[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124933-en":3,"doc-seo-124933-105":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},124933,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Civil Structural Health Monitoring and Machine Learning - A Comprehensive Review","Over the past five years, machine learning (ML) adoption in civil engineering has accelerated, enabling stronger optimization and prediction for diverse structural challenges. By combining laboratory or field test data with ML, more robust prediction models can be built for masonry compressive strength, repair mortar performance, probable building damage scenarios, and concrete models of beams and columns. The review also examines ML paired with soft computing methods like fuzzy logic and design of experiments, highlights case applications for structural reinforcement, and outlines integration challenges and emerging research directions.","6/7/24 , 3:36 PM Scopus-Print Document  \nDocuments  \nAnjum, A.a , Hrairi, M.a , Aabid, A. b , Yatim, N.a , Ali, M.c  \nCivil structural health monitoring and machine learning: a comprehensive review  \n(2024) Frattura ed Integrita Strutturale , 18 (69), pp. 43-59.  \nDOI: 10.3221/IGF-ESIS.69.04  \na Department of Mechanical and Aerospace Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, Kuala Lumpur, 50728, Malaysia  \nb Department of Engineering Management, College of Engineering, Prince Sultan University, PO BOX 66833, Riyadh, 11586, Saudi Arabia  \nc Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, P.O. Box 10, Kuala Lumpur, 50728, Malaysia  \nAbstract  \nIn the past five years, the implementation of machine learning (ML) techniques has surged in civil engineering applications, particularly for optimizing and predicting solutions to various challenges. More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. These models may be used to estimate the compressive strength of masonry or repair mortars, probable damage scenarios in buildings, concrete models, beams, and columns for determining the mechanical characteristics of materials, damage detection in civil structures, and so on. This comprehensive review aims to clarify the array of ML-based methods employed in civil engineering, specifically focusing on their efficacy in strengthening energy efficiency and cost-effectiveness. In combination with ML, the review explores corresponding soft computing methodologies such as fuzzy logic (FL) and design of experiments (DOE) . A variety of case examples that highlight the versatility of these approaches, particularly in applications linked to structural reinforcement, enhance the story. The review navigates difficulties associated with the integration of soft computing in civil engineering and expands its scope to include emerging research directions. This synthesis of advanced artificial intelligence (AI) serves as a guide, providing new researchers with knowledge about a developing field. These methods could revolutionize the current situation by providing creative answers to complex problems that arise in civil structural applications. © 2024, Gruppo Italiano Frattura. All rights reserved.  \nAuthor Keywords  \nConcrete structures; Damage detection; Damage repair; Electromechanical impedance; Machine learning  \nIndex Keywords  \nCompressive strength, Concrete construction, Cost effectiveness, Cost engineering, Damage detection, Design of experiments, Energy efficiency, Fuzzy logic, Repair, Soft computing, Structural health monitoring; Civil engineering applications, Civil structural health monitoring, Damage repair, Electromechanical impedance, Machine learning techniques, Machine-learning, Masonry mortars, Prediction modelling, Robust predictions, Test data; Machine learning  \nReferences  \n Koch, C. , Georgieva, K. , Kasireddy, V., Akinci, B. , Fieguth, P.  \nA review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure  \n(2015) Adv. Eng. Informatics , 29 (2), pp. 196-210.  \n Flah, M. , Nunez, I. , Ben Chaabene, W. , Nehdi, M.L.  \nMachine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review  \n(2020) Arch. Comput. Methods Eng,(0123456789)  \n Zingoni, A.  \nStructural health monitoring and damage detection  \n(2020) Prog. Struct. Eng. Mech. Comput, pp. 145-166.  \n01096  \n Karballaeezadeh, N. , Mohammadzadeh S, D. , Shamshirband, S. , Hajikhodaverdikhan, P. , Mosavi, A. , Chau, K. wing  \nPrediction of remaining service life of pavement using an optimized support vector  \n[https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1798818006&eid=2-s2.0-85192374516&sort=&clicke](https://www.scopus.com/citation/print.uri?origin=recordpage&sid=&src=s&stateKey=OFD_1798818006&eid=2-s2.0-851","cbCaivjknwzAsvIl","https://ap.wps.com/l/cbCaivjknwzAsvIl","pdf",326977,1,11,"English","en",105,"# Abstract\n## ML methods in civil engineering\n## Soft computing integration (fuzzy logic, DOE)\n## Applications and case examples\n## Challenges and emerging research directions","[{\"question\":\"How does machine learning improve civil structural health monitoring tasks?\",\"answer\":\"ML leverages laboratory or field test data to build more robust prediction models for strength estimation, damage scenarios, and structural characteristics.\"},{\"question\":\"Which soft computing approaches are discussed alongside machine learning?\",\"answer\":\"The review includes fuzzy logic and design of experiments (DOE) as complementary soft computing methodologies.\"},{\"question\":\"What benefits and use cases are highlighted for ML and soft computing in civil engineering?\",\"answer\":\"Case examples emphasize structural reinforcement applications and the potential to enhance energy efficiency and cost-effectiveness.\"}]","Civil Structural Health Monitoring and Machine Learning - 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