Abstract

Earthquakes pose a significant threat to the safety and stability of buildings, requiring prompt and accurate assessment of structural damage for effective recovery and reconstruction efforts. This research article investigates the use of computer vision and augmented reality techniques to develop an intelligent system for damage assessment in post-earthquake buildings. By leveraging image processing, deep learning, and augmented reality visualization, this approach aims to provide reliable, automated, and efficient damage assessment, enabling rapid decision-making and prioritization of resources for reconstruction efforts.

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 and AR. Firstly, this paper proposed a fusion mechanism for the CV and AR of the assessment method. Secondly, the CNN 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.