Computer-Vision and Machine-Learning-Based Seismic Damage Assessment of Reinforced Concrete Structures

Abstract

Seismic damage assessment of reinforced concrete (RC) structures is a vital issue for post-earthquake evaluation. Conventional onsite inspection depends greatly on subjective judgments and engineering experiences of human inspectors, and the efficiency is limited to large-scale urban areas. This study proposes a computer-vision and machine-learning-based seismic damage assessment framework for RC structures. A refined Park-Ang model is built to express the coupled effects of structural ductility and energy dissipation, which reflects the nonlinear seismic damage accumulation and generates a synthetical seismic damage indicator within 0~1 using hysteretic curve data. A deep neural network is established to regress the damage indicator using damage-related and design-related parameters as inputs. The results show that the correlation coefficients between the predicted and actual seismic damage index exceed 0.98, and the predicted seismic damage index is unbiased and stable without overfitting. Furthermore, the effectiveness, robustness, and generalization ability of the proposed method are verified.