  Reinforced concrete 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 convolution neural networks have
been adopted in recent research to rapidly quantify the damage state of
structures. In this paper, an advanced object detection neural network, named
YOLO-v2, is implemented which achieves 98.2% and 84.5% average precision in
training and testing, respectively. The proposed YOLO-v2 is used in combination
with the classification neural network, which improves the identification
accuracy for critical damage state of reinforced concrete structures by 7.5%.
The improved classification procedures allow engineers to rapidly and more
accurately quantify the damage states of the structure, and also localize the
critical damage features. The identified damage state can then be integrated
with the state-of-the-art performance evaluation framework to quantify the
financial losses of critical reinforced concrete 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.
