Curvelet transform-based identification of void diseases in ballastless track by ground-penetrating radar

Abstract

Summary Void disease caused by construction quality and external loads contributes to the failure of reinforced concrete (RC) structures. Ground‐penetrating radars have been used for various applications to detect void diseases beneath RC. However, interferences, such as strong reinforcement bar (rebar) reflection and multiple waves, complicate the identification of internal void diseases in RC. On the basis of these problems, we consider the ballastless track of high‐speed railways as a research object and propose a void‐disease identification algorithm based on the directional features of target echoes. First, typical target echo models of rebar and void in RC were built, and the principal directional features of two kinds of target echoes were discussed. And, then, on the basis of differences between rebar and void echoes in direction, a horizontal filter was constructed to identify the void diseases. Finally, the reliability and robustness of the proposed algorithm were verified through forward simulation and field experiments. Results show that horizontal filters can effectively eliminate principal components in the asymptote direction of rebar echoes, reserve principal components in the horizontal direction of void echoes, and implement void disease detection and identification. This study promotes the application of ground‐penetrating radars to detect ballastless track subgrade diseases in high‐speed railways.

Threshold for Chaos of a Duffing Oscillator with Fractional‐Order Derivative

“…We also found that the system would be less chaotic with the smaller order and larger coefficient of the fractional-order derivative. e fractional-order derivative was introduced to more accurately describe the nonlinear dynamic behaviors of physical objects with fractional-order characteristics, such as viscoelastic materials, damping, dynamic characteristics of gear clearance, friction, and impact in mechanical systems [45][46][47][48].…”

Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

“…Therefore, the real-time monitoring of track slab performance and the identification of the damage condition of track slabs are essential for long-term safe operation of the HSR. Existing track slab defect detection methods can be divided into three categories: visual inspection [15,16], nondestructive testing (NDT) [4,11,[17][18][19][20][21][22], and structural health monitoring (SHM) [23][24][25][26][27]. Most of the visual inspection methods rely on a variety of track inspection equipment, such as road rulers, gauges, and various customized equipment.…”

Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

“…Abdel et al applied four edge detectors for finding the concrete cracks, and a fast Haar transform was identified as the best solution [2]. In [3], curvelet transform is utilized for detecting the void diseases in ballast-less track, which may result in the cracks of the track slab. Hutchinson developed one Canny edge-based crack detection, which utilized a semi-automatic threshold value [4].…”

“…(2) Crack region identification should have a low computing burden and can be quickly updated incrementally. (3) Considering that there may be some background disturbances (e.g., handwriting, etc.) similar to cracks, how to minimize the relevance between cracks and those noises is an important quality of robust crack detection.…”