[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117746-en":3,"doc-seo-117746-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117746,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Ballastless track support deterioration evaluation using machine learning","Ballastless tracks are widely adopted for high-speed rail because they require comparatively minimal maintenance, yet support deterioration beneath the connectors between in-between slabs is a well-known industrial issue. Water ingress can rapidly degrade the cement-stabilized soil supporting track slabs, worsening ride quality and threatening safe high-speed operations. Early detection of impaired slab-support conditions enables predictive and preventive maintenance. This study uses slab support stiffness as a precursor and combines validated nonlinear FE models with machine-learning methods trained on axle-box acceleration datasets.","University of Birmingham  \nBallastless track support deterioration evaluation using machine learning  \nSresakoolchai, Jessada; Li, Ting; Kaewunruen, Sakdirat  \nDOI:  \n10. 1007/978-981-19-7331-4_ 115  \nLicense:  \nNone: All rights reserved  \nDocument Version  \nPeer reviewed version  \nCitation for published version (Harvard):  \nSresakoolchai, J, Li, T & Kaewunruen, S 2023, Ballastless track support deterioration evaluation using machine learning. in G Geng, X Qian, LH Poh & SD Pang (eds), Proceedings of The 17th East Asian-Pacific Conference on Structural Engineering and Construction, 2022: EASEC-17, Singapore. 1 edn, Lecture Notes in Civil Engineering, vol. 302, Springer, Singapore, pp. 1455–1463, The 17th East Asia-Pacific Conference on Structural Engineering and Construction (EASEC-17), Singapore, Singapore, 27/06/22 . [https://doi.org/10.1007/978-981-](https://doi.org/10.1007/978-981-)[ ](https://doi.org/10.1007/978-981-)[19-7331-4_115](19-7331-4_115)  \nLink to publication on Research at Birmingham portal  \nPublisher Rights Statement:  \nThis is a pre-copyedited version of a contribution published in  \nGeng, G. , Qian, X. , Poh, L. H. , Pang, S. D. (eds) Proceedings of The 17th East Asian-Pacific Conference on Structural Engineering and Construction, 2022. Lecture Notes in Civil Engineering, vol 302, published by Springer, Singapore. The definitive authenticated version is available online via: [https://doi.org/10.1007/978-981-19-7331-4_1](https://doi.org/10.1007/978-981-19-7331-4_1)15  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 01. Aug. 2026  \nBallastless track support deterioration evaluation using machine learning  \nJessada Sresakoolchai 1, Ting Li2, Sakdirat Kaewunruen 1*  \n1Department of Civil Engineering, University of Birmingham, Birmingham, United Kingdom,  \nB15 2TT  \n2 School of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang, China, 050043  \n[Emails: jss814@student.bham.ac.uk](Emails: jss814@student.bham.ac.uk), [s.kaewunruen@bham.ac.uk](s.kaewunruen@bham.ac.uk), [liting9225@126.com](liting9225@126.com)  \n*Corresponding author  \nAbstract. 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 ","cbCaicbwMwLZEZKa","https://ap.wps.com/l/cbCaicbwMwLZEZKa","pdf",378944,1,12,"English","en",105,"# Abstract\n# Introduction\n# Machine Learning-Based Deterioration Evaluation\n## Dataset Design and Parametric Studies\n## Model Results and Condition Diagnosis","[{\"question\":\"Why is early detection of ballastless track slab support deterioration important?\",\"answer\":\"Support deterioration can be rapidly undermined by water ingress, leading to reduced ride quality and potentially endangering high-speed train operations, making early assessment essential for predictive maintenance.\"},{\"question\":\"What indicator is used to evaluate the deterioration severity in this study?\",\"answer\":\"Track slab support stiffness is used as a precursor measure to identify and quantify the severity of deterioration.\"},{\"question\":\"How are the machine learning models trained and what data are used?\",\"answer\":\"Nonlinear FE models validated by field measurements generate data, and axle box accelerations are used as input datasets for the machine learning models.\"}]","Ballastless track support deterioration evaluation using machine learning | 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is early detection of ballastless track slab support deterioration important?","Question",{"text":75,"@type":76},"Support deterioration can be rapidly undermined by water ingress, leading to reduced ride quality and potentially endangering high-speed train operations, making early assessment essential for predictive maintenance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What indicator is used to evaluate the deterioration severity in this study?",{"text":80,"@type":76},"Track slab support stiffness is used as a precursor measure to identify and quantify the severity of deterioration.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models trained and what data are used?",{"text":84,"@type":76},"Nonlinear FE models validated by field measurements generate data, and axle box accelerations are used as input datasets for the machine learning 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