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.

中文翻译：

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使用双卷积神经网络的钢筋混凝土建筑物基于灾后图像的损伤检测和维修成本估算

钢筋混凝土（RC）建筑物在世界范围内普遍使用。随着全球范围内最近发生的地震，快速的结构损坏检查和维修成本评估对于建筑物所有者和决策者做出明智的风险管理决策至关重要。为了提高这种检查的效率，在最近的研究中采用了基于卷积神经网络的先进计算机视觉技术来快速量化结构的损伤状态（DS）。本文实现了一个名为YOLOv2的高级对象检测神经网络，该网络在训练和测试中的平均精度分别达到98.2％和84.5％。提出的YOLOv2与分类神经网络结合使用，可将RC结构的关键DS的识别精度提高7.5％。改进的分类程序使工程师能够快速，更准确地对结构的DS进行量化，并定位关键的损坏特征。然后，可以将识别出的DS与最新的绩效评估框架集成在一起，以量化关键RC建筑物的财务损失。业主和决策者可以使用结果在强烈地震震动后立即做出明智的风险管理决策。因此，可以快速分配资源以提高社区的弹性。然后，可以将识别出的DS与最新的绩效评估框架集成在一起，以量化关键RC建筑物的财务损失。业主和决策者可以使用结果，在发生强烈地震后立即做出明智的风险管理决策。因此，可以快速分配资源以提高社区的弹性。然后，可以将识别出的DS与最新的绩效评估框架集成在一起，以量化关键RC建筑物的财务损失。业主和决策者可以使用结果，在发生强烈地震后立即做出明智的风险管理决策。因此，可以快速分配资源以提高社区的弹性。