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Smart Structures and Systems Volume 31, Number 4, April 2023 , pages 311-323 DOI: https://doi.org/10.12989/sss.2023.31.4.311 |
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Computer vision and deep learning-based post-earthquake intelligent assessment of engineering structures: Technological status and challenges |
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T. Jin, X.W. Ye, W.M. Que and S.Y. Ma
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| Abstract | ||
| Ever since ancient times, earthquakes have been a major threat to the civil infrastructures and the safety of human beings. The majority of casualties in earthquake disasters are caused by the damaged civil infrastructures but not by the earthquake itself. Therefore, the efficient and accurate post-earthquake assessment of the conditions of structural damage has been an urgent need for human society. Traditional ways for post-earthquake structural assessment rely heavily on field investigation by experienced experts, yet, it is inevitably subjective and inefficient. Structural response data are also applied to assess the damage; however, it requires mounted sensor networks in advance and it is not intuitional. As many types of damaged states of structures are visible, computer vision-based post-earthquake structural assessment has attracted great attention among the engineers and scholars. With the development of image acquisition sensors, computing resources and deep learning algorithms, deep learning-based post-earthquake structural assessment has gradually shown potential in dealing with image acquisition and processing tasks. This paper comprehensively reviews the state-of-the-art studies of deep learning-based postearthquake structural assessment in recent years. The conventional way of image processing and machine learning-based structural assessment are presented briefly. The workflow of the methodology for computer vision and deep learning-based postearthquake structural assessment was introduced. Then, applications of assessment for multiple civil infrastructures are presented in detail. Finally, the challenges of current studies are summarized for reference in future works to improve the efficiency, robustness and accuracy in this field. | ||
| Key Words | ||
| computer vision; deep learning; post-earthquake structural assessment; satellite; unmanned aerial vehicle | ||
| Address | ||
| (1) T. Jin, X.W. Ye, W.M. Que, S.Y. Ma: Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China; (2) T. Jin: School of Engineering, Zhejiang University City College, Hangzhou 310015, China. | ||