[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122842-en":3,"doc-seo-122842-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122842,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Using Machine Learning For Long-Term Track Bed Behaviour Analysis And Maintenance Scheduling Optimisation","This study presents a machine-learning approach to evaluate railway track quality and improve long-term maintenance scheduling. It focuses on track geometry data from a UK high-speed line and two conventional-speed case-study lines, addressing rising rail traffic, faster deterioration of components, and climate-driven events such as track buckling and embankment instability. The work evaluates tamping efficiency, applying a single-layer artificial neural network plus an autoencoder and KMeans-based clustering to identify local deterioration and support targeted proactive interventions that reduce future maintenance and costs.","This thesis has been submitted in fulfilment of the requirements for a postgraduate degree (e. g. PhD, MPhil, DClinPsychol) at the University of Edinburgh. Please note the following terms and conditions of use:  \n• This work is protected by copyright and other intellectual property rights, which are retained by the thesis author, unless otherwise stated.  \n• A copy can be downloaded for personal non-commercial research or study, without prior permission or charge.  \n• This thesis cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author.  \n• The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author.  \n• When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given.  \nUsing Machine Learning For Long-Term Track Bed Behaviour Analysis And Maintenance Scheduling Optimisation  \nKonstantin Popov  \nA thesis presented for the degree of Doctor of  \nPhilosophy The University of Edinburgh School of Engineering September 2023  \nTo my nephew, Mitko.  \nAbstract  \nThe purpose of this study is to present a novel approach for evaluating railway track quality using machine learning algorithms. The work will focus mainly on track geometry data from a high-speed line in the UK, as well as two conventional-speed lines which have been used as case studies. Rail traffic levels have been increasing steadily in the UK over the years and this is expected to continue, as the country looks to shift to more sustainable transportation. Greater traffic loads will lead to quicker declining of track components and overall, more deteriorated state of the infrastructure. In addition, changing climate conditions will further add to this because of more frequent occurrence of events such as track buckling due to high temperatures and embankment instability due to increasing levels of precipitation. As a result, more frequent maintenance interventions will be required, as well as amore detailed approach of track quality assessment.  \nMaintenance in the form of tamping is used to re-position the track at a level, which reduces train vibrations and produces a smooth journey. Remedial works are often scheduled using either corrective or preventive maintenance techniques, which do not always provide the highest efficiency. These can result in over-tamping of the track, which can degrade ballast particles more quickly and prematurely result in highly expensive renewal works. On the other hand, proactive maintenance identifies poorly performing segments and investigates the reason behind it, so an intervention can be targeted to remove that reason and improve quality. This ensures greater track stability moving forward and therefore, fewer interventions in the future. Less tamping will preserve the quality of the ballast for a longer period of time and extend the lifespan of the track, thus reducing annual costs.  \nIn an attempt to achieve proactive maintenance, track inspections have become increasingly digital, which has resulted in the accumulation of large data sets. Such information helps to investigate track response under various maintenance activities and other factors, such as climate conditions. For the purpose, a single-layer Artificial Neural Network was applied in this study.  \nBased on available data from the high-speed track, it was found that tamping works covering long sections of track (>100-200m) were less efficient at correcting geometry than works focusing on shorter spans ( \u003C50m) . Change in quality of 50-metre segments on the high-speed track was analysed and large-scale works exhibited an efficiency of ∼52.5%, whereas concentrated tamping improved quality at the rate of 84% . This suggests that deterioration on this line happens mainly on a local level and requires a different form of analysis to achieve more efficient maintenance, which will ","cbCaifDziGSdJqVj","https://ap.wps.com/l/cbCaifDziGSdJqVj","pdf",27966527,1,325,"English","en",105,"# 1 Introduction\n## 1.1 Problem identification\n## 1.2 Track quality assessment\n## 1.3 Aim and objectives\n## 1.4 Impact of research and beneficiaries\n## 1.5 Thesis outline\n# 2 Track structure and geometry measurements systems\n## 2.1 Track components","[{\"question\":\"What problem does the thesis address?\",\"answer\":\"The thesis targets how to evaluate railway track quality and schedule maintenance more effectively under increasing traffic and accelerating deterioration, including climate-related risks.\"},{\"question\":\"Why is proactive maintenance emphasized over corrective or preventive techniques?\",\"answer\":\"Proactive maintenance identifies poorly performing segments and investigates underlying causes, enabling targeted interventions. This avoids over-tamping, preserves ballast quality longer, and reduces costly renewal work.\"},{\"question\":\"How do the machine-learning methods contribute to detecting deterioration?\",\"answer\":\"A single-layer artificial neural network assesses geometry quality changes, while an autoencoder outputs the magnitude of quality change between inspections. KMeans clustering further distinguishes positive versus negative classes to support maintenance scheduling of critical segments.\"}]","Using Machine Learning For Long-Term Track Bed Behaviour Analysis And Maintenance Scheduling Optimisation | PDF",1785813217,819,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"using-machine-learning-for-long-term-track-bed-behaviour-analysis-and-maintenance-scheduling-optimisation","",{"@graph":36,"@context":86},[37,54,69],{"@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/using-machine-learning-for-long-term-track-bed-behaviour-analysis-and-maintenance-scheduling-optimisation/122842/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the thesis address?","Question",{"text":76,"@type":77},"The thesis targets how to evaluate railway track quality and schedule maintenance more effectively under increasing traffic and accelerating deterioration, including climate-related risks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why is proactive maintenance emphasized over corrective or preventive techniques?",{"text":81,"@type":77},"Proactive maintenance identifies poorly performing segments and investigates underlying causes, enabling targeted interventions. This avoids over-tamping, preserves ballast quality longer, and reduces costly renewal work.",{"name":83,"@type":74,"acceptedAnswer":84},"How do the machine-learning methods contribute to detecting deterioration?",{"text":85,"@type":77},"A single-layer artificial neural network assesses geometry quality changes, while an autoencoder outputs the magnitude of quality change between inspections. KMeans clustering further distinguishes positive versus negative classes to support maintenance scheduling of critical segments.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]