Crack Detection in Civil Infrastructure: A Method-Scenario Review
Ensuring the structural safety of civil infrastructure is vital for public welfare and cost-effective maintenance. Crack detection, as a key indicator of structural health, has transitioned from traditional image processing to advanced deep learning methods. This paper presents a systematic review o...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/31/e3sconf_mdoa2025_01001.pdf |
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| author | Chang Haochen Gu Weifan Guo Baohua Bassir David |
| author_facet | Chang Haochen Gu Weifan Guo Baohua Bassir David |
| author_sort | Chang Haochen |
| collection | DOAJ |
| description | Ensuring the structural safety of civil infrastructure is vital for public welfare and cost-effective maintenance. Crack detection, as a key indicator of structural health, has transitioned from traditional image processing to advanced deep learning methods. This paper presents a systematic review of crack detection technologies organized under a novel “method-scenario” framework that categorizes techniques based on their underlying algorithms and the specific application contexts (e.g., pavements, bridges, tunnels, and specialized materials). By comparing conventional image processing approaches with modern deep learning models and multi-modal fusion techniques, we highlight the strengths and limitations of each method in various real-world scenarios. Our analysis reveals critical challenges—including data scarcity, sensitivity to noise, and the gap between theoretical models and practical deployment—which must be addressed to enhance reliability and generalizability. We conclude by proposing future research directions focused on integrating physics-based constraints with lightweight computational models and establishing unified evaluation protocols to bridge the gap between laboratory precision to engineering implementation. |
| format | Article |
| id | doaj-art-ebb951757dd64418a682d723c6f4471c |
| institution | OA Journals |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-ebb951757dd64418a682d723c6f4471c2025-08-20T02:06:06ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016310100110.1051/e3sconf/202563101001e3sconf_mdoa2025_01001Crack Detection in Civil Infrastructure: A Method-Scenario ReviewChang Haochen0Gu Weifan1Guo Baohua2Bassir David3IRAMAT, UMR-7065, Université Technologique de Belfort-MontbéliardSchool of Energy Science and Engineering, Henan Polytechnic UniversitySchool of Energy Science and Engineering, Henan Polytechnic UniversityIRAMAT, UMR-7065, Université Technologique de Belfort-MontbéliardEnsuring the structural safety of civil infrastructure is vital for public welfare and cost-effective maintenance. Crack detection, as a key indicator of structural health, has transitioned from traditional image processing to advanced deep learning methods. This paper presents a systematic review of crack detection technologies organized under a novel “method-scenario” framework that categorizes techniques based on their underlying algorithms and the specific application contexts (e.g., pavements, bridges, tunnels, and specialized materials). By comparing conventional image processing approaches with modern deep learning models and multi-modal fusion techniques, we highlight the strengths and limitations of each method in various real-world scenarios. Our analysis reveals critical challenges—including data scarcity, sensitivity to noise, and the gap between theoretical models and practical deployment—which must be addressed to enhance reliability and generalizability. We conclude by proposing future research directions focused on integrating physics-based constraints with lightweight computational models and establishing unified evaluation protocols to bridge the gap between laboratory precision to engineering implementation.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/31/e3sconf_mdoa2025_01001.pdf |
| spellingShingle | Chang Haochen Gu Weifan Guo Baohua Bassir David Crack Detection in Civil Infrastructure: A Method-Scenario Review E3S Web of Conferences |
| title | Crack Detection in Civil Infrastructure: A Method-Scenario Review |
| title_full | Crack Detection in Civil Infrastructure: A Method-Scenario Review |
| title_fullStr | Crack Detection in Civil Infrastructure: A Method-Scenario Review |
| title_full_unstemmed | Crack Detection in Civil Infrastructure: A Method-Scenario Review |
| title_short | Crack Detection in Civil Infrastructure: A Method-Scenario Review |
| title_sort | crack detection in civil infrastructure a method scenario review |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/31/e3sconf_mdoa2025_01001.pdf |
| work_keys_str_mv | AT changhaochen crackdetectionincivilinfrastructureamethodscenarioreview AT guweifan crackdetectionincivilinfrastructureamethodscenarioreview AT guobaohua crackdetectionincivilinfrastructureamethodscenarioreview AT bassirdavid crackdetectionincivilinfrastructureamethodscenarioreview |