Post‐earthquake damage recognition and condition assessment of bridges using UAV integrated with deep learning approach

Abstract

Rapid and accurate assessment of the damage to bridge structures after an earthquake can provide a basis for decision‐making regarding post‐earthquake emergency work. However, the traditional structural damage inspection techniques are subjective, time‐consuming, and inefficient. This paper proposed a framework for rapid post‐earthquake structural damage inspection and condition assessment by integrating the technologies of satellite, unmanned aerial vehicle (UAV), and smartphone with the deep learning approach. The images of structural components of post‐earthquake bridges can be obtained by UAVs and smartphones. Furthermore, the multi‐task high‐resolution net (MT‐HRNet) model was adopted to recognize the structural components and damage conditions by weighting and combining the loss functions of a single‐task HRNet model. The performance of the proposed MT‐HRNet model and the single‐task HRNet model was verified based on the Tokaido dataset, which includes 2000 images of post‐earthquake bridges. The results showed that the MT‐HRNet model and the HRNet model exhibited equivalent recognition accuracy, while the number of floating‐point‐operations (FLOPs) and the parameters of the MT‐HRNet model were reduced by 46.48% and 49.58% compared with the HRNet model. In addition, a method for the determination of the safety risk level of the post‐earthquake bridge structures was developed, and the evaluation indices were established by considering the damage type, the spalling area, and the width of cracks as well as the recognition statistics of all images in Tokaido dataset. This study will provide a valuable reference for the rapid determination of structural safety level and the corresponding treatment measures of post‐earthquake bridges.