Postdisaster image‐based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks

Abstract Reinforced concrete (RC) buildings are commonly used around the world. With recent earthquakes worldwide, rapid structural damage inspection and repair cost evaluation are crucial for building owners and policy makers to make informed risk management decisions. To improve the efficiency of such inspection, advanced computer vision techniques based on convolutional neural networks have been adopted in recent research to rapidly quantify the damage state (DS) of structures. In this article, an advanced object detection neural network, named YOLOv2, is implemented, which achieves 98.2% and 84.5% average precision in training and testing, respectively. The proposed YOLOv2 is used in combination with the classification neural network, which improves the identification accuracy for critical DS of RC structures by 7.5%. The improved classification procedures allow engineers to rapidly and more accurately quantify the DSs of the structure, and also localize the critical damage features. The identified DS can then be integrated with the state‐of‐the‐art performance evaluation framework to quantify the financial losses of critical RC buildings. The results can be used by the building owners and decision makers to make informed risk management decisions immediately after the strong earthquake shaking. Hence, resources can be allocated rapidly to improve the resiliency of the community.