debonding extension in the damaged SLJ, simply tuning the excitation frequency of the SHM technique.

In the last decades, fibre-reinforced composites have attracted outstanding interest especially for weigh-sensitive applications as well as for their specific mechanical properties as strength and stiffness. However, with the awareness that, like most materials, fibre-reinforced composites also exhibit the so-called strength versus toughness dilemma and, over the years, many different strategies have been proposed to improve their damage resistance. One of the most accounted strategies consider the use of at least two different reinforcing fibers distributed in the same matrix with typical configurations best known as interlayer, intralayer or intrayarn, depending on whether the fibres of different nature are arranged on as many laminate of the composite, in the same lamina or side-by-side in the strand making up the reinforcing phase. In this frame, the research was focused on polypropylene-based composite laminates manufactured by film-stacking and hot-pressing steps and reinforced by a commercial hybrid fabric obtained by weaving flax and basalt fibres. Specimens, consisting of 6 plies and 3.0 mm laminate thickness, were cut from the prepared plates along the direction of both flax and basalt fibres and subjected to Quasi Static Indentation (QSI) tests. The tested specimens were investigated by combining Optical microscopy (OP) and Electronic Speckle Pattern Interferometry (ESPI) to analyze the complex damage which can be generated as a result of stress on this kind of fibre-reinforced composites.

Custom made electrostatic transducers were used for ultrasonic investigations of damaged CFRP. These investigations resulted in identification of non-linear ultrasonic responses due to the presence of damage. The broadband nature of electrostatic transducers is essential for this work to scan a wide range of frequencies to identify non-linear responses. These non-linear response frequencies were used with an electrostatic transducer to induce thermosonic heating at the damage which was measured with an IR camera. The use of non-linear response frequencies reduces the power required to produce thermosonic heating. Travelling guided waves (Lamb waves) allow excitation of the damage at a distance from the excitation source allowing large samples to be studied. This provides a completely non-contact measurement for NDE which shows promise for future composite inspection systems.

Aircrafts and wind turbine blades often get hit by lightning strikes due to their operating locations. With increasing incidents of lightning flashes every year due to global warming, damage assessment of CFRP structures after lightning strike to aircrafts and wind turbine blades is getting increasingly important. Many researchers are involved in designing better and more resilient lighting strike protection materials and often utilize non-destructuve evaluation (NDE) methods such as ultrasonic testing (UT), high-speed digital videograpgy, and high-speed IR thermography. In particular, UT is widely used due to its cost-effectiveness compared to other methods. Using UT imaging can obtain any potential damaged locations caused by lightning strike through the thickness of the composite specimens. Traditional UT and visual inspections only display the damage in units area, when in fact the UT data is volumetric. This study optimized the UT data to measure and display the volumetric damage after artificial lightning strike and is compared to the standardized visual inspection.

High level nuclear spent fuel canisters have been designed and used for nuclear waste storage since the 1950’s. Stress corrosion cracking (SCC) has shown to occur under certain corrosive chemical conditions when the residual stress was not relived in a welded plate. Typical SCC would eventually cause catastrophic failure of structures. In the case of spent nuclear canisters, the radioactive materials may leak through the cracks if they penetrate the tank wall. Early detection of SCC is crucial, followed with appropriate mitigation methods. Various mitigation methods have been funded and explored for the nuclear facilities. Among them, engineered composite patch repairing technique that was originally developed and adopted for aerospace aluminum structures has been proposed as one of the solutions. We first explored the fundamentals of composite patches for crack repairing through literature study, followed with the development of a complete procedure from composite material selection, adhesive selection, to surface preprocessing. To understand the effect of the patch performance for crack repairing, tensile test with stainless steel coupon samples will be performed. A Lamb wave based noncontact evaluation was performed beforehand to identify the precrack length in the coupon samples and to guide the dimension design of the patch. After that, the surface processing of both the composite patch and the steel plate as well as the patch installation techniques were explored. Finally, samples with and without the mitigation composite patches will be tested under tensile loading in order to understand the benefits of composite patches and meanwhile to identify potential improvements.

This work demonstrated the utilization of non-destructive Digital Imaging Correlation (DIC) method to characterize the elastic, plastic, and damage features during the Mode I intra-laminar fracturing process of self-reinforced thermoplastic composites by using a self-reinforced polypropylene (PP) composite as an example. The DIC results clearly showed the development of huge plastic zone (PZ) and non-negligible Fracture Process Zone (FPZ) in front of the notch tip during the fracturing process, and the geometries and sizes of the foregoing zones at the peak load were further quantified. Such an interesting fracturing behavior of self-reinforced thermoplastic composites is way different from brittle materials (e.g., glass, acrylic, etc.), ductile materials (e.g., aluminum, steel, etc.), and even quasi-brittle materials (e.g., concrete, nanoparticle-reinforced composites, tough ceramics, wood, carbon/glass fiber-reinforced polymers, etc.). Thus, understanding the elastic, plastic, and damage features is the first step before better characterizing the material fracture properties of self-reinforced thermoplastic composites through new analytical methods and computational modeling. These efforts are utmost of importance for wide applications of self-reinforced thermoplastic composites in various engineering fields in the future.

The use of fiber reinforced polymer (FRP) materials has been on the rise in bridge construction industry. They have been mostly utilized for strengthening purposes, especially as externally bonded reinforcement for columns, slabs, and beams. Their lightweight, flexibility and ease of installation, and higher strength offer an ideal solution for increasing the axial, bending, and shear resistance of existing concrete elements, as well as restoration and retrofitting of damaged members. FRP bars have also been used as internal reinforcement for concrete structural elements, providing corrosion-resistant alternatives to conventional steel reinforcements. Although the application of FRP for concrete members has offered many advantages, there have been issues and concerns associated with their long-term performance including their debonding from a concrete surface or within a concrete element. Accordingly, despite their durability, concrete elements strengthened/reinforced with FRP materials need to be inspected periodically to detect potential issues and hence prevent any premature failures. This study investigates Non-Destructive Testing (NDT) methods applicable to inspection of in-service FRP reinforced/strengthened concrete (FRP-RSC) bridge elements. Accordingly, this study first introduces damages and anomalies attributed to FRP reinforced/strengthened concrete bridge elements, and causes are discussed. The study includes a review of some promising NDT methods for the detection of these damages. The results of this study are expected to provide the inspection community more clarity in the application of NDT to FRP reinforced/strengthened concrete.

Adhesive joints have been an effective alternative to conventional mechanical fasteners for joining similar and dissimilar materials in the aerospace industry. Adhesive joints have various advantages, including uniform stress distribution, lower weight, and design flexibility, but quality issues and possible defects in these joints have limited wider use. In this study, contaminants mixed into the epoxy-adhesive, which cause cohesive failure, were investigated. In the manufacturing process there can be various contaminants, such as release agents, oils, and moisture. Since release agents are essential materials during the manufacturing process, these were used in this study. A nonlinear ultrasonic technique was employed to evaluate the micro-scale defects in the adhesive due to contaminants. The experiments measured the nonlinearity parameter, with varying the contamination level, at 0, 0.5, 1.0, and 1.5% of the total weight of the epoxy mixture. The nonlinearity parameter exhibited higher sensitivity than the sound velocity, which is a conventional linear ultrasonic parameter, for the differentiation of the contamination levels in the adhesive. Furthermore, differential scanning calorimetry (DSC) and Rockwell hardness testing were conducted to monitor changes in chemical and mechanical properties respectively, with varying degrees of the contamination. It is shown that using the correlation between the nonlinearity parameter and chemical, and mechanical properties of the adhesive, there is the basis for an advanced inspection system, which has potential to improve the detectability of micro-scale defects in adhesively jointed structures.

A commercially-available air coupled piezoelectric array was used to induce thermosonic heating in damaged CFRP. Themosonic heating of the damage has been observed with an IR camera. The intensity of the thermosonic heating decreased with the increased distance of the ultrasonic excitation from the damage, as would be expected with energy dissipation into the CFRP. The use of an array allowed scanning of the focal point across a sample to locate and image areas of damage, without moving either the array or the camera. This scanning capability could increase the speed at which composites can be inspected, reducing the current laborious contact transducer methods.

Radiographic imaging represents a vital capability within non-destructive assessment, quality control and fundamental science. This study focuses on Gd₂O₂S:Tb³⁺ based scintillating composites incorporated into pixelated-metallic aperture screens, attached to amorphous silicon-based flat panels. Performance metrics are explored through coupled MCNP6- FRED simulations; here, scintillation light transport is investigated as a function of pixelated screen geometry and optical characteristics. For the first time, we demonstrate image acquisition with a 100 micron thick pixelated metallic aperture screen. The results demonstrate promising improvements to x-ray interaction rates while maintaining image quality.

While developing a novel digital image correlation (DIC)-based NDT method, one has to integrate an automatic and robust feature detection method with the DIC technique. Several studies in the past employed various algorithms such as SIFT, SURF and BRISK with DIC for feature detection and correlation initiation purposes. However, our study shows that the performance of available algorithms is subjected to the image of interest from a particular field experiment. Therefore, the selection of the feature detection algorithms is an essential step towards accurate and efficient processing. We have developed a methodology that applies various feature detection algorithms (namely SIFT, SURF, BRISK, ORB and KAZE) and selects the most accurate, efficient and repeatable algorithm for detecting unique natural patterns. Moreover, the methodology is integrated with an in-house 3D-DIC program to identify as well as correlate natural patterns to obtain in-plane and out-of-plane displacements of large structures. The combined methodology is successfully applied and verified by performing field experiments with a light tower of 10m height and a utility-scale wind turbine. It is observed that the developed methodology is robust enough to detect natural patterns accurately and efficiently. It has also been demonstrated that the technique is successful with the determination of 3D displacements and natural frequencies of the large structures.

NASA is developing new vehicles for space transportation. Many of these spacecraft are targeted for long-term use, which offers challenges for inspection. In orbit or on the Moon or Mars, the use of traditional NDE is prohibitive because of location and inaccessibility, and infrequent inspection can lead to conservative, high-weight designs. Structural health monitoring (SHM) can help overcome inspection difficulties and has shown good results on small structures. However, transition to large vehicle structures has been slow. Some reasons for the slow adoption are difficulties with large sensor arrays, timely analysis of large data sets, and overall weight of the system. In order to realize the benefits of SHM, there’s a need to reduce the number of sensors and minimize data acquisition processes while maintaining the ability to accurately detect, locate, and characterize damage. Compressive Sensing (CS) has been shown to greatly reduce data acquisition/processing burdens by providing accurate signal recovery from far fewer samples than conventionally needed. This paper presents the development of data analysis software and hardware to detect damage in large vehicle structures using CS at two stages in the data acquisition/analysis process: (1) temporally undersampled sensor signals from (2) spatially undersampled sensor arrays, resulting in faster data acquisition and reduced data sets without any loss in damage detection ability. The technology reduces data acquisition requirements (energy consumption, number of sensors, data collection and storage, and total system weight) of NDE/SHM systems without compromising damage detection accuracy or probability of detection.

This work is centered on the development of a 3D Finite Difference (FD) model to simulate Single Lap Joints (SLJs) excited by Lamb waves. An approach based on transient waves is proposed for assessing debonding area in aluminum SLJ typically used in aerospace industry. This approach is based on the interference of elastic waves generated by Piezo Wafer Active Sensors (PWAS) attached to an adhesively bonded thin joint and travelling through the adhesion area: destructive interference conditions are promoted when the adhesive is partially debonded and they reveal a specific damage length. The mathematical model is the Cauchy-Navier equation of linear elasticity with variable Lam´e moduli, solved in a regular and relatively simple domain. The main advantage of the proposed 3D numerical model is the mathematical ability to easily reproduce the presence of a damage (debonding) as a discontinuity in velocity values. Numerical results and experimental data are presented in order to validate the obtained novel reduced-order FD 3D model that appears leaner, cleaner and more simplified than FE one. Moreover, the simplicity and low computational cost of the proposed method make it particularly interesting for industrial applications (i.e., online Structural Health Monitoring on working structures).

Fiber-reinforced polymer composites (FRPCs) are valid alternatives to metals, especially in the aerospace industry, for their superior strength-to-weight ratio. FRPCs are generally manufactured by stacking layers of fibers and infusing them with epoxy-based adhesives to result in laminates. Such layer-by-layer structure often ensues a poor interlaminar property of the composites. Additionally, FRPCs can sustain complex damage modes because of their inherent anisotropic characteristics. For example, delamination can occur due to low-velocity impact at a subsurface level, which can result in premature catastrophic failures if undetected. Thus, structural monitoring is crucial to identify such damage. Point-based sensors (e.g., strain gauges, accelerometers, among many) can be embedded in the FRPCs for structural monitoring. However, their electrical power demand often embroils their usage. Therefore, the main objective of this research is to design and implement multifunctional composites that can simultaneously perform passive sensing and energy harvesting while exhibiting better mechanical performance. Previous research efforts have demonstrated that a continuous feedthrough deposition of functional materials on fiber surfaces simultaneously enhanced the mechanical and sensing properties of the FRPCs. This study explores a similar approach to encode passive sensing and energy harvesting properties in the FRPCs by integrating ferroelectric microparticles on the fiber surfaces. Upon ensuring their superior interlaminar shear strength, sensing and energy harvesting properties were characterized through experimental studies. The outcome is a multifunctional composite fabricated by coating the fibers with functional microparticles through a high throughput, scalable, and low-cost approach that enables passive sensing, energy harvesting, and improved mechanical performance.

Advanced functional materials should be developed to address the increasing demand of intelligent systems capable of sensing and self-monitoring. Among different material systems, soft matter composites have attracted much attention during the past several years. Because of their mechanical compliance, they can be used in variety of applications including human computer interactions, wearable biosensors, and internet of things (IoT). Liquid metal polymer composites are a promising class of soft multifunctional materials. Micro and nanoscale droplets of gallium alloys that are liquid at room temperature serve as functional units in these composites. Eutectic gallium indium (EGaIn) and eutectic gallium indium tin (known as Galinstan) are the common non-toxic liquid metal (LM) with high electrical and thermal conductivity. Here, we present a micromechanics model to predict the effective elastic and functional behaviors of EGaInpolymer composites. This Eshelby inclusion-based model has higher accuracy because it accounts for the solid gallium oxide layer that forms around the liquid inclusions. Although the influence of this oxide interphase can be neglected for composites with large diameters (<30 microns), it has a significant effect on elasticity of LM nanocomposites. In addition to studying the core-shell structure of LM inclusions, the formulated model is used for composites with different filler volume fractions and polymer matrices. Moreover, the overall dielectric properties and thermal conductivity of the EGaInpolymer composites are predicted using this model. The modeling results show excellent agreement with finite element analysis and available experimental results. Lastly, we discussed the potential application of LM composites in emerging intelligent systems.

Carbon nanotube (CNT) technology has long been touted as a wonder-material that will solve many materials problemsstronger than steel, more conductive than copper, and all with a density equivalent to carbon black! Due to historical limitations on achieving superb CNT dispersion at scale, adoption of carbon nanotubes has been minimal. CNT technology has steadily been progressing and is now commercially being adopted in various applications at large scale- especially those relating to electric vehicles (EVs). Molecular Rebar Design has pioneered the use of discrete, individual, functional nanotubes at scale, and produces a dispersible functional nanotube called Molecular Rebar (MR) that is being designed for use in lithium batteries and new tires. Most transportation modes use tires, and Molecular Rebar’s individual nature helps to improve each aspect of the ‘Magic Triangle’ of grip, wear, and fuel economy for tires without detrimentally affecting other properties. This allows for changing the rules in tire design. Improving road-tire contact surfaces in a variety of vehicles will further allow innovations within the drivetrain system. As one example, tires with MR can reduce downtime thru improved lifetime in off-the-road tires, and as another example MR enhanced tires will improve wear resistance and fuel economy in EVs, lowering barriers to adoption (range anxiety and higher tire costs). A review of current trends in the marketplace, data produced by MRD, and the resultant value propositions will identify how and why discrete carbon nanotubes will become the reinforcing filler of choice for tires, further improving vehicular transportation.

A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.

One of the current challenges in the guided wave ultrasonic testing of large-scale structures is attenuation. In this study, elastic wave propagation characteristics of a composite plate that is sampled from aerospace industry made of an orthotropic material are augmented with a gradient-index phononic crystal lens. The mechanical lens is made of cylindrical stubs periodically arranged on the plate and is investigated with unit cell simulations involving Bloch-Floquet periodic boundary conditions. The finite element models of the stubbed unit cells are built to generate dispersion curves for the cases with stubs made of structural steel, aluminum-1100, brass, and acrylic plastic. The final choice of material for the stubs is determined to be acrylic plastic due to the highest variation in phase velocities and highest frequency range of the resonance band gap. The changes in the phase velocity of the symmetric Lamb wave mode S0 are simulated to have the highest sensitivity to the changes in the stub heights, making it the target of the lens design. Refractive indices are computed based on the phase velocities. The dimensions of the stubs are tailored by adjusting their heights according to the hyperbolic secant profile to form the effective lens resulting in wave focusing. Multiphysics simulations in the time domain are performed to verify that the ultrasonic guided wave energy is focused on the focal point of the lens, where an amplification of the wave amplitude is expected to happen.

This work aims to obtain a quantitative estimate of stiffness reduction in cross-ply laminates due to transverse cracks in 90-degree plies. The received Lamb wave signal in the pitch-catch transmission mode is expressed in terms of stress wave factors (SWFs) employing certain functions of the power spectral density of the received wave. The stiffness reduction in the laminate is deduced by a correlation with the stress wave factors (SWFs). The use of SWFs for damage quantification has been investigated in a previous experimental study. In the current work, cross-ply laminates of different configurations are modeled in Abaqus. The stiffness degradation due to transverse cracks is represented by a homogenized reduction of transverse Young’s modulus (E2) and in-plane shear modulus (G23) in the 90-degree plies of the laminate. When a Lamb wave propagates through a region of reduced elastic properties, reductions in the amplitude and speed of the propagating wave are expected due to the effective stiffness loss in the direction of wave propagation. One of the measured factors, SWF1, is found to capture this loss and shows a linear correlation with the laminate stiffness reduction. It is indicated that a quantitative assessment of the Lamb wave propagation in composites with damage is possible with a statistical measure of the received signal.

Self-healing cementitious composites provide a solution to the application of costly, manual repairs of construction elements. Additionally, as the healing mechanism is inherently present within the cementitious mixture, issues concerning the repair of structures with limited accessibility are omitted. However, the assessment of the regained mechanical performance as well as the monitoring of the evolution of the healed properties requires destructive tests, which cannot be applied in situ. For this reason, a non-destructive test set-up based on ultrasonic wave transmission was established. Thanks to the sensitivity of ultrasonic waves to the material properties, significant changes between the uncracked, cracked and the healed state of cementitious specimens can be verified, enabling the crack closure monitoring over time as well as the visualization of the interior. In this study, a comparison between the healing ability of a reference mortar and a mortar with superabsorbent polymers (SAPs) was performed and a correlation with the crack width evolution was demonstrated.

Elastic waves are commonly used for the evaluation of concrete structural health. Wave speed is firmly connected to the stiffness and is indicative of strength and damage condition. When access to multiple sides is limited, the evaluation takes place solely from the open surface where all sensors are placed. In this case, the size of the sensor is crucial because of the “aperture effect”. This is basically the phenomenon of wavelengths shorter than the sensor size cancelling each other since both their positive and negative phases act simultaneously on the sensor’s surface. Although this effect has been studied relatively to the amplitude and the frequency content of the surface wave pulses, its influence on velocity has not been similarly studied, even though the velocity value is connected to concrete stiffness, porosity, damage degree and is even empirically used to evaluate the compressive strength. In this study, numerical simulations are conducted with virtual sensors of different sizes to measure the surface wave velocity as well as the dispersion (or its dependence on frequency) in relation to the sensor size on homogeneous and heterogeneous material. The strong effect of sensor size is indicated and suggestions towards rules for reliable measurements on a concrete surface are made. Experimental measurements on cementitious media by sensors of different sizes are also conducted validating the numerical results.

Synthetic aperture radar (SAR) imaging has become an emerging technique in the remote subsurface sensing of construction materials (dielectrics) such as concrete, FRP-concrete, timber, and masonry. Increased center frequency, bandwidth, and synthetic aperture can improve the resolution of SAR images with better contrast and signal-to-noise ratio for condition assessment. However, since most reported SAR applications are for surface sensing, little is known about how to interpret SAR images for subsurface sensing of construction materials. In this paper, extraction, interpretation, and application of parameters from SAR images are presented for condition assessment, using two sides of a CFRP-concrete specimen as an example. A 10-GHz center frequency laboratory SAR imaging system was used for data collection. Seven incident angles were considered (0-deg, 15-deg, 20-deg, 30-deg, 35-deg, 45-deg, and 60-deg.) Multi-dimensional SAR image parameters were defined and applied to the fourteen SAR images of the CFRP-concrete specimen. We have found that performance of SAR image parameters depends on the type of condition assessment problems at hand. Combined use of SAR image parameters of different dimensionalities is encouraged, but a systematic understanding on the physical meaning of each SAR image parameter is necessary. Maximum SAR amplitude is easily affected by background noise. Integrated SAR amplitude in theory performs better than the maximum SAR amplitude. Range curves of SAR images are similar to the A-scan curves in GPR, but they contain more coupling effects from geometry and material's. Cross-range curves of SAR images can also used for surface profiling of concrete specimens. Critical contours of SAR images show the spatial distribution of the backscattering response of concrete. K-R-I curves of SAR images extract features of the images and facilitate quantitative comparison between two SAR images collected at different orientations and image resolutions.

Remote sensing is one of the promising technologies in nondestructive evaluation (NDE) and structural health monitoring (SHM) for civil infrastructures, in view of its convenience and flexibility in field data collection. Among existing remote sensing techniques, laser doppler vibrometer (LDV) is an ideal tool for periodic inspection of building-type and bridge-type structures. In this paper, application of LDV on extracting modal frequencies and damping of a building model on a shake table is investigated to study the effects of mass distribution on characteristic damping of the building model. A commercially available LDV (Short Wave InfraRed (SWIR) laser, range = 1.7m 300m, OptoMET) was used on a building model excited by ground motion generated by a shake table in the frequency range of 1Hz and 10Hz. Sinusoidal ground motion was adapted to produce non-trivial initial conditions (displacement and velocity) in the vibration of the building model. Free vibration response of the building model with different configurations (e.g., location of introduced mass) was measured by the LDV. A metal block was used as introduced mass and a target reflector for the LDV. Effects of excitation frequency on the measurement accuracy of modal frequencies and characteristic damping were also studied. From our experimental result, it is found that the measurement accuracy of LDV is not vulnerable to the change in excitation frequency. In-plane displacement appears to be more reliable than out-of-plane displacement for extracting modal frequencies and characteristic damping of the building model.

The research objective was to evaluate and monitor the pavement roughness over a test track of the urban road in conjunction with the response-based compact roughness evaluation device (CRED) as well as the high-speed laser-based profilometer. The use of CRED starts by installing the device on the dashboard of the vehicle, powering on the device, ensuring the GPS connection, and engaging the recording of the vertical responses through the accelerometer. The research outcomes unveil that the average IRI and AARI of Northside were from 2.0 and 2.5 down to 2.0 and 2.0, respectively. On the other hand, the average of IRI and AARI of Southside were from 2.6 and 2.8 up to 4.2 and 3.4, respectively. The traffic wears and impacts may make the pavement surface smoother in Northside, while the construction of man/utility holes installed on the right wheel path might worsen the pavement roughness in Southside. It also shows that AARI is a capable but bargained device (less than USD 3,000) to monitor the pavement roughness. It is to recommend a longer span of the pavement roughness monitoring and a vehicle detector are required in the future project. Longer monitoring may disclose how the traffic flow to degrade the pavement surface and cause irregularities, while VD is able to provide quantitative data to assist the analysis of pavement roughness associated with traffic flow.

Deterioration of underground structures such as pipelines and culverts are very difficult to be detected in the early stage. Yet, their failures can cause significant damages to the above roadway and adjacent superstructures. However, detecting the development of deterioration in underground culverts is a challenging task. For example, development of an underground void or sink hole can cause stress redistribution in the vicinity of a culvert and potentially damage the culvert. In this paper, we present our research work on the use of a dual-frequency (300MHZ and 800MHZ) ground penetrating radar (GPR) system (UtilityScan, GSSI) for detecting the growth of an artificial void in a layered culvert structure. A laboratory three-layer (asphalt layer, gravel sub-base layer, and subgrade layer) setup with an underground air void and a 30” diameter culvert was manufactured as a testbed. Three different levels of damage were artificially created inside the laboratory setup and inspected by the GPR system on surface layer. From our experimental result, it is found that the wavelength of 300MHz GPR signal is not sensitive enough to image the growth of an underground void inside the experimental setup. On the other hand, the wavelength of 800 MHz GPR signal can reconstruct the growth of the underground void. This research work indicates the advantage of multiple frequencies for GPR subsurface sensing.

Interest in the use of composite materials is increasing due to the urgent need to improve and harden our infrastructure, combined with the pending infrastructure bill. Pilot and prototype efforts demonstrate the advantages of factory-produced rapidly-installed lightweight composite bridge decks. The composite technology and the fabrication method facilitate structural health monitoring using low-cost embedded sensors and fiber optics. This session will detail installation of the first composite bridge deck in Tennessee. Results of the project will be presented, with a focus on the ongoing structural health monitoring, embedded sensors, and the outlook for standard installation.

Active vibration monitoring of civil structures provides a basis for the assessment of their actual health status and advanced warning of potential structural deficiency. This paper discusses the wirelessly networked beam-array Laser Doppler Imaging Vibrometer (LDIV) for non-contact evaluation of civil infrastructures. The measured time domain data on structure spatial displacement and velocity serves as an input for various operational modal analysis algorithms and eventually for detection and localization of the potentially defected area. Essential for LDIV system effectiveness in structural health monitoring is the capability of its operational software in real time processing of the collected data.

Additive manufacturing (AM) techniques can be applied for the production of carbon fiber reinforced polymer (CFRP) elements. This paper aims to present a numerical modelling of tensile and flexural (three-point bending) testing of 3D-printed CFRP specimens as virtual tool using ABAQUS. A 3D unidirectional composite model at the macro-scale has been established to assess the numerical parameters of samples such as its mechanical strength and strain at failure. These simulations can replicate the experimental tensile and flexural testing in which the characterization of specimens’ mechanical properties is obtained. A fairly good comparison was obtained between the predicted results from numerical models at the macroscale level with available experimental data.

Investigating the mechanical properties of biological and biocompatible hydrogel materials has recently gained extensive research interest due to their potential applications in various fields including tissue engineering, biorobotics and sensors. However, estimating the essential structural characteristics such as elastic moduli of hydrogel structures may not be easily identified using conventional contact-based techniques such as accelerometers and strain gauges due to their additional mass loading to the structure and influence on the shape of the hydrogel structure by mechanical contact. Non-contact optical methods such as Laser-Doppler vibrometry may be able to identify the vibration characteristics; yet, the low reflectivity of translucent hydrogel’s surfaces is one of the major challenges in laser-based vibration analysis, and experimentally estimating the mode shape requires significant effort. In this study, we aim to investigate a contactless method to simultaneously identify the Young’s and shear moduli of hydrogel structures by employing video-based vibration analysis. Phase-based motion estimation and magnification are utilized to experimentally determine the resonance frequencies and operational deflection shapes and identify the Young’s and shear moduli of gelatinous hydrogel structures. The experimental results of this study provides promising potential of implementing the proposed approach for applications in areas including advanced manufacturing and soil characteristics identification.

In this study, anisotropic stiffness tensors were reconstructed based on fiber orientation distributions obtained from X-ray computer tomography (xCT). A preform was manufactured via a big area additive manufacturing (BAAM) system with carbon fiber (CF) filled acrylonitrile butadiene styrene (ABS). The tailored preform from additive manufacturing (AM) was used in the compression molding (CM) process to produce a low-void high-performance thermoplastic composite panel. An xCT technique was employed to detect the fiber orientations in CF/ABS composites manufactured via three different methods: AM from BAAM, extrusion compression molding (ECM), and AM-CM. The anisotropic stiffness tensor was obtained from the composite panel manufactured via the three manufacturing methods (AM, ECM, and AMCM). A micromechanics theory was used to obtain the orthotropic stiffness tensors of the composite panels and compared with the experimental values. The predicted stiffness tensors of AM and AM-CM composite panels were used to study the deformation characteristics of a steering wheel during airbag deployment by performing finite element analysis (FEA). The approach developed in this study can be utilized for evaluating high-performance composites.

Large format additive manufacturing (LFAM) proved to have a great potential to become an adjacent technology to traditional manufacturing methods. One of the sectors LFAM is targeting is rapid tool/mold development for composites. This includes large mold structures used for high-temperature molding techniques (in-oven or autoclave). Although, these large printed structures (reaching hundreds of pounds) develop thermal-residual stress during cool-down and can eventually crack, turning the structure into waste. Acoustic emission (AE), a passive non-intrusive global nondestructive evaluation (NDE) technique, was used to monitor crack growth and can provide the right tools that can be used for feedback loop for corrective action. This research performs thermal testing on a large AM mold with preexisting cracks, in an attempt to monitor crack growth using AE. AE was able to detect, identify and locate the crack source by means of acoustic features, waveform characteristics, spectrum analysis, and difference in arrival times.

Radiographic testing is extensively used in non-destructive evaluation. However, the contrast of object defects in X-Ray images is generally weak compared to the image dynamic range, which limits defects visibility and makes the image inspection time consuming and tedious. To circumvent this issue, our work builds on a bilateral filter to extract and amplify the low-contrast local variations of the image, supposed to represent the object defects. A critical step of this approach lies in the selection of the bilateral filter range parameter, which sets a contrast upper-bound to the signals that are filtered out, to be scaled to a constant fraction of the rendering range. As an original contribution, we propose to set this parameter to maximize the tone-mapped quality index (TMQI) of the reconstructed image. Since the TMQI combines a multi-scale structural similarity criterion with a measure of image naturalness, its maximization both discourages the selection of a (too large) range parameter that penalizes structural similarity, and prevents the lack of naturalness associated to an excessive noise amplification caused by a too small range parameter. Image subjective quality assessment experiments not only demonstrate that our approach largely enhances defect visibility on real use cases, but also reveal that our recommended parameter is preferred by users to any reasonable alternative in a large majority of cases ( 90%). An objective evaluation based on the Image Discrete Entropy shows that the enhanced images compare favorably to the ones obtained with previous works devoted to High Dynamic Range image visualization

Thermal infrared images have been widely employed to detect defections. However, it is challenging to identify the defects from thermal infrared images covered with shadows and noise. For a series of thermal infrared images, principal component analysis (PCA) is often applied to transform the given series into a lower-dimensional linear subspace. In the lower-dimensional subspace, a low-rank matrix is generated to serve as an optimal estimation from the series of thermal images. However, the information stored in those thermal infrared images will be kept mostly during the transformation. Full recovery of the lost information is not possible. A template containing the major information from the given series can be extracted by employing PCA. Furthermore, PCA has difficulty finding the template should interferences occur while the thermal images are captured. The unnecessary information collected associated with the interferences causes some unfavorable characteristics of the template extracted by PCA. Robust PCA (RPCA) is less susceptible to the abovementioned constraints. In this study, RPCA is employed to extract a template from a series of thermal infrared images. Local binary functions are built to restore the image free of noise by keeping the local boundaries. The defects can be readily identified from the regional boundaries. The proposed approach combining RPCA and local binary functions to analyze the given images in conjunction with level set functions. The processed results demonstrate that the proposed scheme are more effective than PCA in analyzing a series of thermal infrared images containing interferences.

This paper shows how a convolutional neuronal network can be used to segment multiple features (such as matrix, fiber bundles and defects) in a single step from X-Ray computed tomography data acquired from carbon fiber reinforced polymer (CFRP) specimens. The sample analyzed was 5 plies thick plain weave CFRP widely used in automotive and aerospace application. The specimen was scanned using a GE phoenix X-ray Nanotom XCT with an voltage of 60kV and a voxel size of (2.5μm)². To allow for the prediction of multiple classes, the standard U-Net architecture was extended to use a softmax (one-hot encoding) as output layer. The trained network delivers similar results as compared to current state-of-the art methods, with the additional advantage of reducing the number of required human interaction steps. It is also shown how the change of the voxel size impacts the prediction of the model.

Acoustic emission (AE) is an effective technology that can be used for structural health monitoring. One of the most attractive features is the ability to locate AE sources. Characteristic parameters of waveform importing Artificial Neural Network (ANN) model is proposed for acoustic emission source location. The waveform of AE signal is apperceived by sensors, and decreases dispersion effect by wavelet transform. Input of ANN includes characteristic parameters of AE signal, waveform data and characteristic quantities which have been preprocessed. Time difference of signals and other parameters acts as sample which can decrease the influence of wave speed. Based on the agreement that ANN has the ability approximate any nonlinear mapping, it is feasible to build a model of time difference of signals and other characteristics with AE source position. This locating method can be widely used in AE source location on account of high accuracy, practicality and reliability.

In recent years, structural health monitoring technology has received considerable attention for its potential to provide objective, accurate, and real-time assessment of structural conditions in comparison with the current periodic visual inspection practice. Battery-operated wireless sensors eliminate wires and make their installations easy. However, wireless data transmission consumes significant power and requires frequent replacement of batteries, which is particularly difficult for structures that are located in rural areas with poor accessibility. To address this obstacle, a low-power multi-hop wireless sensor network that monitors the vibration of large-size civil infrastructure is developed and validated in this work. The wireless communication devices employ special lowpower wireless devices that operate in the sub-GHz band, which allows for long-distance communication exceeding 1 km and easy deployment because no license is required. Data collection over wide areas is achieved through relay transmission (multi-hop communication), in which the wireless sensor data are received and retransmitted by surrounding sensors. A fail-safe function is built to achieve a sensor data collection rate of 99.999%. To save power, the communication timings are synchronized, and time-division communication is implemented, in which the wireless devices are made to sleep in time bands when communication is not needed. To validate this wireless sensor network, a field test was carried out to measure the acceleration response of a long-span suspension bridge, based on which the bridges natural frequencies and mode shapes were successfully identified. The field tests also demonstrated the ease of installation and operation of the wireless sensor system.

Current state-of-the-art systems for measuring movements at a microscopic scale in MEMS mostly rely on laser Doppler vibrometry (LDV). However, a major downside of LDV is that only one point at a time can be tracked and only in the direction of the incident laser beam. On the other hand, stroboscopic video microscopy (SVM) allows monitoring the inplane displacements of all points in the field of view simultaneously. Commercially available vibrometry systems often provide an SVM mode. However, their resolution typically ranges from several to tens of nanometers. In contrast, some experimental SVM systems described in literature have achieved resolutions down to tens of picometers. Here we compare the performance of our self-built SVM setup to a modern commercial LDV device in characterizing piezoelectric actuators made from sintered lead zirconate titanate (PZT). The samples were stimulated with sinusoidal signals to induce surface strain in all three directions of space. Maps of the induced strain fields were recorded in-plane with SVM and out-of-plane with LDV. Our measurements prove that SVM, as realized in our setup, can be a cost-effective alternative to LDV for monitoring and characterizing of MEMS with sub-nanometer accuracy. Especially at low frequencies and when applied to challenging samples, SVM can outperform LDV in terms of accuracy and time efficiency.

A flexible transparent piezoelectric surface acoustic wave (SAW) transducer was recently developed based on ultrahigh transparent polyvinylidene fluoride (PVDF) piezoelectric film and Indium tin oxide (ITO) transparency interdigital electrode. The ITO electrode was configurated as a pair of interdigital electrode pattern with input and output terminals. On the surface of a solar cell panel, the measured visible light transparency efficiency of the PVDF film is 99.4%1 . When the input is a pulse voltage signal and the output is a receiver circuit, the SAW transducer is acting as a sensor to detect the variation of the mass, such dust on the surface. When a sinusoidal voltage is applied to the input terminal of the SAW transducer and the output connected with matched resistive load, a unique direction propagation acoustic wave is generated to remove fully covered dust in a few minutes. The transducer can be used for self-sensing and cleaning dust for solar panel, building windows, optical equipment. The transducer can also be used for structure health monitoring and bio detections, etc. In this paper, the finite element modeling results with different conditions for this SAW transducer are presented. These results will provide guidelines for a specific sensor design to match some practical application requirements.

Modal analysis based vibration monitoring has been extensively adopted for health assessment and continuous monitoring of structures. Conventional accelerometer derived displacement based modal parameters are generally used for evaluation but with a high operational cost, fragility issues and bandwidth limitations. Strain based modal parameters have been increasingly explored for monitoring purposes owing to their high sensitivity towards any perturbation in the structural property. Piezoelectric sensors are highly sensitive, low cost, smart material based dynamic strain sensors. However, they have been sparsely investigated for their efficiency in obtaining modal parameters, specially under random excitations. This study examines the ceramic based lead-zirconate-titanate (PZT) piezoelectric sensors for their applicability in modal analysis based vibration monitoring under shaker driven random excitations. PZT patches and accelerometers were used to record the measurements of a scaled down model of pedestrian under random excitations from a mechanical shaker. Polyreference Least-Squares Complex Exponential (p-LSCE) system identification algorithm was adopted to obtain the modal parameters from both the sensors. PZT patches were equally effective as accelerometers in capturing the modal parameters namely frequency, damping and mode shapes. First ten modes under consideration were obtained by PZT patches, with an error of under one percent with accelerometers, from both the system identification techniques. Damping ratios obtained from both the sensors were in good agreement with each other. Mode shapes form the either of sensors were in excellent correlation with each other with modal assurance criteria (MAC) values higher than threshold value of 0.75.

In this study, the application of phase array ultrasonic sensors attached to the edge of thick steel place is studied numerically and experimentally. The dynamic numerical models are built to test different excitation frequencies and their sensitivities to different crack lengths at the sensing points. The frequencies tested are 400 kHz and 300 kHz, while the crack lengths assessed are 0 to 10 mm. In general, damage index related to defect size and mode is calculated by comparing signals from damaged structure to signals from the undamaged structure. New damage index is introduced in this study by using wave modes detected by each sensor in array as the surface wave and the reflected wave from the fatigue crack. Experimental studies are performed using the modified steel compact tension geometry instrumented with in-situ phasedarray ultrasonic sensors and an optical microscope to measure the crack growth per fatigue cycle. The optical microscope images are analyzed with image processing to determine the crack length and the fatigue cycle relationship. The fatigue loading is paused every 1 mm crack length and the ultrasonic measurement is performed. It is validated that the nearest receiver to the transmitter has the highest sensitivity to crack due to the best separation between surface wave and reflected wave.

Turbine engine components are continuously being improved and updated to meet flight safety and durability requirements. This leads to having engine manufacturers try to fulfill their commitments in securing products that offer superior operational security and strength. Most of their concerns or interest lies in the developments of the rotating components such as the rotor disk. These components typically undergo severe operating conditions and are subject to high centrifugal loadings which expose them to various failure mechanisms [1, 2]. Therefore, to alleviate these design issues, health monitoring, experimental testing and analytical validations are a must. As a result, simulation tests studies are conducted to emulate faults in a rotating disk using a highly specialized machinery fault simulator (MFS) with capabilities to replicate problems with balancing, alignments, and bearing defects. Consequently, this paper is focused on demonstrating the applicability of having such technical innovation to help assess the health of a turbine engine like rotating components employing a rotor dynamics approach. This study takes the fault vibration readings at multiple motor speeds and discusses how they can be related to real faults in other machines such as engines and transmissions. Data obtained from these tests related to investigating rotor vibration response under imbalance and shaft misalignments conditions based on frequency and amplitude measurements are presented.

Structured materials with atomic-lattice mimicking features at the microscale, e.g., microlattices, have demonstrated extreme mechanical properties. Elastoacoustic hybridization of water-saturated microlattices can be exploited to achieve a gradient of refractive index for underwater wave focusing. We characterize the acoustic properties of fluid-saturated elastic lattices and construct an ultrasonic wave focusing device with a modified Luneburg lens index profile. Our approach showcases a computationally efficient homogenization design approach that enables accelerated design of acoustic wave manipulation devices. By matching the acoustic impedance with surrounding fluid, microlattices with extraordinary stiffness-to-density ratio and enhanced transmission will prove useful for biomedical applications.

Engine technology advancements is a continuous prospects that manufacturers strive to maintain and improve to ensure better reliability, fuel efficiency and optimum operational capabilities. Primary performance parameters such as altitude and Mach number define the operational set points for the engine. The main aim of these efforts is to introduce the production of adequate thrust output to allow safe and stable maneuver of operation. Improving the efficiency and minimizing the operation cost via fuel savings is a key factor for a successful turbofan engine. The current study is to take on analyzing the performance of a high bypass ratio turbofan with the use of Akira DGEN 380 engine simulator [1-2]. This engine simulator is a key part of the laboratory within the college of Aeronautics and Engineering at Kent State University, Ohio and is essential in understanding the thermodynamic and aerodynamics properties of engine components. Moreover, the purpose of this project is to examine changes in engine performance under failure of components that impact the operation of the aircraft. This is done by simulating the aircrafts flight path and recording the engine key operating parameters throughout the flight. We are able to deduce the change in performance when we compare these simulations to a normal flight. Conditions such as specified components unexpected failures under a quantified flight path are being investigated to assess the performance of the engine under such operational circumstances. The typical flight duration for a private light jet is about two hours, and this simulation includes modeling a trip from Cleveland, OH to Washington D.C. During this flight, the maximum cruise altitude of twenty-three thousand feet traveling at about two hundred and fifty miles per hour is reached. The operational evaluation of the engine was assessed during this flight path and results pertaining to failure diagnosis based on the engine response obtained from the virtual test bench are presented and discussed.

Acoustic-laser technique has been developed as a promising method to detect defects in structures by vibrating the target object with an acoustic excitation, especially to identify near-surface defects in fiber-reinforced polymer (FRP)-bonded systems. The vibration characteristics are measured by laser beam to determine the integrity of interfacial bonding in structural systems. The sensitivity of acoustic-laser technique can be affected by several operational parameters. The limitation of data acquisition system and the missing data during measurement can influence the accuracy of defect detection. The defect size can also affect the effectiveness of acoustic-laser technique as the acoustic wave is unable to excite the defect region if the defect size is too small. To efficiently reconstruct acoustic-laser measurement for continuous or random missing data situations, a machine learning approach is proposed considering the effect of defect size. This method is based on K-singular value decomposition (K-SVD) with the orthogonal matching pursuit (OMP) algorithm. In this study, FRPbonded systems with two different sizes of interfacial defect are adopted in the experimental measurement using acoustic laser technique for defect detection. The results demonstrate the effectiveness of machine learning method in the reconstruction of the missing information for electrical signals. The reconstructed data is more reliable for the cases with smaller defect sizes and random missing data. For further application in a broader range, more measured results of defect size should be considered in the dataset of the proposed method.

The main purpose of this work is the development of a novel nondestructive and non-contact method based on nonlinear acousticsfor assessing the structural integrity of metallic alloys. This method enables real-time monitoring of the material’s degradation. The introduction of a sinusoidal ultrasonic wave, of given frequency and sufficient amplitude into an anharmonic solid, leads to the distortion of the propagating wave. This results to the generation of higher harmonics of the fundamental frequency. In comparison with linear ultrasonic parameters, such as velocity and attenuation, the measurement of the amplitude of these harmonics provides information on the coefficient of higher order terms of the stress strain for the nonlinear solid. A metallic alloy subjected to cyclic loading accumulates damage with time resulting to large changes of the material’s nonlinear parameters. This paper deals with monitoring the second and third harmonics of metallic alloy specimens during mechanical fatigue using Laser Doppler Vibrometry (LDV). Surface acoustic waves were used to induce a single frequency ultrasonic wave in the material for in-situ characterization of the fatigue damage. The LDV technique was able to resolve the third harmonic enabling to assess the third order nonlinear parameter in addition to the second one. It was shown that the third order nonlinear parameter provides a very sensitive measurement of minute microstructural changes due to fatigue.

A new method of mode parameter identification based on Extreme-point Symmetric Mode Decomposition (ESMD) and Matrix Pencil Method (MPM) is proposed for processing wind tunnel test data.The proposed method first decomposes the test data to a series of narrow-band signals by band-pass filtering.Then, the ESMD method is used to perform modal decomposition to obtain several single-mode response signals.Next, each singlemode response signal is processed using Natural Excitation Technique(NExT) to obtain a free attenuation response signals.Finally, the mode parameters were identified by the MPM.After the verification of simulation data, the proposed method is applied to identifying the mode parameters of the wind tunnel test data, and the results are compared with the mode parameter identification results based on the empirical mode decomposition (Empirical Mode Decomposition, EMD). The results show that the proposed method can better identify the mode parameters of the structure from the wind tunnel test data with good applicability and sufficient identification accuracy.

In order to predict the flutter boundary of the wing under turbulent excitation, wavelet decomposition is used to preprocess the signal, and the free attenuation signal is extracted based on CEEMDAN and natural excitation technology. The matrix pencil method is used to identify the modal parameters. Finally, the Z-W method is used to determine the aircraft loss stability, fit the change curve of the judgment and extrapolate the flutter boundary. The modal parameters of the simulated turbulence excitation signal are identified, the numerical simulation of the flat wing model is carried out, and the wind tunnel flutter test data of a single wing model are calculated. The results show that: using matrix pencil method to process the free attenuation signal obtained by ceemdan, wavelet de-noising and natural excitation technology, the modal parameters of turbulent excitation response can be identified more accurately, combined with Z-W method, the flutter boundary can be predicted in the case of early wind speed, and the test safety can be improved.

The large-span transmission tower line system is an important lifeline power engineering facility, because of long-term exposure to the natural environment, it needs to face a variety of different complex environment, especially some complex environment, such as traffic, vehicles influence and lush vegetation influence and so on. Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. For environmental changes such as personnel, vehicles and illegal planting, some transmission towers are in remote locations, and the staff cannot find them in time. Aiming at the deficiency of artificial vision detection method, the research on the environmental perception technology of transmission tower based on deep learning is proposed. A large amount of data collected is trained, verified and tested with deep learning algorithm. In order to solve the problem of transmission towers exposed to complex environmental influence, an appropriate model was established based on deep learning algorithm, and the image was used to verify and test. The trained model was tested on some new images that were not used in the training and verification process. Experimental results show that this method can accurately identify the complex environmental objects.