Technical Papers
Mar 18, 2022

Mud Pumping Defect Detection of High-Speed Rail Slab Track Based on Track Geometry Data

Publication: Journal of Transportation Engineering, Part A: Systems
Volume 148, Issue 6

Abstract

Mud pumping is a major problem for high-speed rail (HSR) slab tracks because it can cause deterioration of track alignment and further compromise operation safety and ride comfort of passengers. Timely inspection for mud pumping is crucial to the operational safety of high-speed rail networks. This paper proposes a novel detection method for mud pumping defects based on the measurement data of track geometry. The time and frequency features of the surface irregularity signal corresponding to the mud pumping sites were first analyzed. The concave anomalies reflecting the features of mud pumping and the peaks at the wavelength of 5 m and its octave on the power spectrum density (PSD) curve were revealed. On this basis, to automatically detect mud pumping defects, a multiscale signal decomposition method was employed and defect-sensitive features were extracted. A mud pumping index (MPI) indicating the severity of mud pumping was then established, and a three-level management scheme for mud pumping maintenance was developed accordingly. The performance of the proposed detection method was verified against visual inspection recorded on an in-service HSR line, which was constructed with the China Railway Track System (CRTS) I slab track. The results show that the proposed detection method can effectively locate and assess the mud pumping defects with acceptable accuracy for HSR track maintenance needs.

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Data Availability Statement

All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to appreciate the funding support by (1) the National Natural Science Foundation of China (Grant Nos. 52178430 and 51978393), (2) the Science and Technology Program of Gansu Province, China (Grant No. 19ZD2FA001), and (3) Ordinary University Engineering Technology Development Center Project of Guangdong Province (Grant No. 2019GCZX006).

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Information & Authors

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Published In

Go to Journal of Transportation Engineering, Part A: Systems
Journal of Transportation Engineering, Part A: Systems
Volume 148Issue 6June 2022

History

Received: Oct 25, 2021
Accepted: Jan 31, 2022
Published online: Mar 18, 2022
Published in print: Jun 1, 2022
Discussion open until: Aug 18, 2022

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Authors

Affiliations

Zai-Wei Li, Ph.D. [email protected]
Associate Professor, School of Urban Rail Transportation, Shanghai Univ. of Engineering Science, 333 Longteng Rd., Songjiang, Shanghai 201620, China. Email: [email protected]
Assistant Professor, College of Urban Transportation and Logistics, Shenzhen Technology Univ., 3002 Lantian Rd., Pingshan, Shenzhen 518118, China (corresponding author). ORCID: https://orcid.org/0000-0002-1360-7202. Email: [email protected]
Fei Yang, Ph.D. [email protected]
Senior Engineer, Infrastructure Inspection Research Center, China Academy of Railway Sciences, 2 Daliushu Rd., Haidian, Beijing 100081, China. Email: [email protected]
Li-Wen Zhang [email protected]
Engineer, Shanghai SEARI Intelligent System Co., Ltd., 505 Wuning Rd., Putuo, Shanghai 200063, China. Email: [email protected]

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Cited by

  • Feature analysis of precipitation-induced subgrade defects on a high-speed rail ballasted track using multiple track inspection data: A case study, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 10.1177/09544097241234094, (2024).
  • Full-image acquisition and analysis of hot heavy rail incorporating multiple CCD, Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023), 10.1117/12.2688729, (25), (2023).
  • PDDD-Net: Defect Detection Network Based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling, IEEE Access, 10.1109/ACCESS.2023.3343566, 11, (141764-141775), (2023).
  • Differential Deformation Identification of High-Speed Railway Substructures Based on Dynamic Inspection of Longitudinal Level, Sensors, 10.3390/s23010219, 23, 1, (219), (2022).
  • Characterizing Particle-Scale Acceleration of Mud-Pumping Ballast Bed of Heavy-Haul Railway Subjected to Maintenance Operations, Sensors, 10.3390/s22166177, 22, 16, (6177), (2022).

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