Intelligent Damage Assessment for Post-Earthquake Buildings Using Computer Vision and Augmented Reality

Zhansheng Liu (), Jie Xue, Naiqiang Wang, Wenyan Bai and Yanchi Mo
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Zhansheng Liu: College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Jie Xue: College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Naiqiang Wang: College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Wenyan Bai: College of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China
Yanchi Mo: School of Engineering, Institute for Infrastructure and Environment, The University of Edinburgh, Edinburgh EH9 3FB, UK

Sustainability, 2023, vol. 15, issue 6, 1-21

Abstract: The most negative effects caused by earthquakes are the damage and collapse of buildings. Seismic building retrofitting and repair can effectively reduce the negative impact on post-earthquake buildings. The priority to repair the construction after being damaged by an earthquake is to perform an assessment of seismic buildings. The traditional damage assessment method is mainly based on visual inspection, which is highly subjective and has low efficiency. To improve the intelligence of damage assessments for post-earthquake buildings, this paper proposed an assessment method using CV (Computer Vision) and AR (Augmented Reality). Firstly, this paper proposed a fusion mechanism for the CV and AR of the assessment method. Secondly, the CNN (Convolutional Neural Network) algorithm and gray value theory are used to determine the damage information of post-earthquake buildings. Then, the damage assessment can be visually displayed according to the damage information. Finally, this paper used a damage assessment case of seismic-reinforced concrete frame beams to verify the feasibility and effectiveness of the proposed assessment method.

Keywords: damage assessment; computer vision; augmented reality; post-earthquake buildings (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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