[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125547-en":3,"doc-seo-125547-105":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},125547,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Parametric analysis of railway infrastructure for improved performance and lower life-cycle costs using machine learning techniques - Research report","Rigorous railway infrastructure management is essential to prevent accidents and reduce operation and maintenance costs by understanding asset interactions and how individual track parameters shape overall performance. The study varies key parameters within realistic ranges on a calibrated finite element slab track model, then uses the resulting responses to train and validate predictive machine-learning models. It incorporates soil/subgrade, layers, sleepers, pads, rails, and also considers axle loads and service speeds, identifying soil properties, rail pad characteristics, and axle loads as most influential. The approach supports predictive maintenance and cost-reduction solutions for competitive rail transport.","Advances in Engineering Software 175 (2023) 103357  \nContents lists available at ScienceDirect  \nAdvances in Engineering Software  \njournal [homepage:](homepage: www.elsevier.com/locate/advengsoft)[ www.elsevier.com/locate/advengsoft](homepage: www.elsevier.com/locate/advengsoft)  \n| Parametric analysis of railway infrastructure for improved performance and   lower life-cycle costs using machine learning techniques\u003Cbr>Jose A. Sainz-Aja a, *, Diego Ferre˜no a, Joao Pombob, c, d, Isidro A. Carrascal a, Jose Casado a, Soraya Diego a, Jorge Castro e\u003Cbr>a LADICIM (Laboratory of Materials Science and Engineering), University of Cantabria. E.T.S. de Ingenieros de Caminos, Canales y Puertos, Av./Los Castros 44, 39005 Santander, Spain\u003Cbr>b Institute of Railway Research, School of Computing and Engineering, University of Huddersfield, United Kingdom\u003Cbr>c IDMEC, Instituto Superior T´ecnico, Universidade de Lisboa, Lisboa, Portugal d ISEL, Intituto Politecnico de Lisboa, Lisboa, Portugal\u003Cbr>e Group of Geotechnical Engineering, [University of Cantabria. E.T.S. de](University of Cantabria. E.T.S. de) Ingenieros de Caminos, Canales y Puertos, Av./Los Castros 44, 39005 Santander, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Railway tracks Infrastructure assets Predictive models Machine learning algorithms Monte Carlo method |  | Rigorous and efficient management of the railway infrastructure is crucial to avoid accidents and reduce operation and maintenance costs. This requires in-depth knowledge of the assets, the interaction among them and the effect that each track parameter has on the overall infrastructure performance. In this study, a large set of studies are carried out, on a previously calibrated finite element slab track model, where the relevant track parameters are varied within their usual ranges. The results are then used to train and validate a series of predictive models based on Machine Learning algorithms. This methodology provides greater understanding and enhanced prediction of the behaviour of tracks, which are composed of multiple variables such as the soil/subgrade, supporting layers, sleepers, pads and rails. The study also considers train axle loads and service speeds, which are other key elements that influence the track performance. The results show that the parameters that have greatest influence on the railway infrastructure are the properties of the soil, characteristics of the rail pads and the axle loads. This work can support the implementation of predictive maintenance procedures for railway tracks and the development of innovative technological solutions, providing responses to the industrial needs of reducing costs and contributing to improve the competitiveness of railway transport. |\n\n1. Introduction  \nModern societies require efficient means of transport for passengers and goods. Speed, comfort, safety and environmental friendliness are unavoidable demands nowadays. There are several reasons that have made the railway one of the most used means of transport worldwide. The main advantages of the railway over other alternatives are the high safety level and reliability, together with reduced costs and the low levels of CO2 emissions [1–3]. According to the Spanish Transport and Logistics Observatory, in 2016, the railway was the means of transport of 28.8% of goods in Spain and more than twice the long-distance journeys were made by train than by airplane [4]. Furthermore, the railway was responsible for 29% of public transport in Spain and the risk of fatal accidents per kilometre is equivalent for railroad and airplane, being 28 times lower than transport by private vehicles [5]. These  \nfigures are easily comparably to other countries. According to the European Union, in terms of pollution, in 2014, roads were responsible for 72.8% of total CO2 emissions in the transport sector, naval was responsible for 13.0% and aviation for 13.1%, while railways only ","cbCaicfdrRhbUXaj","https://ap.wps.com/l/cbCaicfdrRhbUXaj","pdf",12382466,1,16,"English","en",105,"# Introduction\n## Railway performance demands and motivations\n## Infrastructure modeling and predictive maintenance context\n# Methodology and modeling workflow\n## Finite element parametric study\n## Machine-learning model training and validation\n# Key findings\n## Influential track parameters\n## Implications for maintenance and cost reduction","[{\"question\":\"What is the main goal of the parametric analysis in this study?\",\"answer\":\"To improve understanding and prediction of railway track behavior by linking varied track parameters to infrastructure performance and life-cycle cost outcomes.\"},{\"question\":\"How are the predictive models built and verified?\",\"answer\":\"Simulation results from a calibrated finite element slab track model, with parameters varied within usual ranges, are used to train and validate machine-learning predictive models.\"},{\"question\":\"Which factors are reported as having the greatest influence on railway infrastructure performance?\",\"answer\":\"Soil properties, characteristics of the rail pads, and the train axle loads are identified as the most influential parameters.\"}]","Parametric analysis of railway infrastructure for improved performance and lower life-cycle costs using machine learning techniques - Research report | PDF",1785899794,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"parametric-analysis-of-railway-infrastructure-for-improved-performance-and-lower-life-cycle-costs-using-machine-learning-techniques-research-report","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/parametric-analysis-of-railway-infrastructure-for-improved-performance-and-lower-life-cycle-costs-using-machine-learning-techniques-research-report/125547/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the parametric analysis in this study?","Question",{"text":75,"@type":76},"To improve understanding and prediction of railway track behavior by linking varied track parameters to infrastructure performance and life-cycle cost outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the predictive models built and verified?",{"text":80,"@type":76},"Simulation results from a calibrated finite element slab track model, with parameters varied within usual ranges, are used to train and validate machine-learning predictive models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are reported as having the greatest influence on railway infrastructure performance?",{"text":84,"@type":76},"Soil properties, characteristics of the rail pads, and the train axle loads are identified as the most influential parameters.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]