[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118032-en":3,"doc-seo-118032-105":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},118032,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Machine Learning based approach to predict road rutting considering uncertainty","Roads function as essential public assets supporting transportation and long-term societal development. Data-driven methods such as digital twins and machine learning promise accurate future condition modelling and maintenance recommendations, yet practical use is constrained by limited or noisy data and by neglect of underlying pavement physical behaviour. This study proposes a machine-learning framework that accounts for learning uncertainties when predicting road rutting. US LTPP data are combined with physics-based finite element synthetic data to improve accuracy and reduce uncertainty.","University of Birmingham  \nA Machine Learning based approach to predict road rutting considering uncertainty  \nChen, K. ; Eskandari Torbaghan, M. ; Thom, N. ; Garcia-Hernández, A. ; Faramarzi, A. ;  \nChapman, D.  \nDOI:  \n10.1016/j.cscm.2024.e03186  \nLicense:  \nCreative Commons: Attribution (CC BY)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nChen, K, Eskandari Torbaghan, M, Thom, N, Garcia-Hernández, A, Faramarzi, A & Chapman, D 2024, 'A Machine Learning based approach to predict road rutting considering uncertainty', Case Studies in Construction Materials, vol. 20, e03186 . [https://doi.org/10.1016/j.cscm.2024.e03186](https://doi.org/10.1016/j.cscm.2024.e03186)  \nLink to publication on Research at Birmingham portal  \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: 02. Aug. 2026  \nCase Studies in Construction Materials 20 (2024) e03186  \nContents lists available at ScienceDirect  \nCase Studies in Construction Materials  \njournal [homepage: www.elsevier.com/locate/cscm](homepage: www.elsevier.com/locate/cscm)  \n| A Machine Learning based approach to predict road rutting considering uncertainty\u003Cbr>K. Chena, b, *, M. Eskandari Torbaghana, N. Thom b, A. Garcia-Hern´andez c,\u003Cbr>A. Faramarzia, D. Chapman a\u003Cbr>a School of Engineering, University of Birmingham, Birmingham B15 2TT, United Kingdom\u003Cbr>b Nottingham Transportation Engineering Centre, Department of Civil Engineering, University of Nottingham, Nottingham NG7 2RD, United Kingdom c Institute of Highway Engineering, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, Aachen 52074, Germany |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Machine Learning Simulation\u003Cbr>Uncertainty Quantification Road Condition Prediction Digital Twins |  | Roads as vital public assets are the backbone for transportation systems and support constant societal development. Recently, data-driven technologies such as digital twins and especially machine learning have shown great potential to maintain the service level of the existing road infrastructure by accurate future condition modelling and optimal maintenance treatment recommendations. However, the pavement community suffers from inadequate data and errors experienced in data collection, which unavoidably limits machine learning performance. In addition, focusing solely on data without considering the underlying physical behaviour remains as a challenge for the practical implementation of machine learning. To this end, this study provides a machine l","cbCaia6OvjD2ZHEp","https://ap.wps.com/l/cbCaia6OvjD2ZHEp","pdf",6375706,1,23,"English","en",105,"# Introduction\n## Motivation and challenges\n# Method and data\n## LTPP data and physics-based synthetic data\n# Results and implications\n## Accuracy and uncertainty improvements","[{\"question\":\"What problem does the study address for road rutting prediction?\",\"answer\":\"The study targets two practical limitations: inadequate/noisy data that restricts machine-learning performance, and the challenge of ignoring pavement physical behaviour during model development.\"},{\"question\":\"How is uncertainty considered in the proposed machine learning approach?\",\"answer\":\"The framework explicitly accounts for machine learning uncertainties while predicting road rutting, so the predictions reflect both data limitations and model learning variability.\"},{\"question\":\"What data sources are used to train and evaluate the model?\",\"answer\":\"The US Long-Term Pavement Performance (LTPP) database is the main source, and supplementary synthetic data are generated using finite element simulations based on physics.\"}]","A Machine Learning based approach to predict road rutting considering uncertainty | 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