A study on evaluating supporting condition of railway track slab with impact acoustics and non-defective machine learning

Slab tracks are main ballastless tracks used in the high-speed railway system in Japan (Shinkansen). The track slab is a reinforced or pre-stressed concrete component in the slab track. This component is important to support traffic loading stably. There is a need to maintain the supporting condition of track slabs sustainably in the future because it has been passed nearly 50 years since the earliest of slab tracks have laid. Therefore, non-destructive testing methods are needed to evaluating this condition of track slabs. The authors have been focused on non-destructive testing with impact acoustics. In this study, the authors improve this non-destructive testing by experiments in the full-scale models, eigenvalue analysis and non-defective machine learning. The authors have obtained the conclusion in this study as below: (1) Characteristics of impact acoustics depend on hammering position. For example, In the case of hammering at near the corner, impact acoustics is larger and damping more slowly than the case of hammering the center of long side. (2) It is desirable to measure near the hammering point or distance from not more than around 3 times the thickness of a track slab. (3) Principal component analysis and Autoencoder are useful for evaluating supporting conditions of track slabs. Both methods can discriminate the supported area and the voided one with accuracy rates of 81.92 % and 79.51 %, respectively.

• Record URL:
  https://doi.org/10.1016/j.conbuildmat.2023.130905
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0950061823006177
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09500618
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Inaba, Kohko
  • 0000-0002-9019-7892
  • Tanigawa, Hikaru
  • Naito, Hideki
• Publication Date: 2023-4-10

Language

• English

Media Info

• Media Type: Web
• Features: Figures; Illustrations; Photos; References; Tables;
• Pagination: 130905
• Serial:
  • Construction and Building Materials
  • Volume: 373
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0950-0618
  • Serial URL: http://www.sciencedirect.com/science/journal/09500618?sdc=1

Subject/Index Terms

• TRT Terms: Acoustic emission tests; Concrete slab track; Condition surveys; High speed rail; Machine learning; Railroad tracks
• Geographic Terms: Japan
• Subject Areas: Data and Information Technology; Maintenance and Preservation; Railroads;

Filing Info

• Accession Number: 01877986
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2023 9:32AM