Ballastless track support deterioration evaluation using machine learning

Jessada Sresakoolchai, Ting Li, Sakdirat Kaewunruen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Ballastless tracks have been widely used for highspeed rail systems globally since their maintenance is relatively minimal. However, support deterioration right beneath the inbetween slabs’ connectors has been usually reported and quite well known in the industry. Any water ingress can quickly undermine the condition of cement-stabilized soil that supports the track slabs. It is thus very crucial to very early detect the impaired condition of the slab supports since mudded support can result in poor ride quality and eventually endanger highspeed train operations. Therefore, the ability to predict the deterioration of track slab supports is highly beneficial to predictive and preventative maintenance in practice. In this study, track slab support stiffness is considered as a precursor to identify the severity of deterioration. The nonlinear FE models, which were validated by field measurements, have been used to populate data in order to develop machine learning models capable of evaluating the track support deterioration. Axle box accelerations are adopted in a form of datasets for machine learning models. Parametric studies have yielded a diverse range of datasets considering the train speed variations, train axle loads, and irregularities. The results demonstrate that the machine learning models can reasonably diagnose the condition of the track slab supports. The outcome reveals the potential of machine learning to evaluate ballastless track support deterioration in practice, which will be beneficial for railway maintenance.
Original languageEnglish
Title of host publicationProceedings of The 17th East Asian-Pacific Conference on Structural Engineering and Construction, 2022
Subtitle of host publicationEASEC-17, Singapore
EditorsGuoqing Geng, Xudong Qian, Leong Hien Poh, Sze Dai Pang
Place of PublicationSingapore
PublisherSpringer
Pages1455–1463
Number of pages9
Edition1
ISBN (Electronic)9789811973314
ISBN (Print)9789811973307, 9789811973338
DOIs
Publication statusPublished - 14 Mar 2023
EventThe 17th East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-17): EASEC-17 - Singapore, Singapore, Singapore
Duration: 27 Jun 202230 Jun 2022
https://easec-17.org/

Publication series

NameLecture Notes in Civil Engineering
PublisherSpringer
Volume302
ISSN (Print)2366-2557
ISSN (Electronic)2366-2565

Conference

ConferenceThe 17th East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-17)
Abbreviated titleEASEC-17
Country/TerritorySingapore
CitySingapore
Period27/06/2230/06/22
Internet address

Keywords

  • Ballastless track
  • Deterioration
  • Machine learning
  • Finite element modeling
  • Condition monitoring

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