Civil structural health monitoring and machine learning: a comprehensive review
In 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 o...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2024-07-01
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| Series: | Fracture and Structural Integrity |
| Subjects: | |
| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4789/4043 |
| _version_ | 1826902651262992384 |
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| author | Asraar Anjum Meftah Hrairi Abdul Aabid Norfazrina Yatim Maisarah Ali |
| author_facet | Asraar Anjum Meftah Hrairi Abdul Aabid Norfazrina Yatim Maisarah Ali |
| author_sort | Asraar Anjum |
| collection | DOAJ |
| description | In 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. |
| first_indexed | 2024-04-24T07:22:46Z |
| format | Article |
| id | doaj.art-141ae5a0aeef4dc7b970b496503b4a91 |
| institution | Directory Open Access Journal |
| issn | 1971-8993 |
| language | English |
| last_indexed | 2025-02-17T07:43:45Z |
| publishDate | 2024-07-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj.art-141ae5a0aeef4dc7b970b496503b4a912025-01-03T01:28:27ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932024-07-011869435910.3221/IGF-ESIS.69.0410.3221/IGF-ESIS.69.04Civil structural health monitoring and machine learning: a comprehensive reviewAsraar AnjumMeftah HrairiAbdul AabidNorfazrina YatimMaisarah AliIn 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.https://www.fracturae.com/index.php/fis/article/view/4789/4043concrete structuresmachine learningelectromechanical impedancedamage detectiondamage repair |
| spellingShingle | Asraar Anjum Meftah Hrairi Abdul Aabid Norfazrina Yatim Maisarah Ali Civil structural health monitoring and machine learning: a comprehensive review Fracture and Structural Integrity concrete structures machine learning electromechanical impedance damage detection damage repair |
| title | Civil structural health monitoring and machine learning: a comprehensive review |
| title_full | Civil structural health monitoring and machine learning: a comprehensive review |
| title_fullStr | Civil structural health monitoring and machine learning: a comprehensive review |
| title_full_unstemmed | Civil structural health monitoring and machine learning: a comprehensive review |
| title_short | Civil structural health monitoring and machine learning: a comprehensive review |
| title_sort | civil structural health monitoring and machine learning a comprehensive review |
| topic | concrete structures machine learning electromechanical impedance damage detection damage repair |
| url | https://www.fracturae.com/index.php/fis/article/view/4789/4043 |
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