Systematic assessment method for post-earthquake damage of regional buildings using adaptive-network-based fuzzy inference system

Rapid damage assessment of regional buildings is critical in post-earthquake rescue and victim resettlement. In this study, a novel rapid damage assessment method for post-earthquake buildings based on multi-types of damage features was proposed. First, the roof-related damage features are extracted from UAV tilt photography with the method of multiscale segmentation and object-oriented classification. Then, the normalized digital surface model was introduced to determine the height-related damage features. Meanwhile, a lightweight CNN model was employed to realize the preliminary evaluation of building facade conditions. Finally, all obtained damage features were taken as inputs to an adaptive-network-based fuzzy inference system for damage assessment. The proposed method was characterized by the capability to comprehensively cover the damage features of regional buildings involved in the evaluation, and thus be able to obtain more accurate judgments than current methods based on a single feature. At the same time, the effectiveness and robustness of the proposed method were evaluated and demonstrated by the field test on the world's largest earthquake site in Beichuan, China, and the prediction of damage level on the testing set achieved an accuracy of 87.1%.