[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-291189-105":59,"doc-detail-291189-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","proposed-machine-learning-techniques-for-bridge-structural-health-monitoring-a-laboratory-study","Proposed Machine Learning Techniques for Bridge Structural Health Monitoring - A Laboratory Study","","Structural health monitoring for bridges addresses degradation risks driven by defects that can worsen over time. Prior research emphasizes machine-learning models that distinguish healthy from non-healthy structural states by operating on feature sets, even though the feature-to-health mapping is hard to formalize. This study proposes four classification algorithms for SHM using support vector machine concepts. A laboratory experiment at Western Sydney University validates and compares them against a basic SVM to assess performance.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/proposed-machine-learning-techniques-for-bridge-structural-health-monitoring-a-laboratory-study/291189/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/proposed-machine-learning-techniques-for-bridge-structural-health-monitoring-a-laboratory-study/291189.png","ImageObject",300,407,{"name":92,"@type":93},"Valentina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-24","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is structural health monitoring (SHM) important for bridges?","Question",{"text":113,"@type":114},"SHM is needed because bridge defects and degradation (e.g., concrete deterioration, corrosion, and weakening connections) can deteriorate structural integrity and serviceability over time.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What limitations do manual visual inspections have?",{"text":118,"@type":114},"Manual inspections are time consuming, expensive, and subjective, leading to variability and potential errors that depend on inspector proficiency and attention.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the study propose and how is it validated?",{"text":122,"@type":114},"The study proposes four SHM classification algorithms based on support vector machine concepts and uses laboratory experiments at Western Sydney University, comparing results against a basic SVM to evaluate performance.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291189,1789643633,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},13056703020460,"https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923","remote sensing  \nArticle  \nProposed Machine Learning Techniques for Bridge Structural Health Monitoring: A Laboratory Study  \nAzadeh Noori Hoshyar 1,*, Maria Rashidi 2, Yang Yu 3 and Bijan Samali 2  \nCitation: Noori Hoshyar, A.; Rashidi, M.; Yu, Y.; Samali, B. Proposed Machine Learning Techniques for Bridge Structural Health Monitoring:  \nA Laboratory Study. Remote Sens. 2023, 15, 1984. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/rs15081984](10.3390/rs15081984)  \nAcademic Editor: Fabio Tosti  \nReceived: 12 December 2022  \nRevised: 17 March 2023  \nAccepted: 27 March 2023  \nPublished: 9 April 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute of Innovation, Science and Sustainability, Federation University Australia, Brisbane, QLD 4001, Australia  \n2 Centre for Infrastructure Engineering, School of Engineering, Design and Built Environment, Western Sydney University, Sydney, NSW 2747, Australia  \n3 Centre for Infrastructure Engineering and Safety, University of New South Wales, Kensington, NSW 2052, Australia  \n* Correspondence: [a.noorihoshyar@federation.edu.au](a.noorihoshyar@federation.edu.au)  \nAbstract: Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge's structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU) . The results were compared with the basic SVM to evaluate the performance of proposed algorithms.  \nKeywords: bridge monitoring; feature extraction; feature selection; machine learning; crack detection; anomaly detection; structures  \n1. Introduction  \nStructural health monitoring (SHM) can potentially provide effective solutions to the continuous assessment of infrastructural health. In the United States, every two years, approximately 600,000 bridges are inspected, scaled and rated according to their condition [1] . According to this rating, released by Federal Highway Administration, fewer than 60% of the national bridges are functioning efficiently [1] . This clearly highlights the need for structural health monitoring. Concrete degradation, steel corrosion, changes in boundary conditions, and the weakening of connections in structures over time are major concerns, which gradually deteriorate the bridge's structural integrity and service capability if not maintained [2] .  \nCurrently, the main method of physical and functional assessment of conditions in civil infrastructure is manual, and mostly visual, inspection. This method of inspection is time consuming, expensive, and subjective. Studies have declared that such inspections are not always accurate and involve some errors, as they are highly variable and dependent on the inspectors' proficiency, knowledge and experience, and their emotional state and alertness. Several accidents have been reported that can be related to","cbCaid2uuTJi9cgY","https://ap.wps.com/l/cbCaid2uuTJi9cgY","pdf",1201256,23,"English","# Introduction\n## Structural health monitoring needs and challenges\n## Limitations of manual visual inspections\n## Motivation for machine-learning-based SHM\n# Proposed approach\n## Machine-learning paradigms for damage detection using SVM concepts\n## Ensemble classifiers, misclassified points, and kernel combinations","[{\"question\":\"Why is structural health monitoring (SHM) important for bridges?\",\"answer\":\"SHM is needed because bridge defects and degradation (e.g., concrete deterioration, corrosion, and weakening connections) can deteriorate structural integrity and serviceability over time.\"},{\"question\":\"What limitations do manual visual inspections have?\",\"answer\":\"Manual inspections are time consuming, expensive, and subjective, leading to variability and potential errors that depend on inspector proficiency and attention.\"},{\"question\":\"What does the study propose and how is it validated?\",\"answer\":\"The study proposes four SHM classification algorithms based on support vector machine concepts and uses laboratory experiments at Western Sydney University, comparing results against a basic SVM to evaluate performance.\"}]","Proposed Machine Learning Techniques for Bridge Structural Health Monitoring - A Laboratory Study | PDF",58]