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During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

High-speed railway track defects such as rail corrugation, wheel polygonal wear, and rail fastener clip failure are closely related to the modal frequency of the track structure. As the operation time increases, the modal parameters of the track structure will change, and it is necessary to know what they are to diagnose the vibration characteristics. Present methods of modal analysis are limited by measuring points and excitations and cannot be used to extract the operational parameters of the railway track. This paper proposes a novel approach using wheel-rail excitations to identify the rail modal frequency from vibration monitoring data. The approach decomposes the rail acceleration after the wheel-rail excitation passes into intrinsic mode functions and extracts their instantaneous frequencies. The modal frequencies of the rail can be identified from the instantaneous frequencies. To demonstrate the feasibility of the approach, the results using the proposed approach are compared with the modal frequencies of the rail identified using the eigensystem realization algorithm. Then the approach is applied to study the influence of the number of wheel-rail excitations and the number of monitoring points on the identification results in high-speed railway ballastless track rail. The paper concludes that the proposed approach can provide a reliable solution for the identification of the operational modal frequency of track rails based on the monitoring data. INDEX TERMS High-speed railway track, operational modal frequency, synchrosqueezed wavelet transform, variational mode decomposition.

The interface crack of a slab track is a fracture of mixed-mode that experiences a complex loading–unloading–reloading process. A reasonable simulation of the interaction between the layers of slab tracks is the key to studying the interface crack. However, the existing models of interface disease of slab track have problems, such as the stress oscillation of the crack tip and self-repairing, which do not simulate the mixed mode of interface cracks accurately. Aiming at these shortcomings, we propose an improved cohesive zone model combined with an unloading/reloading relationship based on the original Park–Paulino–Roesler (PPR) model in this paper. It is shown that the improved model guaranteed the consistency of the cohesive constitutive model and described the mixed-mode fracture better. This conclusion is based on the assessment of work-of-separation and the simulation of the mixed-mode bending test. Through the test of loading, unloading, and reloading, we observed that the improved unloading/reloading relationship effectively eliminated the issue of self-repairing and preserved all essential features. The proposed model provides a tool for the study of interface cracking mechanism of ballastless tracks and theoretical guidance for the monitoring, maintenance, and repair of layer defects, such as interfacial cracks and slab arches.

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