Post-Earthquake Damage Assessment of Building Based on Deep Learning

Evaluation of the severity of structural damage caused by earthquakes is time-consuming using labor-intensive approaches. Deep learning methods have been increasingly used to detect and classify image damages, whereas few studies have been applied to assess the level of damaged structural images such as no, light, moderate, or severe damages under earthquakes. The goal of this paper is to propose an approach to automatically categorize the structures using convolutional neural networks (CNNs). Two pre-trained CNNs, namely InceptionV3 and Xception were employed. Adaptive moment estimation algorithm (Adam) was adopted to optimize the parameters of deep learning models. A gradient-weighted class activation mapping (Grad-CAM) was applied for locating damages. The testing results highlighted that InceptionV3 and Xception algorithms showed a high performance with 86.67% and 88.33% accuracy, respectively. The Grad-CAM located successfully the actual damages to structures. It depicts the ability to use CNNs for the assessment of structural damages using images.