Prediction of mud pumping in railway track using in-service train data

Zeng, C; Huang, J; Xie, J; Zhang, B; Indraratna, B

Prediction of mud pumping in railway track using in-service train data

Zeng, C Huang, J Xie, J Zhang, B Indraratna, B

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Transportation Geotechnics, 2021, 31, pp. 1-15
Issue Date:: 2021-11-01

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Field	Value	Language
dc.contributor.author	Zeng, C
dc.contributor.author	Huang, J
dc.contributor.author	Xie, J
dc.contributor.author	Zhang, B
dc.contributor.author	Indraratna, B https://orcid.org/0000-0002-9057-1514
dc.date.accessioned	2022-05-11T07:20:39Z
dc.date.available	2022-05-11T07:20:39Z
dc.date.issued	2021-11-01
dc.identifier.citation	Transportation Geotechnics, 2021, 31, pp. 1-15
dc.identifier.issn	2214-3912
dc.identifier.issn	2214-3912
dc.identifier.uri	http://hdl.handle.net/10453/157238
dc.description.abstract	Timely detection and identification of substructure defects in railway track are crucial for the safety and reliability of railway networks. Instrumented in-service trains can provide daily data for assessing the track conditions. This study tries to develop a data-driven model for the prediction of mud pumping defects using daily in-service train data. The data-driven model is based on long short-term memory (LSTM) networks. Bayesian optimization method is used to select the optimal hyper-parameters in LSTM. Genetic algorithm (GA) method is used for feature selection. A four-year real-world dataset from a section of railway network in Australia is used to train and test the data-driven model. The t-distributed Stochastic Neighbor Embedding (t-SNE) method is used to examine the resulting LSTM networks. The results show that the proposed approach can be used to predict the mud pumping defects in advance leaving enough time for maintenance.
dc.language	en
dc.publisher	Elsevier
dc.relation	http://purl.org/au-research/grants/arc/LP160101254
dc.relation.ispartof	Transportation Geotechnics
dc.relation.isbasedon	10.1016/j.trgeo.2021.100651
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering
dc.title	Prediction of mud pumping in railway track using in-service train data
dc.type	Journal Article
utslib.citation.volume	31
utslib.for	0905 Civil Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-11T07:20:33Z
pubs.publication-status	Published
pubs.volume	31

Abstract:

Timely detection and identification of substructure defects in railway track are crucial for the safety and reliability of railway networks. Instrumented in-service trains can provide daily data for assessing the track conditions. This study tries to develop a data-driven model for the prediction of mud pumping defects using daily in-service train data. The data-driven model is based on long short-term memory (LSTM) networks. Bayesian optimization method is used to select the optimal hyper-parameters in LSTM. Genetic algorithm (GA) method is used for feature selection. A four-year real-world dataset from a section of railway network in Australia is used to train and test the data-driven model. The t-distributed Stochastic Neighbor Embedding (t-SNE) method is used to examine the resulting LSTM networks. The results show that the proposed approach can be used to predict the mud pumping defects in advance leaving enough time for maintenance.

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http://hdl.handle.net/10453/157238