This PDF file contains the front matter associated with SPIE Proceedings Volume 11592, including the Title Page, Copyright information, and Table of Contents.

Welcome to the 2021 SPIE Smart Structure/Nondestructive Evaluation (SS/NDE) Conference Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV (SSN07)! On behalf of conference co-chairs Dr. H. Felix Wu (U.S. Department of Energy), Professor Peter Shull (Penn State), and Dr. Andrew Gyekenyesi (Ohio Aerospace Institute) and our Program Committee members, I thank you for joining us in this exciting annual event and sharing your cutting-edge research with the colleagues from around the world.

Significant advances were made in recent years in nondestructive evaluation (NDE) technologies’ efficiency for detecting and characterizing deterioration in bridge decks, including advances in automation of NDE data collection, analysis, and interpretation. Those advances enable more extensive and more frequently implemented data collection that will lead to a more objective description of the current condition and a more precise prediction of the progression of deterioration. Also, complementary use of multiple NDE technologies may assist in the identification of likely causes of deterioration. As such, the NDE data are becoming essential for the effective and economic management of bridges, concrete bridge decks in particular. Still, to develop NDE based deterioration and predictive models on both project and network levels, data from multiple surveys over a more extended period are lacking. It has also been shown that the bridge deck performance varies widely, even between bridges that are very close in age and that have similar traffic loads, designs, and climate conditions. It indicates that deterioration processes, since they are a result of multiple inputs and actions, are inherently complex. Therefore, other influences require an examination to provide complete answers regarding disparate bridge deck performance. Complementary use of NDE, structural health monitoring (SHM), and other technologies for local and global assessment of bridges opens opportunities for providing answers to the development of realistic deterioration models and comprehensive evaluation of factors influencing concrete bridge deck performance. The paper concentrates on the discussion of merging of the technologies in the achievement of two specific objectives: assessment of the influence of bridge superstructure on the deck performance using large mobile shakers, and the use of accelerated structural testing for fast and comprehensive development of an understanding of deterioration processes in concrete bridge decks.

Concrete delaminations are commonly-found defects in bridge decks and rigid pavements. While previous studies reported that ground-penetrating radar (GPR) could visualize those defects in many instances, it is still unclear about the factors that may affect such an application. For this reason, this study aimed to develop an understanding of the factors that influence the detectability of concrete delamination in GPR images/signals. Concerning the methodology, the study was conducted using both synthetic data generated from a GPR simulation program, and real data collected on a concrete bridge deck specimen. The analysis of such image data indicated the following. First, there is always some energy reflected from concrete delamination. However, its shape and strength are affected by the thickness of the delamination, the material (air or water) within it, and the peak (most energetic) frequency of the emitted signal. Second, the depth of delamination and its position relative to reinforcing steel bars might impact its visibility in GPR images.

Moisture content in Portland cement concrete structures is commonly associated with many durability problems (e.g., steel corrosion) and can be used in structural health monitoring and nondestructive evaluation/testing (NDE/T) of concrete structures. While several intrusive techniques are available for quantifying moisture content inside concrete structures, it is a challenging task to estimate the moisture content of concrete in the field without using embedded moisture sensors. In this paper, synthetic aperture radar (SAR) imaging as a nonintrusive technique and the K-R-I (curvature-area-amplitude) transform are applied to concrete panel specimens made of two water-to-cement (w/c) ratios (w/c = 0.4 and 0.5) for moisture determination. Concrete panel specimens with dimensions of 0.3 m-by-0.3 m-by-0.05 m were manufactured and conditioned in a laboratory environment by air-drying for three months. Its time-dependent moisture variation was simultaneously monitored by a 10.5- GHz center frequency SAR imaging sensor inside an anechoic chamber. Quantitative analysis of SAR images was carried out by using the K-R-I transform to understand the simultaneous change of SAR amplitude and distribution (contour shape) at different moisture contents. It was found that SAR amplitude and its distribution increase with the increase of moisture content inside concrete panel specimens. Spatial distribution of SAR amplitudes can be used to indicate subsurface moisture distribution inside concrete. The area-amplitude (R-I) curve of SAR images can be used to quantify the relationship between moisture content and its distribution.

Concrete compressive strength is important, yet difficult to quantify without direct testing. In particular, it is difficult to obtain the mature concrete strength measurements which are necessary for safe and optimal use of existing structural capacity. Reliable measurements of mature strength using nondestructive testing methods (NDT) like ultrasonic pulse velocities depend on many factors, including the inherent material variability, sampling frequency, and quality of the NDT measurements. Methods like ground penetrating radar (GPR) and concrete maturity relationships are common for investigating the early-age properties of concrete but are rarely used for mature concrete. Using a case study of a concrete pedestrian bridge where both long term temperature data from structural health monitoring (SHM) and recent GPR surveys of the bridge are available, this work compares the predicted 8-year strength using two different indirect methods. The first uses a regression model trained on laboratory GPR attributes and material properties. The second uses the maturity method to predict strength based on 28-day cylinder tests and the temperature history recorded by the bridge's SHM system. The maturity method predicts the correct relative trends in strength between the two phases and overpredicts the cylinder 28-day strength by 12% 􀀀 25%. The GPR predictions do not reliably capture the relative difference between the two phases, but have similar accuracy and underpredict cylinder strength by 4% 􀀀 22%. These strength comparisons from noninvasive methods motivate further improvements in GPR attribute modeling and integrating these methods with other ultrasonic models to improve spatial resolution and reliability.

The use of Ground Penetrating Radar (GPR) in the Non-Destructive Evaluation (NDE) of structures has significantly increased in the past decades. Several attempts have been made by the researchers to detect the surface and subsurface deterioration of the concrete using GPR. This study aims to analyze the attenuation of the signal in concrete due to various subsurface defect and rebars, simulated in a controlled laboratory environment. Three small plain concrete slabs of surface dimensions of 25 x 50 cm and varying depths of 5 cm, 10 cm, and 20 cm were utilized in this study. The slabs were scanned using a GPR by stacking the slabs one above another to simulate different depths for the detection of the gap between the concrete slabs. Materials such as paper sheets, cardboard sheets, foam sheets, reinforcement bars, and an FRP (Fiber Reinforced Polymer) bar were placed between the concrete slab, and the reflections amplitudes were investigated at the interface medium. An air gap and water gap are introduced between the slabs to simulate defects between slabs which is typically the case for horizontal cracks and delamination. The results obtained are promising as they show a variation in the measured reflected amplitudes for different materials across all depths. This research will be helpful to detect subsurface cracks on the actual structures such as concrete bridge deck and its standardized quantification could be useful to the decision-makers for the monitoring as well as planning the repair and retrofitting of the structures.

The widespread commercial adoption of high-performance, fiber-reinforced composites has pushed research interests toward the next generation of composites. These new composites are tasked with integrating additional functionalities into the structures without causing a trade-off in mechanical performance. One such functionality that has received significant interest is sensing. This is especially important for composites using high-performance fibers (e.g., carbon fiber) because their strain-to-failure is relatively low, resulting in brittle fracture. Besides, fiber damage can be hidden within the composite, potentially leading to premature catastrophic failure if not detected. In prior research, we demonstrated continuous feed-through deposition of ceramic nanoparticles on carbon fiber’s surface that simultaneously enhanced both the piezoresistive response and interlaminar shear strength. In this work, a similar continuous feed-through deposition process was used to demonstrate passive sensing and energy harvesting by integrating ferroelectric microparticles on the surface of electrically nonconductive fibers. The sensing and energy harvesting capabilities were characterized by mechanically straining composite beams and measuring the power generated. The improvements in mechanical properties are shown through interlaminar shear strength tests. Therefore, this research aims to demonstrate a high throughput, commercially scalable approach to coat fibers with ferroelectric microparticles that enable passive sensing as well as improved mechanical performance when fabricated into a fiber-reinforced composite.

Carbon fiber (CF) holds structural self-sensing capability using its electrical resistance so that electromechanical behavior of carbon fiber reinforced plastic (CFRP) was investigated for using CF and carbon-glass hybrid fiber (CGHF). CGHF contains CF in either warp or weft, whereas glass fiber is perpendicularly woven in the other. Electrical resistance of CF monofilament whose diameter is 8 μm was increased when tensile strain was applied in fiber direction, which is called piezoresistive effect. When CF is gathered into a bundle, similar piezoresistive effect was observed. Moreover, distance change between the adjacent CF also led to the resistance change, because the number of electrical contacts can be differed with respect to the tow gap. We call this phenomenon “inter-tow interaction.” Another discriminative electromechanical contact is “inter-ply interaction” which has electromechanical contact between adjacent plies. Likewise, several electromechanical factors hold structural self-sensing capability. Therefore, real-time non-destructive evaluation (NDE) and structural health monitoring (SHM) can be realized with carbon fiber. The CF for the self-sensing can be constituted in various forms in a polymer matrix such as a plain-woven fabric, a uni-directional fabric and a grid. The self-sensing CF grid can be a compromised arrangement between the sensing performance and the material cost. In addition, the self-sensing algorithms of CFs can be comprehended by electrically equivalent circuit models. Reversely, the sensor design can be aided by the equivalent model which contains the aforementioned interactions.

The damage evolution on thin-wall large-scale textile-reinforced cement composite structural elements loaded under bending is tracked in this study using acoustic emission. The monitoring technique has been extensively applied in recent decades for the structural health assessment of voluminous and bulk concrete structures, still its performance and efficacy on thin elements is not extensively investigated. In a well-established approach today, the AE waveform is analyzed based on a series of wave features. Indicatively, the damage progress is tracked based on AE energy and correlated to the mechanical properties (load, displacement). AE source localization detects and pinpoints the zones where cracks and internal debonding occur indicating the damage shift from the middle bending span to the edges of the beam. Based on this observation, the rise time and frequency analyses pinpoint the effect of source-sensor distance on the wave shape. This latter result is discussed considering a preliminary attenuation analysis at intact and damage state. The RA-value and frequency trends are also reported in an attempt to relate the research outcome to the Rilem TC 212-ACD protocol. It is shown that the AE inspection accuracy does not diminish due to thin-wall geometry, still taking into account of the attenuation effect at both intact and damage state is recommended.

As a non-destructive testing (NDT) method, the acoustic-laser technique has demonstrated its effectiveness in defect detection of composite systems. The technique is particularly useful for the detection of near-surface defects in fiber reinforced polymer bonded concrete by vibrating the material with an acoustic excitation and measuring the vibration signals with a laser beam. However, same as the other vibration-based measurement methods, the accuracy of acoustic-laser technique is sensitive towards sampling rate during the measurements. The sampling rate of acoustic-laser measurement adopted in previous studies is as high as 50000 Hz to assure the accuracy of measurement. However, such high sampling rate cannot always be guaranteed due to the limitation of data acquisition system, or any missing data generated during the measurement. In this study, the effect of sampling rates on the accuracy of acoustic-laser technique is investigated through the experimental study. Six sampling rates, i.e. 10 Hz, 100 Hz, 1000 Hz, 10000 Hz, 25000 Hz, and 50000 Hz, are adopted to measure the FRP bonded system with an interfacial defect, in order to study the relationship between the sampling rate and measurement accuracy. Moreover, an upsampling method using machine learning is proposed in this study, in order to reconstruct the missing data to the target sampling rate, so that the accuracy of the detection can be improved from low sampling rate measurement with missing data.

Structural Health Monitoring (SHM) techniques can be classified into global and local monitoring strategies. Regarding the global strategies, the aim is to monitor the structure for any change that can be related to damage globally, whereas, the local damage detection schemes usually aim at detecting damage at a very confined area on the structure. The global techniques, which are also sometimes termed as vibration methods, are very sensitive to Environmental and Operational Variations (EOV). These variations can affect the structural response and, subsequently, mask any changes in vibration signals that can be referred to as damage. It is known that temperature has the most effect on the natural frequencies of any structure. In this paper, an inverse strategy is proposed that aims to predict the temperature variation using frequency time series of the structure. To this end, a Recurrent Neural Networks (RNN) is exploited in which the natural frequency time series obtained from the intact structure along with recorded temperatures are respectively used as training features and label. The trined RNN is then tested on data obtained from the damaged structure. It is shown that the error in predictions increases as the damage occurs. A numerical example is presented to demonstrate the applicability of the proposed method.

We present a two-stage method for detection and quantification of surface defects in concrete bridge decks using a hybrid deep learning and image processing technique. In the first stage, a multi classifier based on an integrated convolutional neural network and long short-term memory architecture is developed to detect cracking and spalling regions. A new algorithm based on denoising and nearest neighbor methods is then developed to quantify the crack length within the detected cracking regions. The proposed method offers an acceptable damage detection and quantification performance on rough concrete surfaces. We highlight various aspects of a software program developed using the proposed method for autonomous inspection of bridge and pavement systems.

Effective rail neutral temperature (RNT) management for continuous welded rail (CWR) is of great importance to the railway industry. RNT is the temperature at which the longitudinal stress of a rail is zero. Due to the natural axial constraint and lack of expansion joints in CWRs, rails can develop internal tensile stresses in cold weather or compressive stresses in warm weather, which can lead to rail fracture or buckling in extreme conditions. In this work, the team proposes a practical and effective method for RNT estimation. First, a contactless non-destructive and non-disrupting sensing technology was developed to collect real-world rail vibrational data, and a series of laboratory data collection is performed for verification. Second, the team established an instrumented field test site at a revenue-service line in the state of Illinois, and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. Third, numerical models were developed to understand and predict the rail track vibration behavior under the influence of temperature and RNT. An excellent agreement (discrepancies less than 0.01%) between model and experimental results were obtained by using an optimization approach. Finally, a supervised machine learning algorithm was developed to estimate RNT using the field-collected rail vibration data. Furthermore, sensitivity studies and error analyses were included in this work. The system performance with field data indicates that the proposed framework can support reasonable RNT prediction accuracy when measurement/model noise is low.

The present study is based on developing a novel in-house BRISK-DIC methodology to identify and correlate natural patterns to perform 3D vibration studies of large structures such as wind turbines. The method includes identifying natural patterns using the BRISK (Binary Robust Invariant Scalable Keypoints) algorithm and its integration with an in-house 3D-DIC method for the measurement of in-plane and out-of-plane displacements. The stereo-calibration is performed using a novel calibration technique that uses an IMU sensor and Laser Measure for extrinsic parameters and a representative computational stereovision system for intrinsic parameters. In a preliminary field experiment, the developed technique is used to study the vibrations of a light tower of 10m height. The BRISK algorithm is applied to the selected reference images from both of the cameras to detect natural patterns. The in-house 3D-DIC program uses the identified natural patterns as well as calibration parameters for correlation and reconstruction purposes. The developed BRISK-DIC method is able to measure 3D displacements as well as accurately obtain the natural frequencies of the light tower. The vibration results are validated using an accelerometer. Finally, in another field experiment, the technique is successfully implemented for 3D vibration study of a utility-scale wind turbine. Natural patterns are identified at the nacelle and correlated to obtain the vibrational parameters of the wind turbine tower. The measured natural frequencies are validated with the measurement carried out in the past using a ground-based microwave interferometer.

Digital image correlation (DIC) is an image registration technique to measure finite three-dimensional shape and deformations of planar and curved surfaces. This technique requires an optimal unique pattern or set of unique localized patterns as a carrier of deformation information in order to accurately measure correlations in temporal images. Recent advances in obtaining an optimal pattern in terms of saliency and uniqueness require operators’ experience and/or prior metrics. In our study, we propose a preprocessing methodology to automatically classify the saliency and uniqueness of a localized pattern for DIC processing of a large structure for structural health monitoring. In order to ensure pattern saliency, we develop a localized multi-scale CNN classifier using an in-house dataset containing 20k unique coarse and fine patterns. This classifier ensures that the projected pattern is salient within a real world image. For ensuring uniqueness within an image and a set of images, we develop a novel uniqueness algorithm that ensures the structural similarity (SSIM) index of the pattern is above a similarity threshold in every part of an image as well as for all subsequent images. We integrate these algorithms as a preprocessing step to our in-house 3D-DIC program for an efficient study of 3D vibrations of large-sized structures. Initial experiments are performed on a large-sized (10m height) light tower, and it is observed that our methodology is capable of optimizing the size, saliency, and uniqueness of a pattern in order to perform efficient displacement measurements for vibrational study and health monitoring purposes.

In many situations real flaws such as tight cracks with known morphology cannot be manufactured in part configuration specimens. Typically, fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or round specimens. If an NDE technique is required to provide a reliably detectable α_90/95 flaw size, then the direct method for qualifying the NDE procedure is to use the appropriate real flaw specimens, run inspection procedure, and perform probability of detection analysis. This is can be described as direct POD demonstration testing which follows guidelines of MIL-HDBK-1823. The POD testing is operator specific. When many operators undertake the same test then conclusions can be drawn regarding percentile of operators meeting detection of certain flaw size with minimum 90%/95% POD/Conf. This paper takes a case where, real flaws are not available in part configuration specimens and a direct POD demonstration study cannot be undertaken because of lack of real flaws. In such situation, general practice is to use artificial flaws in part configuration specimens. Just artificial flaws are not sufficient for this analysis. Additional flaws including some real flaws in same and simpler geometry specimen are required. Signal response data is taken on all sets of artificial and real flaws. Noise is measured on each specimen and signal response variations are measured. The paper provides a procedure to calculate the flaw delectability size using the transfer function analysis. The analysis assumes that the chosen transfer function that relates artificial and real flaw signal responses in real parts and/or simple geometry specimens to compute a90/95 flaw size for real flaws in real parts. Both single-hit and multi-hit flaw detection is addressed.

Surface crack patterns can easily show the behavior of concrete elements and structures. However, visual damage inspection performed by experts is a subjective approach and is prone to inaccuracies. The quantification makes this process objective and more reliable. In that regards, the maximum crack width is used as a practical measurement. It has been shown that the crack width is not a good indicator of damage due to its inherent uncertainties; In fact, it might bounce back after unloading. As such, a dimensionless parameter so-called Fractal Dimension has been introduced as a robust index that can quantify the complexity of crack distribution efficiently. The present study aims to investigate the relation between crack pattern and stiffness loss of RC arch structures. An experimental program, constituting four RC arch specimens, is also conducted and the structural behavior and the surface crack pattern results are captured during the test. The results of analysis show that the Fractal Dimension representation of the crack pattern provide satisfactory correlation with experimentally obtained tangential stiffness and can be recommended for use as a quantized visual damage index.

The paper provides a model for multi-hit limited sample POD analysis for simulated raster scanning that is used for flaw detection in nondestructive evaluation. Raster scanning is used in ultrasonic testing with unfocused and focused transducers. In multi-hit POD analysis, system resolution is taken into account. The scan data, known as C-scan, is represented as a 2D pixel grid. Typically, the pixel gray value is equal to the ultrasonic signal amplitude. The data is taken at every step or index between the pixels. Each pixel represents an area of part that is sampled by the transducer. The sampled area or the aperture provides the signal amplitude. The step size may be equal to or smaller than the aperture, creating either a non-overlapping or overlapping aperture scan pattern. Optimal scanning uses least number of steps or pixels that can provide reliable flaw detection, accurate flaw sizing and adequate spatial resolution for the target size and larger flaws. Here, the scan patterns are defined in relation to the target flaw size, transducer aperture, and step size. In this work, probe field is simulated as symmetrical bivariate standard distribution and distribution and flaw as a square shaped reflector. This arrangement is similar to a raster scan with focused transducer. The optimal scan pattern is determined by simulating a number of scan patterns and comparing their signal amplitude, flaw sizing accuracy, probability of detection and probability of false positive in relation to contrast-to-noise ratio. If results of such simulation are corroborated in empirical data, the model can be used in assessing reliability of flaw detection in NDE as well as for choosing optimal scan pattern.

Gas turbine engines undergo very harsh operating environmental conditions, and this leads to various issues related to components materials strength limitations, degradation, cracking, and other durability problems. Under such circumstances, a robust material design is required to prevent these critical components from failing in service and preventing catastrophic events from taking place. The robust design must enhance component durability, which could be degraded due to material processing defects, variability in material properties, in-service loads, and operating environment. To encounter and manage these durability issues, materials scientists and engineers that are involved in this field along with engine makers are continuously working on developing protective materials to alleviate and increase materials damage tolerance and prevent components failure. Ceramic matrix composites (CMC) are now materials of choice for gas turbine engine design and manufacturing. The CMC has a good capability in operating at high temperatures up to 1500 °C which is within the norm of gas turbine engine operation and it is much lighter compared to metals. Good impact resistance and stability at high operating temperatures make the silicon carbide (SiC) ceramic matrix composite system a desirable option for jet engines [1]. However, CMC’s when they undergo the degradation process that typically includes coating interface oxidation as opposed to a moisture-induced matrix which is generally seen at a higher temperature. Additionally, other factors such as residual stresses, coating process-related flaws, and casting conditions may influence the degradation of their mechanical properties. These durability considerations are being addressed by introducing a highly specialized form of environmental barrier coating (EBC) that is being developed and explored in particular for hightemperature applications greater than 1100 °C [2]. In this paper, a CMC substrate is being evaluated for failure under supportive protection of EBC coatings. The primary aim is to identify the crack propagation phenomenon, the sequence of failure of the EBC and assess the life the CMC substrate. An analytical simulation applying the extended finite element method (XFEM) in ABAQUS software [3] is used to perform this analyses. Analytical results obtained are discussed and checked against test data.

In this presentation, a coupling approach of Voronoi-cell lattice model (VCLM) and finite element method (FEM) is proposed for simulating the fracture behaviors of quasi-brittle solids. By the central role of the Voronoi cell into the elastic uniformity of solid deformations, the finite element is introduced to compliment the Poisson effect. The Delaunay/Voronoi dual tessellations in the mesh construction facilitate the compatibility of the VCLM with the FEM. Therefore, the proposed approach effectively simulates both the volumetric and fracture behaviors. Besides, a coupling strategy of the VCLM domain and the FEM domain is discussed for numerical efficiency.

Cracking of concrete structures is a phenomenon familiar to civil engineers but difficult to interpret and to quantify. While many remote sensing techniques have been proposed for quantifying surface crack properties (e.g., length, width, distribution) on concrete structures with an improved accuracy and efficiency, little has been known about the structural significance of concrete cracks without the knowledge about subsurface crack properties (e.g., depth, orientation, volume). Among several remote sensing techniques, radar (e.g., ground penetrating radar and synthetic aperture radar) imaging is chosen for its superior performance on subsurface sensing. This paper presents a numerical study on the near-field electromagnetic scattering pattern of surface cracks in plate-like structures for determining the optimal inspection angle using the finite difference time domain (FDTD) method. Three artificial cracks (1”-by-1”, 1”-by-0.5”, and 0.5”-by-1”) on a concrete plate (dielectric constant = 5, electrical conductivity = 0.05 S/m) were simulated to develop their near-field scattering response with a transverse electric (TE) wave at a carrier frequency from 8GHz to 18GHz. From the simulated result, a noise criterion (using the signal-to-noise ratio) and a robustness criterion (using standard deviation) are proposed for determining optimal inspection angle. Our simulation result reveals that, with the combined used of two criteria, an optimal inspection angle can be selected from the angular range of 41-deg. (12GHz) to 45-deg. (14GHz).

Intelligent transportation systems (ITS) collect traffic data from various sensors deployed in smart cities. Yet, such information that are ubiquitous in real-world transportation systems mainly suffer from irregular spatial and temporal resolution. Consequently, missing and incomplete traffic data are inevitable as a result of detector and communication malfunctions when collecting information from ITS. Reconstructing missing values is then of great importance yet challenging due to difficulties in capturing spatiotemporal traffic patterns. This paper presents a transportation data reconstruction/imputation model that leverages Bayesian inference and tensor decomposition/completion to effectively address the missing data problem. On this basis, Bayesian tensor train (TT) decomposition is incorporated with Markov chain Monte Carlo Gibbs sampler to learn parameters of the model. Results of the experiments indicate that the proposed Bayesian TT model can effectively impute missing traffic data with acceptable accuracy.

The recent advances in sensor technologies have led to the daunting task of combining the information and robust decision making for damage detection in structural health monitoring (SHM) due to its capabilities of extracting multiple information. The Electromechanical Impedance (EMI) method employs high frequencies range in assessing the local structural response based on structural health monitoring (SHM). This work describes the quantification of the frequency domain on the Al plate using principal component analysis (PCA) based hotteling’s T² damage curve in describing the behaviour of signal. PCA used to reduce multivariable complex data set to lower dimension in order to reveal simplified statistical patterns. The EMI method used damage metrics as a tool to separate quantitatively or qualitative pre-process data of EMI spectra into classes depending on the damage presence, level and location. The information of sensor’s resistance (R) and conductance (G) is studied in the frequency domain and data fusion is realized at the variable level using fused variable F. The proposed methodology is tested and validated for Al material by creating drilled holes.

NDE methods assume capability to detect certain flaw size. Sometimes the capability is validated using probability of detection (POD) demonstration. Although, NDE imagers may highlight indications that meet certain indication detection criteria, the inspection results are evaluated by a certified operator and the operator takes responsibility of the results of his assessment. The operator assesses both i.e. the NDE data of the indication and the visual indication itself. Normally, assessment of NDE data also has a visual component and may also have visual inspection of the part. Visual assessment of image data and visual inspection of part are two kinds of visual flaw detection. Every visual flaw detection has either implicit or explicit flaw detectability size associated with it. Explicit flaw detectability size has POD/confidence associated with it. The paper provides methodology to assess flaw detectability in visual detection. Indication contrast, indication size, background illumination, and noise are important factors in flaw detectability. Visual perception is affected by these factors. Visual acuity of operators is measured using Jaeger chart. Visual resolution is measured by another visual acuity test using line-pair targets. Contrast sensitivity as a function of line-pair frequency can be measured using a line-pair target with sinusoidally varying luminance across the line-pair widths with logarithmically decreasing contrast along length of the line-pairs. Operator contrast sensitivity function, indication contrast-to-noise ratio (CNR) indication size are necessary for assessment of flaw detectability in visual inspection. Alternately, a function called CNR sensitivity function, CNR and new parameter called resolution ratio have been proposed to relate to reliability of detecting indication. In visual assessment of data, important factors to consider are system resolution, resolution of data display monitor, contrast sensitivity, indication contrast, indication size, background illumination, and noise.

Ultrasonic monitoring of fresh cement-based materials is important as pulse speed and attenuation are indicative of the increasing stiffness of the medium, and enable characterization of the curing stage and projections to the mechanical strength from an early age. Despite its importance, practical application is not straightforward due to severe heterogeneity and inherent damping. One crucial parameter in the ultrasonic behavior of fresh cement is the air bubbles, which impose a frequency dependent phase velocity and attenuation, as also observed in all bubbly liquids. In this study, ultrasonic experiments take place in fresh mortar as well as in reference media like water and shampoo. Results show that both shampoo and mortar exhibit strong dispersion relatively to water, seen by the dependence of phase velocity on frequency. Gradually and as bubbles are released due to gravitational settlement (in shampoo) or constrained (hardening of cement) the dispersive trend weakens reaching towards a nearly flat dispersion curve like water. The results highlight the influence of cavities which are considered one of the strongest types of scatterers, while quantification of cement ultrasonic dispersion opens the way for more accurate characterization of the curing behavior.

Guided wave ultrasonic testing (GWUT) has been commercially used in pipelines since the last two decades. T(0,1), the axisymmetric torsional mode, is one of the preferred wave modes in GWUT because it is nondispersive. In this study, the T(0,1) plane wave within a steel pipe is generated using an array of d35 piezoelectric actuators attached around the pipe circumference. A previously designed mechanical lens built of gradient-index phononic crystal is implemented to extend the propagation distance of torsional wave mode by reducing attenuation. The decrease in attenuation is achieved by manipulating the refractive properties of the propagating elastic waves such that the wave energy is focused at a specific point along the pipe. Time-dependent finite element simulations show that the amplitude of the signal received at the focal point is amplified by approximately 80%. Experiments validate the excitation of T(0,1) mode but not the wave focusing as the adhesive layer creates a band gap by changing the dispersion characteristics. Moreover, findings from the numerical studies involving unit cell and full-sized pipe models and are presented. The two-dimensional Fast Fourier Transform on full-pipe models confirms that the pipe with GRIN lens is more sensitive to overall thickness change within the pipe wall, in agreement with the unit cell simulations. The sensitivity of the GRIN lens depends on the location around the pipe circumference in the case of localized defects. Experiments with T(0,1) focusing need to be explored further considering the adhesive.

This paper proposes a new nonlinear ultrasonic technique based on three-wave mixing to generate and measure thirdorder combined harmonics (TOCH) for detecting material nonlinearity in plate-like structures. This technique introduces three input ultrasonic waves with distinct frequencies to a nonlinear structure (e.g., Lattice-anharmonicity). The mutual interaction of these waves generates TOCH at mixing frequencies of input frequency components. The amplitudes of the generated TOCH and the input frequency components are used to estimate the cubic nonlinearity parameter and thereby based on it the associated the material nonlinearity. Here, the cubic nonlinearity parameter is defined as the third- and fourth-order elastic constants of the material. A theoretical model to predict the TOCH due to the mutual interaction of input waves in a nonlinear structure is developed. Further, the experimentally evaluated material nonlinearity of the aluminum specimen is compared with the material nonlinearity estimated by the theoretical model. When the phase matching and non-zero power flux conditions are satisfied, the amplitude of the third-order harmonics was observed to increases steeply with the propagation distance. Because material nonlinearity alters the third- and fourth-order elastic constants of the material, the proposed technique for measuring the TOCH can be used to identify the material nonlinearity more effectively. Attributable to the fact that the material nonlinearity alters the third- and fourth-order elastic constants of the material, the proposed nonlinear three-wave mixing technique is more effective in identifying the material nonlinearity.

The evaluation of hardened concrete quality became significantly important as a result of the continuous and increasing demand on concrete. Mechanical rebound hammer is a quick non-destructive evaluation method used to test the performance of hardened concrete onsite to assure the quality of newly casted concrete or asses the performance of old concrete elements. However, rebound hammer values have uncertainty problems in detecting concrete stiffness and are less reliable. In addition, the device performance is degraded with time due to the fatigue of the mechanical spring. Consequently, this paper presents a new methodology for developing the current measurement technique of Schmidt hammer by using a jerk sensor. Jerk sensors are used in the field of analytical dynamics which includes sensing of acceleration and rate of change of force. The sensor idea is based on using a gyroscope fixed at the free end of a metal cantilever. A cantilever (L-18) of dimensions 18x5x0.5 [mm] and natural frequency 202 [Hz] was designed and modeled using ANSYS finite element analysis (FEA) software. Modal and harmonic analyses were conducted to determine the sensor sensitivity for input jerk. The sensor was applied by constructing a finite element model (FEM) of the mass-spring system inside Schmidt hammer and attaching the sensor to the hammer mass. The correlation relationship between the sensor response and the stiffness modulus of concrete was examined by conducting a transient analysis simulating the Schmidt hammer test of 6 cubic concrete specimens of different grades 20, 30, 37, 45, 50 and 60 [MPa]. Each concrete grade has a different modulus of elasticity. FEA results showed that the L-18 cantilever model has a constant jerk sensitivity of 0.047 [(deg/s)/(G/s)] in the bandwidth 0.1~60 [Hz] and a maximum sensitivity of 1.0 [(deg/sec)/(G/s)] at impact (resonance). The predicted input jerk and output angular velocity are in phase. In addition, the results proved that the stiffness modulus of concrete can be evaluated more efficiently at the impact instant using the rate of change of impact force rather than the rebound distance.

Health state monitoring and prognostics and management of composite were investigated with piezoresistivity data based on the electromechanical behavior of carbon fibers during dual cantilvever bending testing. Crack length in real-time and remaining crack length were calculated with measured electrical resistance. Prediction of crack length was estimated based on prediction result of electromechanical behavior. This research indicated optimized in situ diagnosis and prognosis of carbon fiber reinforced composites with self-sensing data. Self-sensing capability of self-sensing data using electrical resistance was investigated which is applicable to both SHM and PHM.

In recent decades, the use of ultra-high performance concrete (UHPC) has been widely accepted by the construction industry for buildings and bridges. The exceptional properties of UHPC on strength (18 ksi 35 ksi) and durability (freeze-thaw resistance, abrasion resistance, chloride ion penetration resistance) have made it a popular construction material for durable and sustainable civil infrastructure systems. The objective of this paper is to investigate the shortterm mechanical strength development of UHPC specimens using a noncontact synthetic aperture radar (SAR) imaging sensor. UHPC cubes and cylinders were designed and manufactured for nondestructive strength monitoring and kinematic and rheological characterization. Change in moisture content and distribution inside UHPC cylinders was monitored by a laboratory 10-GHz SAR imaging sensor inside a microwave anechoic chamber at UMass Lowell. Hydraulic permeability of UHPC specimens was measured by their bulk electrical resistivity using a concrete resistivity meter (ASTM C1876). The rate of water uptake (absorption or sorptivity) was characterized by an apparatus used to measure the water absorption rate of both the concrete surface and interior concrete (ASTM C1585). Early stage shrinkage behavior of UHPC specimens during the first seven days was also measured using a shrinkage cone. Level of cement hydration in UHPC specimens was quantified by the loss of free water inside UHPC and remotely measured by the SAR imaging sensor. Mechanical strength development in UHPC specimens was monitored by following ASTM C109/C109M. From our preliminary result, it is found that change in SAR amplitude and amplitude distribution can be correlated to the level of strength development.

Improvements to processes and materials have led to increased additive manufacturing capabilities using the fused filament fabrication method in terms of speed, quality, and repeatability. However, there are significant challenges in guaranteeing the desired output quality due to uncertainties inherent to the printing process. These include uncertainties in the quality of raw materials across different batches, fabrication environment (e.g., humidity, temperature), and machine wearing. The widespread adoption of fused filament fabrication for industrial applications faces considerable challenges in reducing part-to-part variations and assuring the mechanical properties of a manufactured component. In this paper, an in situ fault detection platform that considers the structural properties of the printed part is proposed. The presented system uses the optical camera and a deep learning methodology to detect faults online using training sets developed offline. The performance of the system is quantified using a variety of metrics. Computational speed for inference computation, minimum fault-sized detection, and measurement noise in the system are examined in this work.

Distributed sensors have become a great advantage for Structural Health Monitoring (SHM) as they allow for the multiple points measurement using a single sensor. Nevertheless, the installation of this technology can be time-consuming and have an impact on the overall cost of the project. For this reason, this paper explores the application of different techniques for embedding fiber optic cable into textile for Distributed Optical Sensors which could greatly reduce the installation time. This embedding also provides the ability to design sensors with different patterns that enable monitoring structures like pipelines, bridges, and others. In this paper we have identified an embedding technique that does not damage the fiber optic cable. Additionally, the sensors were tested to study their response to temperature and strain by using Brillouin Optical Time Domain Amplification (BOTDA) interrogation technique.