A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Computer vision techniques have gained great traction in civil infrastructure inspection and monitoring. This paper conducted a systematic review of recent data-driven computer vision algorithms in structural damage detection published during the past 5 years. The theories of prevalent computer vision models are first reviewed with an emphasis on the progressive innovation in algorithms’ architecture. Then, recent applications of computer vision models for structural damage evaluation are discussed, which are classified into different structural categories by their material types (i.e., concrete, steel, masonry, timber) at three hierarchical levels including damage recognition, localization, and quantification. In particular, the paper also highlights the current state of using computer vision for damage assessment of timber structures, which remains under-explored compared to concrete and steel structures. Next, the paper scrutinizes existing structural damage inspection guidelines to identify key technological gaps between the capability of existing computer vision methods and manual inspection practices in the field. Finally, the paper summarizes existing challenges and recommends future research opportunities including the integration of computer vision methods with multimodal large language models, sensor-fusion, and mobile inspection approaches.
Date Issued
2025-10-01
Date Acceptance
2025-03-20
Citation
Archives of Computational Methods in Engineering, 2025, 32 (10), pp.4587-4619
ISSN
1134-3060
Publisher
Springer
Start Page
4587
End Page
4619
Journal / Book Title
Archives of Computational Methods in Engineering
Volume
32
Issue
10
Copyright Statement
© Crown 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/41078604
PII: 10279
Subjects
BOLT-LOOSENING DETECTION
CIVIL INFRASTRUCTURE
CLASSIFICATION
Computer Science
Computer Science, Interdisciplinary Applications
Computer vision
CONCRETE
CRACK DETECTION
Deep learning
Engineering
Engineering, Multidisciplinary
IDENTIFICATION
Image processing
INSPECTION
Machine learning
Mathematics
Mathematics, Interdisciplinary Applications
Physical Sciences
Science & Technology
Sensor fusion
Structural damage detection
Structural health monitoring
Technology
Unmanned aerial and ground vehicles
Publication Status
Published
Coverage Spatial
Netherlands
Date Publish Online
2025-04-03