[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124466-en":3,"doc-seo-124466-105":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},124466,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","From Traditional Damage Detection Methods to Physics-Informed Machine Learning in Bridges - A Review","Structural Health Monitoring (SHM) of bridges is essential for infrastructure management, supporting safety and durability under varied operational and environmental conditions. A key task is Structural Damage Detection (SDD), which identifies, localizes, and quantifies damages such as cracks and corrosion. Traditional physics-based and Machine Learning approaches can struggle with bridge-system complexity and limited or noisy data. Physics-Informed Machine Learning (PIML) combines ML with physical constraints, improving accuracy, interpretability, and generalization. This review traces SHM evolution to PIML, evaluating strengths, limitations, and case studies while emphasizing early damage detection.","Engineering Structures 330 (2025) 119862  \n| Review article\u003Cbr>From traditional damage detection methods to Physics-Informed Machine Learning in bridges: A review\u003Cbr>Safae Mammeria, Brais Barros b, Borja Conde-Carneroa, Belén Riveiroa ,∗\u003Cbr>a CINTECX, Universidade de Vigo, GeoTECH Group, As Lagoas Marcosende, 36310, Vigo, Spain b ICITECH, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, 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>Structural Health Monitoring Bridges\u003Cbr>Structural Damage Detection Physics-Informed Machine Learning |  | Structural Health Monitoring (SHM) of bridges plays a crucial role in infrastructure management, ensuring the safety and durability of bridges under diverse operational and environmental conditions. A vital aspect of SHM involves Structural Damage Detection (SDD), which focuses on identifying, localizing, and quantifying structural damage such as cracks, corrosion, and other forms of deterioration. While traditional SDD methods, including physics-based and Machine Learning (ML) methods, are effective, they often tend to be challenging in addressing the complex and dynamic nature of bridge systems, particularly when dealing with limited or noisy data. Physics-Informed Machine Learning (PIML) has emerged as a promising approach that integrates the strengths of ML with the reliability of physical constraints and principles, offering more accurate, robust interpretability and generalization capabilities, thereby strengthening the SHM framework. This paper provides a comprehensive overview of the evolution of SHM, from traditional SDD methods to the application of PIML. By analyzing key case studies and examining the strengths and limitations of each method, this review highlights the potential of PIML to address the challenges of real-world bridge monitoring and improve the early detection of structural damage. |  |\n\nContents  \n1. Introduction ...................................................................................................................................................................................................... 2  \n2. Structural health monitoring (SHM) .................................................................................................................................................................... 3  \n2.1. Structural damage detection (SDD) methods ............................................................................................................................................. 3  \n2.2. Methodology of SHM .............................................................................................................................................................................. 3  \n3. Traditional damage detection methods ................................................................................................................................................................ 4  \n3.1. Physics-based methods ............................................................................................................................................................................ 4  \n3.2. Data driven methods .............................................................................................................................................................................. 5  \n3.2.1. Fundamental machine learning tasks in BHM ............................................................................................................................. 5  \n3.3. Challenges of traditional SDD methods..................................................................................................................................................... 6  \n4. Physics-informed machine learning (PIML)........................................................................................................................................................... 6  \n4.1. Featur","cbCain5hSiKd6iEz","https://ap.wps.com/l/cbCain5hSiKd6iEz","pdf",2123793,1,15,"English","en",105,"# Introduction\n# Structural health monitoring (SHM)\n## Structural damage detection (SDD) methods\n## Methodology of SHM\n# Traditional damage detection methods\n## Physics-based methods\n## Data driven methods\n# Physics-informed machine learning (PIML)\n## Features of PIML methods\n## Methodologies of PIML","[{\"question\":\"What is Structural Damage Detection (SDD) in bridge SHM?\",\"answer\":\"SDD aims to identify, localize, and quantify structural damage such as cracks, corrosion, and other deterioration forms within bridge systems.\"},{\"question\":\"Why can traditional SDD methods face difficulties in practice?\",\"answer\":\"They often struggle with the complex and dynamic nature of bridges and with limited or noisy data, which can reduce robustness and reliability.\"},{\"question\":\"How does Physics-Informed Machine Learning (PIML) improve bridge monitoring?\",\"answer\":\"PIML integrates machine learning with physical constraints and principles, enhancing accuracy, interpretability, and generalization for stronger SHM performance.\"}]","From Traditional Damage Detection Methods to Physics-Informed Machine Learning in Bridges - A Review | PDF",1785822486,38,{"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},"from-traditional-damage-detection-methods-to-physics-informed-machine-learning-in-bridges-a-review","",{"@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/from-traditional-damage-detection-methods-to-physics-informed-machine-learning-in-bridges-a-review/124466/",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","2026-08-04",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 Structural Damage Detection (SDD) in bridge SHM?","Question",{"text":75,"@type":76},"SDD aims to identify, localize, and quantify structural damage such as cracks, corrosion, and other deterioration forms within bridge systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why can traditional SDD methods face difficulties in practice?",{"text":80,"@type":76},"They often struggle with the complex and dynamic nature of bridges and with limited or noisy data, which can reduce robustness and reliability.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Physics-Informed Machine Learning (PIML) improve bridge monitoring?",{"text":84,"@type":76},"PIML integrates machine learning with physical constraints and principles, enhancing accuracy, interpretability, and generalization for stronger SHM performance.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]