This PDF file contains the front matter associated with SPIE Proceedings Volume 12950, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Polymer-based composites frequently encounter damage, often lurking beneath the surface and proving challenges to their early detection and repair. While material-based sensors show promise for encoding self-sensing properties within these composites, their in situ healing and reprocessability remain significant challenges. Therefore, the overarching goal of this study is the creation of a reprocessable polymeric composite encoded with self-healing attributes and the ability to autonomously sense damage.

At the core of this innovation are vitrimers, a polymeric material characterized by a covalently adaptive dynamic network responsive to external factors such as heat. They combine thermoset-like resilience with thermoplastic-like flowability on demand under external stimuli. We nanoengineer a polyester-based vitrimeric polymer by incorporating piezoresistive carbon nanotubes (CNTs) as reinforcing elements that not only enhance its mechanical strength but also create a percolation network within the composite, thereby enabling piezoresistive self-sensing properties, all the while preserving the intrinsic self-healing capabilities offered by the vitrimeric matrix.

The fabrication process of the composite involves a solvent-free in situ polymerization method that combines epoxy and anhydride-containing monomers with ~ 0.1 wt.% of CNTs. Once it was established that the introduction of CNTs into the polymeric matrix did not compromise the mechanical properties of the composite, their strain-sensing properties were characterized by applying cyclic loading while measuring their electrical resistance. Strikingly, CNT-enhanced vitrimer composite consistently retains its mechanical and sensing properties through repeated cycles of reshaping and reprocessing, underscoring its potential as a robust distributed strain sensor. This polyester-based vitrimeric composite is also easily recyclable without harsh chemical treatments. Preliminary findings from this study conclusively demonstrate that the bulk composite boasts both self-sensing capabilities and in situ detect healing properties, charting a promising course towards the development of a mechanically resilient multifunctional composite that seamlessly integrates selfsensing and healing capabilities.

Post processing X-ray computational tomography (CT) inspection data for additively manufactured (AM) components can induce deviations in defect quantification, affecting subsequent fatigue and failure predictions. To assess the influence and potential impact of segmentation-induced measurement deviations, this paper applies several segmentation techniques to X-ray CT data for powder bed fusion Ti-6Al-4V specimens exhibiting porosity conditions. X-ray CT reconstructions were segmented with varying techniques including Otsu’s thresholding, random forest, k-nearest neighbors, and the multilayer perceptron. Metrics such as pore size and global porosity were compared for internal validity. Then, top-down X-ray CT measurements of surface-breaking porosity were compared to optical profilometry for cross-validation.

X-ray inspection for detection of cracks requires that the x-ray incident angle is within a small angle e.g., +/-5° of assumed crack plane orientation at the point of x-ray intersection with the crack. Currently, American Society of Testing and Materials (ASTM) nondestructive evaluation (NDE) standards or guides do not provide a procedure to meet this requirement. Because of lack of precise guidelines, the requirement is not implemented consistently across industry in using x-ray inspection for detection of cracklike flaws. Inconsistent implementation of the requirement causes variation in reliability of detection of cracklike flaws. In many cases, it is observed that the implemented techniques, which use inaccurate interpretation of the x-ray incident angle requirement have resulted in lower reliability. This work provides correct implementation of x-ray angle requirement for cylindrical parts.

Electrical conductivity in nanocomposites is a complex phenomenon governed by a myriad number of physical and chemical factors. However, the interrelationships between segmental dynamics and its effect on electrical conductivity is less understood. Herein we create a solvent free nanocomposite synthesized in a single step process. Facile covalent bonding is achieved between functionalized nanotubes and the lignin-based matrix using small molecule coupling agents. The covalent bonding and shearing are hypothesized to lead to a breaking of the larger agglomerates, leading to excellent dispersion and thereby percolation at much lower concentrations than can be achieved by traditional blending. We show that while the above process can be utilized to achieve excellent dispersion and thus percolation and conductivity, segmental dynamics also plays a key role in dictating electrical conductivity.

Portland cement concrete (PCC) is a versatile and widely used construction material renowned for its strength and durability. The mechanical properties of PCC, including compressive strength, flexural strength, and splitting tensile strength, play a pivotal role in ensuring the safety and sustainability of structures such as buildings, bridges, and dams. Traditionally, the determination of PCC's compressive strength involves destructive testing of standard-size concrete cylinders until they fail. While nondestructive evaluation (NDE) techniques are available for assessing these properties, they often require direct contact between the sensor and the concrete surface, making them less efficient and practical compared to remote sensing techniques. In this paper, we applied three NDE techniques for estimating the mechanical properties of concrete, including synthetic aperture radar (SAR), ultrasonic pulse velocity (UPV), and a rebound hammer. We manufactured a total of 48 laboratory concrete cylinders (diameter = 3", height = 6"). These cylinders were created with different water-to-cement ratios (0.4, 0.45, 0.5, and 0.55) with a mix design ratio of 1:2:3 for cement: sand: gravel (by mass). Four dates of compressive testing were considered (7-day, 14-day, 28-day, and 96-day). Before these cylinders were tested by destructive compression test, they were measured by three NDE techniques. A 10GHz SAR system with a 1.5 GHz bandwidth, a 54kHz UPV system, and a Schimdt rebound hammer were used to inspect those cylinders. Our experimental results reveal a discernible relationship between the compressive strength of concrete and the NDE data. The increase of cylinder age resulted in the increase of compressive strength of PCC cylinders. SAR image parameters, UPV curves, and rebound hammer curves showed correlated patterns. This technique has the potential to provide a nondestructive and efficient means of assessing concrete strength and durability, with significant implications for the construction industry in ensuring the safety and sustainability of various structures.

Wooden crossties are an important component in railroad infrastructure as a load-bearing member connecting rail tracks and the ballast. Their internal failures (e.g., flexural cracks, delamination, and railroad crosstie-ballast failures) could be left undetected for years from routine inspection. Subsurface nondestructive testing/evaluation (NDT/E) techniques are required for effective routine inspections. In this paper, the feasibility of a 1.6 GHz ground-penetrating radar (GPR) was assessed for condition assessment of wooden crossties in both contact and remote setups. Two wooden crossties (one intact with length = 100 “, width = 7”, depth = 9”), and one damaged with (length = 100”, width = 6”, depth = 9”, with two symmetrical wedge-shaped cracks due to end-splitting on both sides) were considered. A height-adjustable Styrofoam slab was created and used for controlling the separation distance to different values (0, 1”/2.5 cm, 2.6”/6.6 cm, 4.2”/10.7 cm, and 5.8”/14.7 cm). The crossties were scanned along their length and multi-dimensional amplitude, and dielectric mapping analyses were performed to detect and map the locations of crack boundaries for each separation distance. From our results, it was found that GPR scan parameters such as dielectric analysis and A-scan comparison can detect and map defects in railroad crossties. Moreover, it was found that an increasing separation distance reduces the GPR signal amplitude as well as damage detectability for condition assessment of wooden crossties in railroad infrastructure.

This study aims to advance the field of composite material fatigue prognosis by employing Long Short-Term Memory (LSTM) neural networks for in-situ damage progression monitoring under random dynamic loading conditions. A unique approach is adopted, wherein Laser-Induced Graphene (LIG) interlayers are embedded into fiberglass composites. These LIG interlayers are innovative sensors owing to their piezoresistive properties, enabling real-time measurement of fatigue damage monitoring. The crux of this research lies in applying LSTM neural networks, specifically designed to handle time-series data, making them ideal for modeling the stochastic and unpredictable nature of fatigue loading in composite materials. Contrasting the performance of LSTM with traditional Multilayer Perceptrons (MLP), it is observed that LSTM yields superior prediction accuracy in estimating the remaining useful life (RUL) of LIG interlayered fiberglass composites. By utilizing predefined electrical resistance damage parameters, the LSTM algorithm correlates the rate of fatigue damage buildup to the impending decline in mechanical performance. This research establishes that integrating piezoresistive LIG interlayers with LSTM neural networks culminates in a robust, reliable, and closed-loop system for structural fatigue monitoring and lifecycle prediction in composite materials subjected to random dynamic loading.

Carbon nanotubes (CNTs) have been widely used in various fields due to their remarkable electrical and mechanical properties. In the construction industry, several studies have been conducted in an attempt to impart conductivity to cement-based composites by incorporating CNTs into the composites. The electrical properties of CNT-incorporated cement-based composites undergo changes due to several factors, allowing one to use these composites as cement-based sensors. Carbonation has been regarded as a significant factor contributing to the deterioration of concrete structures. The reaction between hydrates and dissolved carbon dioxide in concrete leads to the precipitation of calcium carbonate and reduction of pH levels, causing corrosion of reinforcement. Thus, numerous efforts have been dedicated to non-destructively assessing the extent of carbonation of concrete structures. This paper summarizes previous studies on the non-destructive evaluation of the extent of carbonation in concrete. In addition, a preliminary study on the applicability of CNT-incorporated cement-based composites embedded in the concrete structure as sensors for measuring carbonation front will be introduced.

A 3-dimensional (3D) voxel-based synthetic Polycrystalline Volume Element (PVE) is generated at various texture intensities; subsequently, the structural-mechanics module of a commercially available finite element (FE) solver is used to simulate elastic wave propagation through the PVE. Keeping the grain size distribution unchanged, two extreme behaviour of wave velocities have been achieved at two texture condition(s), namely Cube texture {001}⟨100⟩ and Copper texture {112}⟨111⟩, where Cube texture corresponds to most compliance; in contrast to, Copper texture which corresponds to the stiffest direction of the crystals in the PVE. Results also show a significant suppression of wave dispersion with an increment in texture intensity.

This paper presents a feasibility study for the visualization of hidden damage in aluminum plates using the integration of a digital camera, projected speckles, and a baseline-free wavelet transform mode shape curvature (WT-MSC) damage index. To capture out-of-plane motion in the plates, off-axis 2D digital image correlation (DIC) is applied. With the camera at an angle with respect to the plate surface normal, a component of the higher-amplitude transverse displacements can be captured. Compared to 3D DIC, the system is less complex, there is no need for camera calibration, and the 2D DIC algorithm is more computationally efficient. A major limitation of DIC for practical applications is the need to apply a speckle pattern to the surface to introduce trackable features in the image for tracking displacement. Projected speckles replace the need for surface-applied speckle patterns. Thus, minimal surface preparation is required. Two geometrically identical 305-mm x 305-mm aluminum plates with thinning defects of different sizes and depths were used to demonstrate the system. Through the excitation of a 20 Hz to 1 kHz chirp signal in a single-edge-clamped plate, the first 12 transverse vibration modes of the plate were sensed. These mode shapes were recreated with the off-axis 2D DIC system, and a wavelet transform mode shape curvature (WT-MSC) damage index was applied for damage imaging. This index is sensitive to irregularities in the higher mode shapes caused by differences in geometry in the damaged regions. The system provided clear damage images with a clear correlation with actual damage geometry regardless of plate orientation. This system serves as a preliminary study for the eventual application of imaging barely visible impact damage in composite plates using projected speckles.

High-rate systems are defined as physical systems that undergo large perturbations, often exceeding 100 g’s, over very short durations, often less than 100 milliseconds. Examples include blast mitigation mechanisms and advanced weaponry. The use of control feedback to empower high-rate systems requires the capability to estimate system states of interest in the realm of microseconds. However, due to the dynamics of these high-rate systems being highly nonlinear and nonstationary, it is challenging to predict their behavior using conventional state estimation methods. To address this issue, we conduct a study that explores the integration of topological data analysis (TDA) and recurrent neural network (RNN) to improve predictive capabilities for high-rate systems. Here, TDA features are used as the input to a machine learning algorithm to determine the state of a high-rate system. We conduct practical evaluations using laboratory datasets from experiments in the dynamic reproduction of projectiles in ballistic environments for advanced research (DROPBEAR), focusing on localizing fast-changing boundary conditions on a cantilever beam. The study demonstrates the ability of the method to classify and predict a system’s fundamental frequencies. This approach helps understand the structure of the underlying high-rate dynamics, leading to improved accuracy and precision in state estimation and prediction.

In computational materials science, simulation techniques must balance bridging time and length scales with maintaining high accuracy at a reasonable computational cost. In this study, a data-driven parametrization method through machine learning algorithms is proposed. The proposed method aims to decrease the time required for parameter optimization, enhancing efficiency of ReaxFF potential. This innovative approach employs a combination of reactive and non-reactive molecular dynamics simulations to simulate phenomena that demand extended time scales or involve larger systems beyond the conventional capabilities of ReaxFF. ML algorithms are utilized between the reactive and non-reactive stages to forecast non-reactive forcefield parameters by considering the updated bond topology of the system. The proposed algorithm can be accelerated on the GPU, achieving optimized management of the computing power and memory requirements imposed by ReaxFF MD on computer hardware. This study is envisioned to promote the application of ReaxFF in large and complex material systems which aims to provide more efficient and accurate predictions compared to traditional ReaxFF.

This study introduces an innovative approach that employs Physics-Informed Neural Networks (PINNs) to address inverse problems in structural analysis. Specifically, we apply this technique to the 4-th order PDE of Euler-Bernoulli formulation to approximate beam displacement and identify structural parameters, including damping and elastic modulus. Our methodology incorporates partial differential equations (PDEs) into the neural network’s loss function during training, ensuring it adheres to physics-based constraints. This approach simplifies complex structural analysis, even when specific boundary conditions are unavailable. Importantly, our model reliably captures structural behavior without resorting to synthetic noise in data. This study represents a pioneering effort in utilizing PINNs for inverse problems in structural analysis, offering potential inspiration for other fields. The reliable characterization of damping, a typically challenging task, underscores the versatility of methodology. The strategy was initially assessed through numerical simulations utilizing data from a finite element solver and subsequently applied to experimental datasets. The presented methodology successfully identifies structural parameters using experimental data and validates its accuracy against reference data. This work opens new possibilities in engineering problem-solving, positioning Physics-Informed Neural Networks as valuable tools in addressing practical challenges in structural analysis.

Active acoustic probing is important for supporting the investigation of icy subsurfaces and under-surface liquid water in potentially water-bearing habitable worlds. Many planetary orbital spacecraft and lander mission concepts have already been equipped with or are in the process of being equipped with state-of-the-art active sounding radar instruments. However, these subsurface radar instruments face challenges when it comes to icy subsurfaces and water-bearing worlds like Europa and Enceladus. The high attenuation of signals in briny ice and salty water hampers the ability to detect subsurface features below such layers, limiting the effectiveness of electromagnetic exploration in these environments. In contrast to electromagnetic based radar instruments, active elastic sounding technology is not limited by the presence of highly conductive layers, such as melt pockets or brine layers that are proposed as explanation of observed surface features on Europa. In this paper, we present our progress in developing the active Elastic Wave Analyzer for Icy Sub-surfaces (EWAIS), which may be feasible to probe kilometers-deep in ice with sensitivity sufficient to yield geological properties.

Nondestructive evaluation (NDE) techniques have made substantial progress, with a focus on sensors utilizing piezoelectric materials to detect guided waves in elongated structures. Guided waves, known for their efficient long-distance propagation characteristics, have traditionally been detected by conventional sensors designed for Lamb wave acoustic emission (AE) detection. However, underwater inspections, such as those in nuclear reactor vessel heads and internal lifting fixtures, introduce unique complexities. The presence of mechanical equipment in contact with water creates a complex acoustic environment, resulting in noise across a broad frequency range, significantly affecting conventional AE sensors designed for specific resonances. To address these challenges, shear-horizontal (SH) sensors, tailored for SH wave detection, can reduce the effect of environmental noise, resulting in cleaner and more reliable AE signals. Comparative studies between conventional and SH sensors confirm the superiority of SH sensors under the noise environment underwater. The paper presents numerical simulations, stress state analysis, attenuation studies, and AE tests, offering insights into the behavior and advantages of SH sensors in underwater inspections. These findings will help the continued development and application of SH sensors in critical industrial settings.

This paper presents a resource-constrained localization system that uses geophones to map pedestrian locations in outdoor spaces. It addresses the need to non-intrusively monitor the level of community utilization of social infrastructure, such as public parks and markets. The system measures the time differences of arrival (TDOA) of footstep ground vibration signals to localize people using hyperbolic positioning. However, signal noise and dispersion impair conventional approaches like cross-correlation to compute the TDOA. This paper introduces a 1D-convolutional neural network model to compute the TDOA based on training data collected at the deployment setting. The model takes short windows of synchronized geophone time-series as input and provides a real-time estimation of the time difference. Results from a validation study in an urban setting show that the TDOA model outperforms baseline methods by over 60%, achieving a localization accuracy of less than 1 meter for single pedestrians.

Concrete crack quantification is a crucial step toward the assessment of concrete structures. Although many computer vision algorithms have been developed to detect cracks and measure their properties, such as width and length, interpretation of the detected cracks in terms of structural behavior remains a challenge. Specifically, identifying the onset of changes in the behavior mechanism (e.g., shear or flexural) of the structure is of great interest. This is particularly important in concrete shear walls subject to cyclic loading, in which cracks may close and thus cause crack width measurements to be unreliable. In such structures, individual and disjointed cracks gradually form mosaic patterns. The transition from one state to the other results in a sudden change in the cracking patterns. This study builds upon the previous work of the authors and uses graph theory to represent concrete crack patterns. The main idea is to utilize graph features and their changes to track changes in the crack patterns. To validate the proposed method, surface crack images of 15 large-scale reinforced concrete shear walls under cyclic loads are used. For each wall, the images include crack patterns at different load levels. Using the proposed methodology, the images of the crack images are converted to their representative graph. Afterward, two specific graph features are extracted: 1) the average degree of network (k_avg) and 2) the weighted average degree of network (kw_avg). The ratio of k_avg/kw_avg versus the drift of the walls at each load cycle is used to detect the change in the cracking mechanism. Results show that the minimum value of the ratio corresponds to the change in the cracking mechanism. The robustness of the proposed metric indicates that it can be used for training machine learning models to develop systems that can automatically signal the onset of a change in the cracking mechanism.

Markers are crucial for measuring the displacement of large structures using digital image correlation (DIC). In general, creating or affixing artificial markers on large structures is challenging. Several studies in the past have addressed this challenge by employing marker-less or target-free approaches. In this work, we integrate an AI-based non-intersecting poly-shape marker identification technique with the DIC program that uses the structural pattern as a marker and automates the selection of the region of interest by enabling strong correlation criteria to obtain the displacements in real-time. The proposed AI-DIC algorithm segments non-intersecting poly-shape markers from the images of the target structure based on the features detected by the KAZE feature detector and descriptors. Further, the investigated marker or the natural structural pattern is automatically given as an input region of interest to the DIC program. Moreover, it considers the marker as a template, correlates it with all subsequent images, and analyzes the displacements and frequencies of the target structure. In addition, the AI-DIC algorithm is realized on an in-house cantilever beam experiment where the images are acquired and processed in real-time.

Building health monitoring is an essential issue in maintaining building sustainability. Thermography has been widely employed for monitoring buildings. Thermography records the surface temperatures of a target. Those areas with abnormal surface temperatures with their neighborhoods can be treated as defects. Image segmentation groups those pixels with similar surface temperatures such that the recorded thermography can comprise several segmented regions. Those segmented regions offer an essential clue for defect detection. Recently, a thermal camera has been installed on an unmanned aerial vehicle (UAV) to collect the surface temperatures of a building. Those collected thermal infrared images are analyzed with image segmentation. With the segmented regions, the potential defects can be identified. On the other hand, a thermal camera installed on the ground is used to record a series of thermal infrared images. The series of thermal infrared images were analyzed using robust principal component analysis (RPCA) to project the given data onto a low-dimension and feature space. The first image extracted from the low-dimension space inherits the significant properties of the recorded images such that the extracted image can be segmented to illustrate the defects. Two processed results are compared. UAVs provide an efficient way to monitor the building's health conditions, and periodic ground observations offer a stable way to monitor the building. Both ways provide an efficient and robust method to monitor the health conditions of a building.

Chloride-induced corrosion attack in steel-reinforced concrete highway bridges is a common and ongoing issue in New England. If left untreated, late-stage corrosion can result in steel rebar section loss, internal stress imbalance, and surface cracks. In recent years, nondestructive testing and evaluation (NDT/E) techniques have been emerging as alternative methods for structural health monitoring (SHM) of civil infrastructure. This paper aims to use synthetic aperture radar (SAR) for corrosion detection of steel-reinforced concrete (RC) panels that were subjected to chloride-induced rebar corrosion. For this purpose, three RC panels (30×30×12.7 cm²) were cast with a No.6 steel rebar (19 mm diameter) at their mid-height with one serving as baseline, and the other two were corroded by accelerated corrosion test (ACT). The RC panels were kept in a temperature-controlled environment (23 − 25° C) since 2017. The RC panels were scanned by a laboratory 10.5 GHz SAR with a 1.5 GHz bandwidth to develop SAR images with two scan ranges of 60 and 70 centimeters. The SAR images were analyzed in time domain and their amplitude parameters were used for corrosion detection. Furthermore, a half-cell potential device was used for verification and quantification in conjunction with out SAR parameters. Our results indicate that the progression of corrosion based on HCP, is correlated with SAR signal parameters such as maximum and integrated, and average SAR amplitudes.

Defects such as surface cracks have considerably smaller scales in civil infrastructure, despite the grand scope of the structures themselves. This article introduces a robotic technique designed to bridge this scale gap by measuring the dimensions and characteristics of small-scale surface cracks in concrete structures at multiple scales. Using a convolutional neural network, our system initially detects potential surface cracks - regions of interests (ROI). Once identified, a high-definition laser scanner, steered by a robotic arm, scans the geometry of these ROIs. The detailed laser scan data is subsequently integrated with surrounding large-scale environmental scans obtained via LiDAR, utilizing 3D point cloud alignment methods. We validate the proposed solution through both computer simulations using the robotic operation system (ROS) as well as testing on a physical concrete specimen. Our method offers an unprecedented resolution of 0.004 mm for crack width measurement and has been successfully tested on real-world cracks with a width of 0.17 mm. A comparative analysis with existing vision-based solutions and traditional crack-width measuring instruments confirms the superior accuracy and efficiency of our multi-scale robotic approach in deriving crucial metrics necessary for assessing the structural health of civil infrastructure.

This study aims to create a comprehensive model that considers multiple scales and physics for predicting the electromechanical behavior of fiber-reinforced composites enhanced with barium titanate (BaTiO₃). In our earlier work, we have demonstrated that depositing BaTiO₃ microparticles of 200-nm-diameter, on fiber surfaces during fiber-reinforced composite fabrication enhances mechanical strength, passive self-sensing, and energy harvesting properties. The key is to carefully control the microparticle concentration to prevent agglomeration. Since the particles are micron-sized, understanding how agglomeration affects the composites' electromechanical properties is crucial for guiding such multifunctional materials’ design. This study introduces a micromechanics-based approach to explore the impact of microparticle dispersion on the bulk composites' electromechanical properties. Insights gained from this investigation are applied in experiments, enabling accurate predictions of mechanical and self-sensing responses in BaTiO₃-enhanced fiber-reinforced composites. Micro-level findings from this computational approach can be integrated into larger continuum models to comprehensively capture the electromechanical behavior of the composite structures at bulk scale. The proposed model is validated by comparing predictions with experimental results, accounting for the nonlinear mechanical and electromechanical behaviors of constituent materials. Consequently, this computational model serves as a digital platform for efficiently designing multifunctional composites.

Carbon fiber reinforced polymers (CFRP) are promising next-generation, lightweight materials for use in the automotive and aerospace industries. Unfortunately, the production cost of virgin carbon fiber is expensive, the manufacturing of CFRP parts is costly and wasteful, and the recycling of CFRP generally results in (1) the reduced mechanical properties of recycled carbon fibers (rCF) and (2) the incorporation of rCF into low-value composites. In efforts to improve upon these areas, we have recently developed malleable, healable, and recyclable vitrimer composites with milled rCF that have produced promising material and mechanical properties—this work aims to investigate and understand the damage/failure mechanisms of these materials. Herein, we utilize dynamic mechanical analysis (DMA) and scanning electron microscopy (SEM) to understand and observe the damage mechanisms that result in the mechanical failure of these materials. Further, we utilize this information to inform the development of a constitutive model. The model is based on a statistical description of the network structure. The principles of thermodynamics are then used to derive the constitutive behavior for CFRP.

The automotive industry is rapidly transforming due to the growing demand for decarbonization. Electric Vehicles (EVs) are becoming increasingly common, and subsequent innovations regarding vehicle lightweighting can increase vehicle efficiency and further reduce carbon footprint. Lithium-Ion Batteries (LIBs) have become the most common power source used in EVs, but LIBs host some inherent challenges, namely thermal runaway. Thermal runaway can be caused by various mechanisms including thermal, mechanical, or electrical impacts which occur during extreme operating conditions. This kind of failure can result in battery fires and explosions that are extremely difficult to extinguish and pose a significant safety risk. A self-sensing LIB enclosure that monitors the temperatures of individual battery modules and provides an early warning signal may be a viable solution to the thermal runaway safety issue. This work studies a hybrid carbon and glass Fiber Reinforced Polymer (FRP) composite designed to replace the traditional metal LIB enclosure, lightweighting the EV design and allowing for condition monitoring sensors to be embedded during the manufacturing process. Battery enclosures have tight space constraints which prohibit surface mounted sensors, making sensor embedment essential. Embedded sensors also have the advantage of a protective composite layer that makes the sensor system more robust during manufacturing and operating conditions. However, this composite layer under which the sensors are sealed produces a time lag in detecting a temperature change within the battery enclosure. This time delay would reduce the efficacy of an early warning system. The purpose of this study is to lay the groundwork for a self-sensing condition monitoring LIB enclosure and characterize the composite enclosure’s temperature response at different layers. A theoretical design of said system is detailed, and a prototype enclosure sample instrumented with temperature sensors is fabricated. Experiments are performed to measure the temperature response of the self-sensing composite prototype when exposed to realistic thermal runaway conditions. This is accomplished through a novel experimental test set up that imposes a unidirectional heat transfer condition by exposing the composite sample to oven temperatures on the top surface and ambient temperatures on the bottom surface. A computational model is developed to predict the composite’s thermal response during different LIB failure temperatures. This finite element transient heat transfer simulation is tuned using initial experimental results and validated by subsequent thermal tests. This study produces a high accuracy thermal model which can be used to provide design optimization information, like the ideal placement of sensors, and predict the thermal response of a composite enclosure when exposed to different thermal loading conditions. The thermal simulation could also be utilized in future works to develop a temperature inference model which could predict LIB health from embedded sensor measurements. This work details the novel experiments and derived finite element model that characterize a potential LIB management system integrated within a self-sensing composite battery enclosure.

Polarimetric methods using the properties of photoelasticity and polarisation optics allow the evaluation of stress patterns in transparent materials. Those are well established for transmission geometries measuring the integrated stress through the whole volume along the light path. On the contrary, surface sensitive measurements are challenging to realise, thus, rarely found and much less precise. However, due to the rising requirements concerning glass quality a surface stress measurement method, potentially suitable for inline processes, is needed. We are developing a non-contact and spatially resolved polarimetric measurement technique to evaluate the surface stress of single-pane and laminated safety glass, which is beyond the current industrial standard methods.

The development of 3D printing has revolutionized the construction industry, presenting a sustainable approach to creating complex structures. However, the use of 3D printing materials in structural applications requires a thorough examination of their strength and serviceability to ensure their safety for practical use. This study aims to investigate the mechanical properties of thirty-six 243.1 mm-long 3D printed W beams (flange thickness t_f = 1.72 mm, web thickness t_w = 2.11 mm, flange width b_f = 17 mm for static testing, and b_f = 12.31 mm dynamic testing, depth d = 13.1 mm, and fillet radius r = 2.19 mm) and ST beams (flange thickness t_f = 1.77 mm, web thickness t_w = 1.86 mm, flange width b_f = 17 mm for static testing and b_f = 14.6 mm dynamic testing, depth d = 12.2 mm, and fillet radius r = 1.87 mm) for structural design and construction applications through static and dynamic testing. Static testing assesses several parameters, including stress-strain curve, Young's modulus, deflection, Poisson's ratio, and shear modulus, while dynamic testing evaluates stiffness and damping under free vibration. Three filaments were used, including Acrylonitrile Butadiene Styrene (ABS), Polyethylene Terephthalate Glycol (PET-G) and Polylactic Acid (PLA). This research used strain gauges to develop stress-strain curves and a laser Doppler vibrometer (LDV) to measure stiffness and damping. Findings were developed on the mechanical properties of 3D printed W and ST beams to improve our understanding of 3D printing for structural design and construction applications. From our experimental result, we found that printing orientation can reduce the stiffness of specimens, even the material has a large value of Young's modulus. Printing orientation can also affect the damping of specimens

A reliable nondestructive evaluation (NDE) technique provides minimum 90% probability of detection (POD) with 95% confidence for detection of cracklike flaws of a qualified size. In this case, the flaws are on backside of metallic material. Eddy current array (ECA) probe or a single sensor eddy current probe in c-scanning mode is used for the flaw detection. Here, two geometries are considered for the test specimens. The two geometries are, a flat plate, and tube or pipe. The probe is assumed to be on outer diameter (OD) surface and the flaw is assumed to be at the inner diameter (ID) surface for tube inspection. The flaw and probe are on opposite side of wall thickness for inspection of plate material. The application may be used for acreage inspection or just for inspection of butt weld and heat affected zone (HAZ) in a tube, cylinder, or plate. The proposed approach is based on developing an instrument standardization or calibration procedure. Decision threshold of inspection procedure is determined using empirical qualification and noise data. The work explores essential parameters that are required to meet certain conditions to ensure reliable flaw detection. Physics-based simulation of eddy current array flaw detection is used to understand effect of essential parameters on signal response (amplitude and phase), and therefore flaw detectability. Simulation data is used to justify choice of calibration reference standard requirements and the qualification approach. Calibration reference standards use artificial flaws such as electro-discharged-machined (EDM) notches. An ECA technique qualification model is provided. Empirical study is proposed to estimate crack to artificial flaw signal response transfer ratio. The paper gives a brief description of tasks to be completed for qualifying ECA technique for reliable detection of the cracklike flaws.

This paper introduces a hybrid composite material, where the structural carbon fiber tow is transformed into a piezoresistive damage localization sensor network, and the structural glass fiber operates as electrical insulation. The piezoresistive damage localization sensor network consists of an array of carbon fiber tows, each with increasing resistance, connected in parallel. Including a second orthogonal array enables accurate 2D damage localization of large-area composites. Impact tests were conducted, finding the sensor network able to reliably determine the location of damage in both orthogonal directions, pinpointing the exact location of damage. This illustrates a capability for in situ monitoring of large-area composites throughout the life cycle of the structure.

Guy wires stabilize outdoor structures typically up to hundreds of feet in height. Exposed to potentially harsh environmental conditions, these wires can experience internal and external corrosion. Up-close visual inspection is often unrealistic given the height. A typical way to characterize corrosion involves taking samples of the wire, which requires destruction and replacement of the current wire. Non-destructive methods are preferred for this reason. To this end, we explore the feasibility of using computer vision techniques on images captured from Unmanned Aerial Vehicles (UAV) to automatically detect corrosion or swelling due to internal corrosion on a wire or cable. We leverage a data-centric approach to train a classifier for identifying corrosion level from a single RGB image. We evaluate the model performance on a dataset of wire images displaying 4 different corrosion levels. Finally, in order to provide additional insights and explainability to a human operator, we use GradCam to analyze the model's decision-making process and identify parts of an image that contributed to the resulting corrosion level label.

For the purpose of lightweight and long span, the structural solution using cable is proposed, especially for the bridges and roofs design. Herein, one of the key members is the anchor cable, which plays decisive role in the cable structure. But the prestressed anchor cable is vulnerable to corrosion and fatigue damage due to various environmental activities. Failure due to accumulative defects or broken wires is inevitable, which seriously effects on the status of the cable-structure system. Therefore, it is essential to propose an efficient method which can realize the real-time evaluation and monitoring of the health status of the prestressed anchor cable. In this paper, the acoustic emission (AE) technique was proposed to quantify the damage progress in the prestressed anchor cable. In order to verify the proposed AE-based method, three prestressed anchor cables with different prescribed defects were tested to failure under the fatigue tensile. Different depths of scratches were preset at different positions of the steel wires in the cables. Aiming to the bundle of the anchor cable, the conventional 1-D AE localization and zonal localization method were combined to localize the micro-crack and rupture of cable. The relationship between AE signal signature and damage was established. The acoustic emission signatures were identified and characterized: (i) friction between cables; (ii) plastic deformation of cable; (iii) rupture. With the localization of AE and typical AE signature, the failure progress can be described. The massive AE signals of plastic deformation can provide the precursor of the cable rupture. AE shows good potential for predicting the healthy status of the prestressed anchor cable.

With the rapid growth of urban population, many footbridges have been used for improving the efficiency of transport. However, the construction process of the traditional footbridge is complex, especially for the urban concentrated area, which may involve massive demolition and renovation of the underground pipeline. Usually, it will involve the high cost and long construction period. Herein, the piers and foundations are essential for the construction of the footbridge. Moreover, the conventional pier requires extensive on-site implementations (e.g. casting-on-site, traffic restriction, demolition of underground infrastructure, etc.) Therefore, this study proposes to a novel modularized design of the pier for the aluminum alloy footbridge to avoid the demolition of the underground facility. For rapid installation of each member, each member was designed as the modular: RC shear walls as main bearing element, steel diagonal bracing and capping beams. With the connection of whole grouted sleeves and built-in bolt, all the members can be easily assembled. And also, all the components can be manufactured in the factory and delivered to the construction field conveniently because of the proposed modular design. Though checking computing of each member of modularized design was conducted analytically, the mechanical and seismic performance were evaluated and analyzed by the scaled experiment. The pseudo-static test was carried out. The seismic performance indicators such as the hysteresis curve, skeleton curve, energy dissipation curve and stiffness degradation curve were obtained; additionally, the damage mode was observed. The experimental results show that the novel modularized design of the pier for the aluminum alloy footbridge has good seismic performance. The proposed design can provide the promising option for the construction of footbridge especially in the urban concentrated area.

Expansion joints are a crucial component of bridges, influencing not only the force state, service performance, and operational safety of the structures but also playing a substantial role in ensuring the comfort and safety of traffic on the bridge deck. This paper investigates the static mechanical responses of bridge modular expansion joints using a full-scale 3D solid finite element (FE) analysis method. Firstly, the working mechanism, force transmission path, and influencing factors of the modular expansion joint are analyzed. Secondly, a full-size 3D solid finite model of the modular expansion joint is established using ANSYS FE software and APDL parametric modeling. The model incorporates the actual structure, force transmission path, contact effects, and boundary conditions of the expansion joint. Then, the load size and action position of typical vehicles on the joint are analyzed to obtain adverse vehicle action. Finally, the force state of the modular expansion joint under vehicle load using the 3D solid FE model is calculated and discussed. This paper proposes a precise FE analysis method for the complex stress state calculation of bridge modular expansion joints, providing theoretical and technical support for damage identification, state evaluation, and safety assessment of such joints, and can also be learned and used for other types of bridge expansion joints.