This article focuses on the acoustic emission (AE) monitoring of a new type of mass timber buckling restrained brace (T-BRB), consisting of a wood casing made of mass plywood panel and a steel core embedded in the casing, for improving seismic resilience of mass timber structures. The casing secures the steel core through a series of bolts. To investigate the failure mechanisms of T-BRBs, two cyclic loading tests are conducted on a mass timber frame braced by T-BRBs with and without carbon fiber-reinforced polymer wraps, and an AE system is deployed for structural health monitoring (SHM). Multiple AE signatures are analyzed to infer the yielding point and failure modes of the T-BRB. AE features, including AE hit rate and cumulative energy, can successfully identify the ultimate structural failures. Results from AE hits and measured area of rectified signal envelope (MARSE) per quarter cycle suggest that the first significant feature values correspond to the cycle when the embedded steel core yielded. The b-value analysis demonstrates a general decreasing trend through the loading process and can characterize the evolution of structural damages. By evaluating both the strain energy and AE energy to reflect damage severity, the sentry function can identify the load cycle of steel core yielding and ultimate structural failure. As the first work applying AE monitoring on structural experiments of T-BRB, this research provides general guidances for SHM of T-BRB braced mass timber structures.

We observe maxon-like dispersion of ultrasonic guided waves in elastic metamaterials consisting of a rectangular beam and an array of cylindrical resonators. The pillars act as asymmetric resonators that induce a strong modal hybridization. We experimentally observe the strongly localized maxon mode with zero group velocity. Our study also demonstrates a unique feature of the maxon with a down-shifting peak frequency in space. To reveal the fundamental mechanism, we conduct comprehensive numerical studies on all frieze group symmetries and key geometric parameters.

Rail breakage can cause train derailment that leads to catastrophic consequences, prevention of which is worthy of a significant amount of investment in finance and time. Non-destructive evaluation can provide qualitative and quantitative inspection of rail tracks in-situ to detect dangerous faults, even though these defects are primarily invisible or hardly noticed from the surface of the rail. To make it efficient and convenient, infrared thermography technique offers a remote operation without interfering with rail transportation. The image processing for edge detection can further quantify the defect deterioration by depicting the contour of the defect. Moreover, the optimal parameters for various edge operators provide reliable detection for natural fracture defects.

Thermographic techniques are widely used to characterise material damage, porosity and moisture in structures. Due to the thermographic signal reconstruction (TSR) technique, active thermography has made significant progress. In this study, the TSR technique was applied to identify a rubber layer that mimics an internal defect, which is attached to the back of the steel plate, after a 1.0 min thermal stimulation. The peaks of the first and second derivative logarithmic curves correspond to the important time point revealing the position of the attached flaw. Furthermore, a new post-processing technique for infrared thermographic data was proposed by exploiting polynomial orders of the fitting curves to produce a synthetic image, which can highlight the flaw with distinctive contrast. Finally, the upgraded TSR technique was applied to image a natural shelling defect in a rail sample with an excellent contrast.

This study showcases the electromechanical impedance (EMI) technique for extracting and promoting zero-group velocity (ZGV) and cutoff frequency resonances in a waveguide structure. We identify the mechanisms of multiple resonances in the EMI spectra via a wave propagation perspective. Both simulation and experiments reveal the fact that sharp resonances in the conductance spectra are associated with either ZGV or cutoff frequency points. Consequently, we design four test configurations to enhance local resonances by aligning induced motions with considered mode shapes. Reasonable agreement between simulation and experiment results is observed. We evaluate the performance of considered configurations in terms of mode enhancement, and configurations that can selectively promote certain mode families are summarized. This study also shines the light on the EMI technique for quantitative non-destructive evaluation (NDE) by potentially supporting the inverse characterization of mechanical properties of host structures.

Local resonances, formed by zero-group velocity (ZGV) and cutoff frequency points, have been extensively studied using impulse-based approaches, such as pulse laser and impact echo. In this work, we showcase the electromechanical impedance (EMI) technique as an option to extract and promote zero-group velocity and cutoff frequency resonances in a waveguide structure. We identify the mechanisms of multiple resonances in the EMI spectra via a wave propagation perspective. Furthermore, we extract the dynamic response profiles at a cutoff frequency and a ZGV frequency to confirm the localized minimum frequency behavior within corresponding branches.

Unfavorable roadway conditions, such as slippery roads, can negatively affect the safety of highway transportation. We aimed to develop a convenient tool capable of evaluating multi-lane road slippery conditions in winter seasons. In this work, field data collection using a dual-spectrum camera was first performed at a field site in the state of Utah, U.S. We analyzed optical and infrared images covering a field of view over three lanes through two snowstorms. Image processing techniques, including image registration, morphological operation, and segmentation, were implemented on both types of images collected under different illumination and temperature conditions. Moreover, the ratio of snow-covered pixels was computed to quantify the snow coverage rate of individual lanes. Finally, we verified the system performance by comparing our estimation with the ground truth via a confusion matrix. The high accuracy, precision, true positive rate, and true negative rate suggest the developed approach can support satisfactory performance for roadway snow detection. Besides, the performance of the unsupervised k-means clustering algorithm and supervised support vector machine (SVM) were evaluated on a dataset of 22 optical images and 19 infrared images. Both the k-means clustering and SVM can support a reasonable image segmentation for roadway snow coverage estimation. Thus, the developed technique offers the potential to facilitate local agencies' decision-making on snow-plowing resource planning and performance evaluation and support winter safety for connected vehicles.

Measuring rail thermal stresses or rail neutral temperature (RNT) in continuous welded rails (CWRs) is a challenging task for the railroad industry, especially in a nondestructive and nondisruptive manner. This paper examines the potential of local resonances for thermal stress measurement in rails. Local resonances associated with zero-group velocity (ZGV) and cutoff frequency points usually demonstrate sharp resonances in amplitude spectra, which can be utilized for Nondestructive Evaluation (NDE) and Structural Health Monitoring (SHM), and we previously reported their existence in rails. In this study, we promote local resonances by bonded piezoelectric elements on a short rail sample. Two tests were performed: (i) the sample is subjected to stepwise increasing uniaxial compressive loads in a constant temperature environment, and (ii) the same sample is free to expand and subjected to rising temperatures in an oven. By measuring local resonant frequencies, we quantified the sensitivity of the resonant frequencies to axial stress and temperature. The results show that appreciable sensitivities of the local resonances are found under varying stress and temperature levels and can be utilized for in-situ rail thermal stress determination.

The deficiency of rail stress management in continuous welded rails could lead to rail buckling or pull-apart, which can seriously jeopardize rail safety. Federal Rail Administration (FRA) safety report indicates “track alignment irregularities (buckled/sun kink)” is one of the leading causes within the track-related category for rail accidents [1]. Therefore, managing risks of rail buckling and pull-apart due to thermal stress is important to ensure rail safety. In the past decades, significant effort has been invested by both industry and academic communities for rail axial stress estimation [2]. The existence of local resonances formed by zero-group velocity (ZGV) and cutoff frequency points in rails has been verified, which can be utilized to monitor local structural conditions [3,4], such as axial loads. In this work, the axial loads’ effect on local resonances in rails was investigated through experimental studies.

Rail defects, especially transverse defects (TDs), can pose risks to safe and efficient railroad operations. Effective rail defect detection is critical for the prevention of broken rail-induced accidents and derailments. In this study, a deep autoencoder (DAE) rail defect detection framework is developed to process ultrasonic A-scan data collected by a roller search unit and to identify the presence of TDs in rail samples. An autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that significantly deviate from the remaining observations and can be used for rail defect detection. Ultrasonic A-scan signals collected from both pristine and damaged rail segments are analyzed, where the pristine dataset is used to train a DAE model. To improve the accuracy and sensitivity of defect detection, we optimize the architecture and hyperparameters of the DAE model. Moreover, we evaluate the performance of two features extracted from the DAE model through receiver operating characteristic curves and confusion matrix. The DAE features outperformed conventional knowledge-driven features in the accuracy and robustness of defect detection, especially with the presence of noise.

In-situ thermal stress determination in structures is a challenging experimental mechanics task, especially if it requires a nondestructive approach. Thermal stress measurement and management of continuous welded rail (CWR) have become more important for railroad maintenance. Local resonances formed by zero-group velocity (ZGV) and cutoff frequency points usually demonstrate sharp resonance peaks in frequency spectra, which can be utilized for nondestructive evaluation (NDE) and Structural Health Monitoring (SHM). This paper examines the potential of the local resonances to provide an estimation of axial stress in rail structures. The local resonances are generated by bonding a piezoelectric element on the rail samples. A 610-mm rail sample was tested, and different axial stress levels were applied by an MTS tensile-compression machine and by measuring the local resonance signature in selected frequency bands to study sensitivity to axial stress of local resonances. The results show that appreciable sensitivities of the local resonances are found under varying stress levels and can be further utilized for in-situ thermal stress determination for rails.

Important characteristics of a zero-group velocity (ZGV) mode in a standard rail are investigated through numerical simulation and experiment. First, the semi-analytical finite element analysis is implemented to compute dispersion curves for the rail structure and the first ZGV point is identified. Backward waves are identified through opposing senses of group and phase velocities. Next, a time-dependent finite element model is used to understand the dynamic response of the rail. Finally, experimental measurements confirm that ZGV modes in rail structures are formed through interferences between two opposite-traveling waves, which is analogous to the S1-S2b ZGV Lamb mode in plate structures.

Thermal buckling of continuous welded rail (CWR) has been a long-standing challenge for the railroad industry because of the high derailment rate and the associated social, economic, and environmental impacts it causes. Rail buckling is generally attributed to excessive thermally-induced axial compressive stress developed in the rail from high temperatures. Knowing the rail thermal stress or its rail neutral temperature (RNT) is critical for safe and efficient rail system operation. There has been great interest and much work on the development of nondestructive evaluation (NDE) techniques to estimate rail thermal stress and RNT in situ. This paper reviews the findings and conclusions from research about NDE approaches for estimating rail thermal stress or RNT, emphasizing the physical phenomena and performance interpretation related to each of the approaches. We identify the type of reference measurements each technique relies on and tabulate this information showing key assumptions, performance, and limitations for each technique.

Local resonances formed by zero-group velocity (ZGV) and cutoff frequency points usually demonstrate sharp resonance peaks in frequency spectra, which can be utilized for nondestructive evaluation (NDE) and Structural Health Monitoring (SHM). The existence and application of those local resonances have been extensively reported in plate and pipe structures. However, local resonances in rails are rarely studied. The team recently reported that impulse dynamic tests can promote the local resonances in rails up to 40 kHz, and the results were verified using both semi-analytical finite element (SAFE) analysis and frequency-domain fully discretized finite element analysis. In this work, we present the discovery of ZGV modes and cutoff frequency resonances in free rails up to 80 kHz using piezoelectric elements. A miniature low-cost PZT patch works as a consistent excitation source compared with the impulse dynamic testing method. First, we implement the SAFE analysis to compute dispersion curves of a standard AREMA 115RE rail and to identify potential ZGV and cutoff frequency points up to 80 kHz. Then, to understand the existence and detectability of identified ZGV and cutoff points in a free rail, we install one PZT patch on the side of the rail head. A chirp signal covering 20 to 120 kHz is selected as the excitation to cover the desired frequency range. Finally, we perform a spatial sampling of wave propagation using three receivers along the wave propagation direction to calculate the dispersion relations experimentally via two-dimensional Fourier Transforms (2D-FFT). This study verifies the existence of ZGV modes in free rail up to 80 kHz and demonstrates the feasibility of using piezoelectric elements to generate local resonances.

Safety is the principal concern of the railway industry, and rail internal defects can pose significant risks to safe and efficient railroad operations. Effective rail flaw detection, especially for transverse defects, is critical to prevent accidents and derailments due to broken rails. Both industrial and research communities have invested much effort in solving this problem. For example, the rail industry relies heavily on ultrasonic bulk waves and, more recently, began exploring ultrasonic guided waves for rail internal defect detection. This study developed an improved semi-supervised learning algorithm based on a deep autoencoder (DAE) for ultrasound-based rail flaw detection. The DAE algorithm identifies observations in a dataset that deviate significantly from the remaining observations. First, the team trained a DAE to reconstruct ultrasonic signals obtained from clean rail segments. To improve the robustness for defect detection, we then optimized the architecture and hyperparameters of the DAE models. Also, we adopted mean squared error (MSE) as a feature to highlight rail defects. Lastly, the team fed the test set of ultrasonic signals into the trained DAE model and evaluated its capability for rail defect detection. We found the proposed DAE can support a superior and robust ultrasonic rail defect detection capability compared to conventional knowledge-driven approaches.

Ultrasonic guided waves are of practical interests for nondestructive evaluation (NDE) and structural health monitoring (SHM) since users can promote desirable wave modes for damage detection, thickness measurement, surface condition characterization, stress measurement, and so on. This study focuses on demonstrating the existence of zero group velocity (ZGV) modes for guided waves in free rails. First, the team computed dispersion curves of AREMA standard rails to identify ZGV points through semi-analytical finite element analysis (SAFE). Second, finite element models were established to spatially sample wave propagation in free rails for wavenumberfrequency domain analysis. The results of finite element simulations were compared with dispersion curves produced by SAFE, and multiple points were identified with vanishing group velocity at non-zero wavenumbers. And resonances with positive and negative wavenumbers revealed that the observed standing waves phenomenon results from the interference of two traveling waves propagating with opposite directions. Our observation and developed methodology have potential applications for rail defect detection, support condition assessment, and rail stress measurement.

Rail internal defects such as detail fracture and transverse fissure are among the leading causes of track-related railway accidents. Therefore, it is critical to develop effective rail defect inspection systems and data processing methods to prevent catastrophic accidents and derailments. This study developed an anomaly detection framework using deep autoencoder (DAE) for rail defect detection. And the team evaluated its performance based on data collected by a prototype passive acoustic rail inspection system. Autoencoder is a semi-supervised learning algorithm that identifies observations in a dataset that deviate significantly from the remaining data. First, the team performed data cleaning and transfer function reconstruction using a dataset collected at the Federal Railroad Administration’s Transportation Technology Center in Pueblo, Colorado. Then, handcrafted or knowledge-driven features were extracted from the transfer functions and fed into a statistical outlier analysis as the benchmark. Also, reconstructed transfer functions at clean rail segments were directly used as the input to train and validate the DAE algorithm. The results demonstrated the effectiveness of DAE for structural discontinuity detection and showed promise for rail flaw detection.

Longitudinal rail force management of continuous welded rail (CWR) is important for safe and efficient railroad operation. A key parameter to measure and monitor is the rail neutral temperature (RNT) or the stress-free temperature. The team proposed a supervised learning framework to estimate the RNT using impulse vibrational responses from CWRs. We first established an instrumented field site on a revenue-service line and collected impulse vibrational response data covering a wide range of temperature and thermal stress. Then, we trained a data-driven model that uses rail temperatures and modal frequencies as the input for in-situ RNT prediction. The results demonstrated that the proposed framework could provide RNT estimation with a reasonable precision (±5 ºF)

With increasingly frequent extreme heat events over the past half century, thermal stress measurement and management of continuous welded rail (CWR) have become more important for railroad maintenance. Methods, including visual inspections and rail lifting, are routinely performed in railroad networks of the U.S. to prevent rail thermal buckling. When intervention becomes necessary, a rail distressing process, involving rail cutting and welding, will be performed to re-establish the zero-stress state at a desirable temperature. And the temperature at which the rail is stress-free is defined as rail neutral temperature (RNT). In this work, an RNT predictive tool that exploits zero group velocity (ZGV) modes and machine learning is proposed. First, the existence of ZGV modes in CWR is investigated through numerical simulation, using both semianalytical finite element analysis (SAFE) and finite element (FE) models. Further, parametric studies are performed to quantify the effect of axial loads and rail temperature on ZGV modes. Additionally, the team established an instrumented field test site at a revenue-service line and performed multi-day data collection to cover a wide range of temperature and thermal stress levels. FE models were calibrated based on the field-collected vibrational data via a linear program optimization approach and an excellent agreement between model and experimental results was obtained. Finally, a supervised learning framework was developed to estimate the RNT using rail temperature and resonance frequencies as the inputs. The results show that the proposed framework can provide RNT estimation with reasonable accuracy (±5 ºF) when measurement noise is low.

Zero-group velocity (ZGV) modes in rails are studied through simulation and experiments in this work. Local resonances associated with ZGV modes appear as distinct, sharp peaks in the frequency amplitude spectrum, whose resonant frequencies can serve as indicators of the local structural integrity condition of the rail itself, assuming that one can excite, detect, and identify wave mode type with confidence. To better understand these interesting modes, semi-analytical finite element (SAFE) analysis is implemented to compute dispersion curves of a standard rail and to identify potential ZGV points. A fully discretized Finite Element (FDFE) model then simulates responses of a free rail when subjected to impulse-based dynamic testing. Experimental impulse vibration data are collected from a 25-m rail with multiple impact-receiver configurations to understand the detectability and excitability of specific resonances associated with ZGV modes in rails. Spatial sampling of wave disturbance is performed to calculate the dispersion relations experimentally via two-dimensional Fourier Transforms (2D-FFT). The excellent agreement between simulation and experimental results confirms the existence of ZGV modes and cutoff frequency resonances in rails and verifies the feasibility of using impulse-based dynamic tests for the promotion of ZGV modes.