A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision | Artificial Intelligence and Applications </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/5/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Artificial Intelligence and Applications"/> <meta name="citation_journal_abbrev" content="AIA"/> <meta name="citation_issn" content="2811-0854"/> <meta name="citation_author" content="Khaled Hallak"/> <meta name="citation_author_institution" content="University of Lorraine, France"/> <meta name="citation_author" content="Adel Abdallah"/> <meta name="citation_author_institution" content="University of Lorraine, France"/> <meta name="citation_title" content="A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2024/06/06"/> <meta name="citation_volume" content="2"/> <meta name="citation_issue" content="4"/> <meta name="citation_firstpage" content="315"/> <meta name="citation_lastpage" content="329"/> <meta name="citation_doi" content="10.47852/bonviewAIA42022662"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/AIA/article/view/2662"/> <meta name="citation_abstract" xml:lang="en" content="Vehicle collision on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a ML approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications. 聽 Received: 22 February 2024 | Revised: 17 April 2024 | Accepted: 30 May 2024 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. 聽 Author Contribution Statement Khaled Hallak: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization. Adel Abdallah: Conceptualization, Methodology, Investigation, Resources, Writing - review &amp; editing, Supervision, Project administration, Funding acquisition."/> <meta name="citation_keywords" xml:lang="en" content="supervised machine learning"/> <meta name="citation_keywords" xml:lang="en" content="distributed machine learning"/> <meta name="citation_keywords" xml:lang="en" content="anomaly detection"/> <meta name="citation_keywords" xml:lang="en" content="structural health monitoring"/> <meta name="citation_keywords" xml:lang="en" content="vehicle-bridge collisions"/> <meta name="citation_keywords" xml:lang="en" content="railway bridges"/> <meta name="citation_keywords" xml:lang="en" content="classification model"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/AIA/article/download/2662/1031"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Khaled Hallak"/> <meta name="DC.Creator.PersonalName" content="Adel Abdallah"/> <meta name="DC.Date.created" scheme="ISO8601" content="2024-06-06"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2024-02-22"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2024-10-28"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-11-25"/> <meta name="DC.Description" xml:lang="en" content="Vehicle collision on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a ML approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications. 聽 Received: 22 February 2024 | Revised: 17 April 2024 | Accepted: 30 May 2024 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data available on request from the corresponding author upon reasonable request. 聽 Author Contribution Statement Khaled Hallak: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review &amp; editing, Visualization. Adel Abdallah: Conceptualization, Methodology, Investigation, Resources, Writing - review &amp; editing, Supervision, Project administration, Funding acquisition."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="2662"/> <meta name="DC.Identifier.pageNumber" content="315-329"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewAIA42022662"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/AIA/article/view/2662"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2024 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0"/> <meta name="DC.Source" content="Artificial Intelligence and Applications"/> <meta name="DC.Source.ISSN" content="2811-0854"/> <meta name="DC.Source.Issue" content="4"/> <meta name="DC.Source.Volume" content="2"/> <meta name="DC.Source.URI" content="https://ojs.bonviewpress.com/index.php/AIA"/> 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<nav class="cmp_breadcrumbs" role="navigation" aria-label="You are here:"> <ol> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/index"> Home </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/issue/archive"> Archives </a> <span class="separator">/</span> </li> <li> <a href="https://ojs.bonviewpress.com/index.php/AIA/issue/view/98"> Vol. 2 No. 4 (2024) </a> <span class="separator">/</span> </li> <li class="current" aria-current="page"> <span aria-current="page"> Research Article </span> </li> </ol> </nav> <article class="obj_article_details"> <h1 class="page_title"> A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision </h1> <div class="row"> <div class="main_entry"> <section class="item authors"> <h2 class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Khaled Hallak </span> <span class="affiliation"> University of Lorraine, France </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0002-4057-1620" target="_blank"> https://orcid.org/0000-0002-4057-1620 </a> </span> </li> <li> <span class="name"> Adel Abdallah </span> <span class="affiliation"> University of Lorraine, France </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewAIA42022662"> https://doi.org/10.47852/bonviewAIA42022662 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> supervised machine learning, distributed machine learning, anomaly detection, structural health monitoring, vehicle-bridge collisions, railway bridges, classification model </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>Vehicle collision on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a ML approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications.</p> <p>聽</p> <p><strong>Received:</strong> 22 February 2024 <strong>| Revised:</strong> 17 April 2024<strong> | Accepted:</strong> 30 May 2024</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that they have no conflicts of interest to this work.</p> <p><br><strong>Data Availability Statement</strong></p> <p>Data available on request from the corresponding author upon reasonable request.</p> <p>聽</p> <p><strong>Author Contribution Statement</strong></p> <p><strong>Khaled Hallak:</strong> Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization. <strong>Adel Abdallah</strong><strong>: </strong>Conceptualization, Methodology, Investigation, Resources, Writing - review & editing, Supervision, Project administration, Funding acquisition.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89268", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":210,\"events_count_by_day\":[],\"events_count_by_month\":[{\"count\":\"10\",\"date\":\"2024-07\"},{\"count\":\"24\",\"date\":\"2024-08\"},{\"count\":\"69\",\"date\":\"2024-09\"},{\"count\":\"31\",\"date\":\"2024-10\"},{\"count\":\"76\",\"date\":\"2024-11\"}],\"events_count_by_year\":[{\"count\":\"210\",\"date\":\"2024\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewaia42022662\",\"doi\":\"10.47852\\/bonviewaia42022662\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewaia42022662\",\"ISSN\":[\"2811-0854\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewaia42022662\",\"abstract\":\"<jats:p>Vehicle collisions on bridges is an important issue for the transportation infrastructure management. 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The full text ingestion is still in progress and will be completed at January, 2025.</p> </div> </article> <article class="block_announcements_article"> <h3 class="block_announcements_article_headline"> <a href="https://ojs.bonviewpress.com/index.php/AIA/announcement/view/87"> AIA Published Volume 2, Issue 4 on October 28, 2024 </a> </h3> <time class="block_announcements_article_date" datetime="2024-10-28"> <strong>October 28, 2024</strong> </time> <div class="block_announcements_article_content"> <p>We are excited to announce that <em><strong>Artificial Intelligence and Applications (AIA)</strong></em> published Volume 2 Issue 4 on October 28, 2024!</p> </div> </article> <a id="show-all" href="https://ojs.bonviewpress.com/index.php/AIA/announcement">Show all announcements ...</a> </div> </div> <div class="pkp_block block_keyword_cloud"> <h2 class="title">Keywords</h2> <div class="content" id='wordcloud'></div> <script> function randomColor() { var cores = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']; return cores[Math.floor(Math.random()*cores.length)]; } document.addEventListener("DOMContentLoaded", function() { var keywords = [{"text":"text detection","size":1},{"text":"text recognition","size":1},{"text":"text spotting","size":1},{"text":"text classification","size":1},{"text":"scene text","size":1},{"text":"car number plate detection","size":1},{"text":"optical character recognition","size":1},{"text":"supervised machine learning","size":1},{"text":"distributed machine learning","size":1},{"text":"anomaly detection","size":1},{"text":"structural health monitoring","size":1},{"text":"vehicle-bridge collisions","size":1},{"text":"railway bridges","size":1},{"text":"classification model","size":1},{"text":"deep learning","size":1},{"text":"polyp detection","size":1},{"text":"cnn","size":1},{"text":"image classification","size":1},{"text":"colorectal disease","size":1},{"text":"alzheimer's disease","size":1},{"text":"intervention techniques","size":1},{"text":"conventional methods","size":1},{"text":"artificial intelligence","size":1},{"text":"cognitive stimulation","size":1},{"text":"reality orientation","size":1},{"text":"reminiscence therapy","size":1},{"text":"enemy identification","size":1},{"text":" text similarity","size":1},{"text":"sentence transformer models","size":1},{"text":"natural language processing","size":1},{"text":"machine learning","size":1},{"text":"hr demand","size":1},{"text":"business","size":1},{"text":"hr management","size":1},{"text":"m-knn algorithm","size":1},{"text":"origin tool","size":1},{"text":"soliton solutions","size":1},{"text":"modified benjamin-bona-mahony equation","size":1},{"text":"ostrovsky-benjamin-bona-mahony equation","size":1},{"text":"mikhailov-novikov-wang equation","size":1},{"text":"physics informed neural networks","size":1},{"text":"cancer prediction","size":1},{"text":"prostate cancer","size":1},{"text":"unsupervised learning","size":1},{"text":"intelligent system","size":1},{"text":"cybernetics","size":1},{"text":"decision support","size":1},{"text":"baby cry","size":1},{"text":"multiple instance learning","size":1},{"text":"audio classification","size":1}]; var totalWeight = 0; var blockWidth = 300; var blockHeight = 200; var transitionDuration = 200; var length_keywords = keywords.length; var layout = d3.layout.cloud(); layout.size([blockWidth, blockHeight]) .words(keywords) .fontSize(function(d) { return fontSize(+d.size); }) .on('end', draw); var svg = d3.select("#wordcloud").append("svg") .attr("viewBox", "0 0 " + blockWidth + " " + blockHeight) .attr("width", '100%'); function update() { var words = layout.words(); fontSize = d3.scaleLinear().range([16, 34]); if (words.length) { fontSize.domain([+words[words.length - 1].size || 1, +words[0].size]); } } keywords.forEach(function(item,index){totalWeight += item.size;}); update(); function draw(words, bounds) { var width = layout.size()[0], height = layout.size()[1]; scaling = bounds ? 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A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision | Artificial Intelligence and Applications