High-precision ultrasound imaging of void defects is critical for the performance and safety assessment of ballastless track structures. The sound propagation velocity of each layer in the ballastless track structure is quite different. However, the traditional concrete Synthetic Aperture Focusing Technique (SAFT) ultrasound imaging method is based on the assumption that the concrete has a single constant shear wave velocity. Thus, it is not a suitable method for the ultrasonic imaging of multilayer structures. In this paper, a Multilayer SAFT high-precision ultrasound imaging method is proposed. It is based on the ray-tracing technique and uses the Fermat principle to find the refraction point that minimizes the delay of the acoustic wave propagation path at the interface of the discrete layers. Then, the acoustic wave propagation path is segmented by the position of the refraction point, and the propagation delay of the ultrasonic wave is obtained segment by segment. Thus, the propagation delay of the ultrasonic wave is obtained one by one, so that the propagation delay of the ultrasonic wave in the multilayer structure can be accurately obtained. Finally, the focused image is obtained according to the SAFT imaging algorithm. The finite element simulation and experimental results show that the Multilayer SAFT imaging method can accurately track the propagation path of the ultrasonic wave in ballastless track structures, as well as accurately calculate the propagation delay of the ultrasonic wave and the lengths of void defects. The high accuracy of the Multilayer SAFT imaging represents a significant improvement compared to traditional SAFT imaging.

Slab track is widely used in many newly built high-speed rail (HSR) lines as it offers many advantages over ballasted tracks. However, in actual operation, slab tracks are subjected to operational and environmental factors, and structural damages are frequently reported. One of the most critical problems is temperature-induced slab-warping deformation (SWD) which can jeopardize the safety of train operation. This paper proposes an automatic slab deformation detection method in light of the track geometry measurement data, which are collected by high-speed track geometry car (HSTGC). The characteristic of track vertical irregularity is first analyzed in both time and frequency domain, and the feature of slab-warping phenomenon is observed. To quantify the severity of SWD, a slab-warping index (SWI) is established based on warping-sensitive feature extraction using discrete wavelet transform (DWT). The performance of the proposed algorithm is verified against visual inspection recorded on four sections of China HSR line, which are constructed with the China Railway Track System II (CRTSII) slab track. The results show that among the 24,806 slabs being assessed, over 94% of the slabs with warping deformation can be successfully identified by the proposed detection method. This study is expected to provide guidance for efficiently detecting and locating slab track defects, taking advantage of the massive track inspection data.

Surface cracks are typical defects in high-speed rail (HSR) slab tracks, which can cause structural deterioration and reduce the service reliability of the track system. However, the question of how to effectively detect and quantify the surface cracks remains unsolved at present. In this paper, a novel crack-detection method based on infrared thermography is adopted to quantify surface cracks on rail-track slabs. In this method, the thermogram of a track slab acquired by an infrared camera is first processed with the non-subsampled contourlet transform (NSCT)-based image-enhancement algorithm, and the crack is located via an edge-detection algorithm. Next, to quantitatively detect the surface crack, a pixel-locating method is proposed, whereby the crack width, length, and area can be obtained. Lastly, the detection accuracy of the proposed method at different temperatures is verified against a laboratory test, in which a scale model of the slab is poured and a temperature-controlled cabinet is used to control the temperature-change process. The results show that the proposed method can effectively enhance the edge details of the surface cracks in the image and that the crack area can be effectively extracted; the accuracy of the quantification of the crack width can reach 99%, whilst the accuracy of the quantification of the crack length and area is 85%, which essentially meets the requirements of HSR-slab-track inspection. This research could open the possibility of the application of IRT-based track slab inspection in HSR operations to enhance the efficiency of defect detection.

The reliability assessment of rail infrastructure is directly related to the safety and effectiveness of railway transportation and critical for railway management department. This paper presents an approach for service reliability analysis of slab track from the point of view of the vehicle-track system operational safety. The vehicle-track dynamics is simulated by the established finite element model (FEM) and the limit state function (LSF) is defined on the danger of derailment. To reflect the real state of track geometric condition, the track irregularity spectrums (TISs) are extracted from the measured track irregularity data and fitted by the seven-parameter formula. Then, a set of time series can be obtained from the TISs using a binary wavelet-based inversion method and then input to the FEM to obtain the value of LSF. Finally, Monte Carlo simulation (MCS) is used in the calculation of track reliability. To overcome the slow convergence of the vehicle-track models, this study develops a surrogate model based on support vector machine (SVM). It is validated that the established SVM can well approximate the relationship between the track irregularities to the derailment coefficients in terms of small error and high correlation. More importantly, the efficiency is more than 1000 times higher than traditional FEM. This is a pioneering study to incorporate vehicle-track dynamics into the reliability assessment of slab track in service. For a 1024-m section of slab track, results show that its reliability index can satisfy the requirement in specifications.

The deformation of longitudinally coupled prefabricated slab track (LCPST) due to high temperature may lead to a reduction in ride comfort and safety in high-speed rail (HSR) operation. It is thus critical to understand and track the development of such defects. This study develops an online monitoring system to analyze LCPST deformation at different slab depths under various temperatures. The trackside system, powered by solar energy with STM8L core that is ultra-low in energy consumption, is used to collect data of LCPST deformation and temperature level uninterruptedly. With canonical correlation analysis, it is found that LCPST deformation presents similar periodic variation to yearly temperature fluctuation and large longitudinal force may be generated as heat accumulates in summer, thereby causing track defects. Then the distribution of temperature and deformation data is categorized based on fuzzy c-means clustering. Through the distribution analysis, it is suggested that slab inspection can be shortened to 6 hours, i.e. from 10:00 am to 4:00 pm, reducing 14.3% track inspection workload from the current practice. The price of workload reduction is only a 2% chance of missed detection of slab deformation. The finding of this research can be used to enhance LCPST monitoring efficiency and reduce interruption to HSR operation, which is an essential step in promoting reliable and cost-effective track service.

The surface crack on track slab is the main cause of the service reliability deterioration of the high‐speed rail (HSR) slab track. To realize the rapid and accurate detection of these cracks, infrared thermography (IRT) can be regarded as an effective way in HSR application. However, the performance of IRT can be significantly affected by the testing environment. This study aims to analyze the feasibility of IRT in crack detection for HSR slab track by investigating the key factors affecting the detection accuracy. Firstly, the principle of IRT in detecting surface crack of track slab is given, and the key factors influencing the detection performance are discussed. Considering the difficulty in conducting field tests for slab tracks, this study establishes a finite element (FE) model to simulate the temperature field of track slab with surface cracks. This model is verified by using the field measurement results of track slab temperature. With the FE model, the effects of detection time, ambient temperature, and sensitivity of the thermal imager on the detection accuracy are revealed. The results show that, during the window time for track maintenance (0:00 am–3:00 am), the difference of temperature between crack areas and noncrack areas gradually increases with time, so it is recommended to conduct detection during 2:00–3:00 am; under the condition of no auxiliary heat source, the crack detection is suggested to be arranged in summer with a high incidence of crack; the minimum limit of thermal sensitivity of thermal imager is below 108mk@25°C. The findings in this research can offer guidance for the implementation of IRT‐based crack detection for HSR slab crack.