Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review
Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitori...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4855 |
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| author | Vijay Prakash Carl James Debono Muhammad Ali Musarat Ruben Paul Borg Dylan Seychell Wei Ding Jiangpeng Shu |
| author_facet | Vijay Prakash Carl James Debono Muhammad Ali Musarat Ruben Paul Borg Dylan Seychell Wei Ding Jiangpeng Shu |
| author_sort | Vijay Prakash |
| collection | DOAJ |
| description | Concrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring (SHM) to ensure their safety and long-term durability. Therefore, a narrative review was conducted to examine the use of Artificial Intelligence (AI)-integrated techniques in the SHM of concrete bridges for more effective monitoring. Moreover, this review also examined significant damage observed in various types of concrete bridges, with particular emphasis on concrete cracking, detection methods, and identification accuracy. Evidence points to the fact that the conventional SHM of concrete bridges relies on manual inspections that are time-consuming, error-prone, and require frequent checks, while AI-driven SHM methods have emerged as promising alternatives, especially through Machine Learning- and Deep Learning-based solutions. In addition, it was noticeable that integrating multimodal AI approaches improved the accuracy and reliability of concrete bridge assessments. Furthermore, this review is essential as it also addresses critical gaps in SHM approaches and suggests developing more accurate detection techniques, providing enhanced spatial resolution for monitoring concrete bridges. |
| format | Article |
| id | doaj-art-13b672123bbe445d9b5638db4e0af22e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-13b672123bbe445d9b5638db4e0af22e2025-08-20T01:49:09ZengMDPI AGApplied Sciences2076-34172025-04-01159485510.3390/app15094855Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative ReviewVijay Prakash0Carl James Debono1Muhammad Ali Musarat2Ruben Paul Borg3Dylan Seychell4Wei Ding5Jiangpeng Shu6Department of Communications and Computer Engineering, Faculty of ICT, University of Malta, MSD 2080 Msida, MaltaDepartment of Communications and Computer Engineering, Faculty of ICT, University of Malta, MSD 2080 Msida, MaltaDepartment of Communications and Computer Engineering, Faculty of ICT, University of Malta, MSD 2080 Msida, MaltaFaculty for the Built Environment, University of Malta, MSD 2080 Msida, MaltaDepartment of Artificial Intelligence, Faculty of ICT, University of Malta, MSD 2080 Msida, MaltaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaConcrete has been one of the most essential building materials for decades, valued for its durability, cost efficiency, and wide availability of required components. Over time, the number of concrete bridges has been drastically increasing, highlighting the need for timely structural health monitoring (SHM) to ensure their safety and long-term durability. Therefore, a narrative review was conducted to examine the use of Artificial Intelligence (AI)-integrated techniques in the SHM of concrete bridges for more effective monitoring. Moreover, this review also examined significant damage observed in various types of concrete bridges, with particular emphasis on concrete cracking, detection methods, and identification accuracy. Evidence points to the fact that the conventional SHM of concrete bridges relies on manual inspections that are time-consuming, error-prone, and require frequent checks, while AI-driven SHM methods have emerged as promising alternatives, especially through Machine Learning- and Deep Learning-based solutions. In addition, it was noticeable that integrating multimodal AI approaches improved the accuracy and reliability of concrete bridge assessments. Furthermore, this review is essential as it also addresses critical gaps in SHM approaches and suggests developing more accurate detection techniques, providing enhanced spatial resolution for monitoring concrete bridges.https://www.mdpi.com/2076-3417/15/9/4855concrete bridge inspectionstructural health monitoringmachine learningcrack detectionautomationdigitalisation |
| spellingShingle | Vijay Prakash Carl James Debono Muhammad Ali Musarat Ruben Paul Borg Dylan Seychell Wei Ding Jiangpeng Shu Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review Applied Sciences concrete bridge inspection structural health monitoring machine learning crack detection automation digitalisation |
| title | Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review |
| title_full | Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review |
| title_fullStr | Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review |
| title_full_unstemmed | Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review |
| title_short | Structural Health Monitoring of Concrete Bridges Through Artificial Intelligence: A Narrative Review |
| title_sort | structural health monitoring of concrete bridges through artificial intelligence a narrative review |
| topic | concrete bridge inspection structural health monitoring machine learning crack detection automation digitalisation |
| url | https://www.mdpi.com/2076-3417/15/9/4855 |
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