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A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms,
Xiao Pan1,2, Tony T Y Yang2, Jun Li3
1Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong.
Archives of Computational Methods in Engineering : State of the Art Reviews
|October 13, 2025
Summary
This review examines data-driven computer vision for structural damage detection over the last five years. It highlights progress, identifies gaps in timber structure assessment, and suggests future research directions for infrastructure monitoring.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Computer vision is increasingly vital for civil infrastructure inspection and monitoring.
- Data-driven algorithms are revolutionizing structural damage detection.
Purpose of the Study:
- To systematically review recent computer vision algorithms for structural damage detection (past 5 years).
- To analyze applications across different structural materials (concrete, steel, masonry, timber) and damage assessment levels (recognition, localization, quantification).
- To identify technological gaps between computer vision capabilities and current inspection practices.
Main Methods:
- Systematic literature review of data-driven computer vision algorithms.
- Analysis of algorithm architectures and their innovations.
- Classification of applications by structural material and damage assessment hierarchy.
- Scrutiny of existing inspection guidelines.
Main Results:
- Prevalent computer vision models and architectural innovations are reviewed.
- Applications are categorized by material type and damage assessment level.
- Under-exploration of computer vision for timber structure damage assessment is highlighted.
- Key technological gaps between current methods and manual inspection are identified.
Conclusions:
- Computer vision shows significant potential for structural damage detection, but timber structures require more research.
- Future research should focus on integrating computer vision with multimodal large language models, sensor fusion, and mobile inspection.
- Bridging the gap between advanced algorithms and practical field inspection is crucial for infrastructure safety.
