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

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.

A Multi-Feature Fusion-Based Method for Crater Extraction of Airport Runways in Remote-Sensing Images

“…A blockade is an important sign of the destruction of an airport runway and the crater size on the surface of the runway is an important factor that affects the probability of blockade [3]. With the development of remote-sensing image processing, the damage information obtained from remote-sensing images improves the convenience of damage assessment [4,5].…”