[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125997-en":3,"doc-seo-125997-105":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},125997,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Civil structural health monitoring and machine learning - a comprehensive review","Manual visual inspection remains the baseline for assessing civil infrastructure safety and serviceability, yet aging structures and growing wear make efficient condition evaluation essential. This comprehensive review explains how structural health monitoring converts sensor data into actionable knowledge and contrasts physics-based and data-driven approaches. Emphasis is placed on integrating machine learning for concrete structures, addressing energy efficiency and cost-effectiveness, and analyzing practical implementation challenges and real-world applications.","A. Anjum et alii, Frattura edIntegrità Strutturale, 69 (2024) 43-59; DOI: 10.3221/IGF-ESIS.69.04  \nCivil structural health monitoring and machine learning: a comprehensive review  \nAsraar Anjum, Meftah Hrairi*  \nDepartment of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia  \n[asraar.anjum@live.iium.edu.my and meftah@iium.edu.my](asraar.anjum@live.iium.edu.my and meftah@iium.edu.my)  \nAbdul Aabid  \nDepartment of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh 11586, Saudi Arabia  \n[aaabid@psu.edu.sa](aaabid@psu.edu.sa), [http://orcid.org/0000-0002-4355-9803](http://orcid.org/0000-0002-4355-9803)  \nNorfazrina Yatim  \nDepartment of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia  \n[noorfazrina@iium.edu.my](noorfazrina@iium.edu.my)  \nMaisarah Ali  \nDepartment of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, 50728, Kuala Lumpur, Malaysia  \n[maisarahali@iium.edu.my](maisarahali@iium.edu.my)  \nCitation: Anjum, A., Hrairi, M., Aabid, A., Yatim, N., Ali, M., Civil structural health monitoring and machine learning: a comprehensive review, Frattura ed Integrità Strutturale, 69 (2024) 43-59.  \nReceived: 02.01.2024  \nAccepted: 08.04.2024  \nPublished: 17.04.2024  \nIssue: 07.2024  \nCopyright: © 2024 This is an open access article under the terms of the CC-BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nKEYWORDS. Concrete structures, Machine learning, Electromechanical impedance, Damage detection, Damage repair.  \nA. Anjum et alii, Frattura edIntegrità Strutturale, 69 (2024) 43-59; DOI: 10.3221/IGF-ESIS.69.04  \nINTRODUCTION  \nM anual visual inspection is the primary approach for assessing the condition of civil infrastructure, ensuring it  \nmeets safety and serviceability standards. This process, carried out by qualified inspectors or structural engineers,  \ninvolves identifying defects like cracks, damage, corrosion, and more in elements such as beams, columns, bridges, and roads [1] . It is conducted at regular intervals or following disasters to prevent accidents resulting from inadequate inspection. The gathered data helps predict future conditions, aids investment planning, and optimizes resource allocation for maintenance and repairs, ensuring ongoing infrastructure functionality.  \nCivil structures are vital for the global economy and people's daily lives but are aging and facing significant wear [2,3] . Replacing them is impractical due to cost and resource constraints. Engineers have developed strategies to enhance safety and structural integrity [4] . In the past decade, the adoption of computer vision methods in civil engineering has surged, thanks to affordable, high-quality visual sensing technology. This progress is evident in integrating computer vision modules in modern structural health monitoring (SHM) frameworks [5] .  \nIn computer science, SHM data analysis aims to transform sensor data into meaningful information and knowledge about structures. This knowledge is crucial for various applications, including life-cycle management and lifetime forecasting [6] . Two main approaches are used to assess the structural condition of civil engineering structures: physics-based and datadriven methods [7] . Physics-based methods create models based on the structure's physical characteristics and compare them with sensor data [8], demanding significant processing resources [9] . On the other hand, AI, which found early success in fields like robotics and data mining [10], has gained traction in civil engineering [11], offering knowledge-based systems, fuzzy logic algorithms, and artificial neural networks (ANNs) [12,13] . ML, a subset of AI, is also","cbCaii4wZ2eRCfWy","https://ap.wps.com/l/cbCaii4wZ2eRCfWy","pdf",2224995,1,17,"English","en",105,"# Introduction\n## Machine learning in structural health monitoring\n# Machine learning\n## Data mining and prediction principles\n## Challenges and validation with mechanics concepts","[{\"question\":\"What is the main purpose of structural health monitoring in civil engineering?\",\"answer\":\"It transforms sensor data into meaningful information and knowledge about structures, supporting applications such as life-cycle management and lifetime forecasting.\"},{\"question\":\"How do physics-based and data-driven approaches differ in assessing structural condition?\",\"answer\":\"Physics-based methods build models from the structure’s physical characteristics and compare them with sensor data, while data-driven methods rely on learning from data to infer condition-related knowledge.\"},{\"question\":\"Why is machine learning increasingly adopted in civil engineering structural monitoring?\",\"answer\":\"Machine learning helps improve accuracy by understanding data structures and fitting them into predictive models, gaining traction alongside advances in AI and computer vision for SHM.\"}]","Civil structural health monitoring and machine learning - 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