论文标题
深贝叶斯U网络，可高效，稳健和可靠的灾后损害定位
Deep Bayesian U-Nets for Efficient, Robust and Reliable Post-Disaster Damage Localization
论文作者
论文摘要
地震后，灾后检查对紧急情况管理至关重要。地震后立即在民事基础设施条件下获得数据的可用性对于紧急管理至关重要。利益相关者要求此信息采取有效的行动并更好地从灾难中恢复。由数据驱动的SHM表现出了巨大的承诺，可以实时实现这一目标。有几项建议使用深度学习从不同的输入来源自动化检查过程。文献中现有的模型仅提供最终的预测输出，而将此类模型用于安全至关重要的评估的风险不应忽略。本文致力于开发深贝叶斯U网络，其中预测的不确定性是该模型的第二个输出，这是通过测试时间中的蒙特卡洛辍学采样而实现的。基于网格样的数据结构，重新审视语义损伤分割（SDS）的概念。与图像分割相比，这表明损害诊断需要更高的精度。为了验证和测试所提出的框架，利用了基准数据集，10,800个非线性响应历史记录分析在10层10层2D 2D钢筋混凝土矩框架上。与基准SDS模型相比，贝叶斯模型具有更高的鲁棒性，具有增强的全球和平均级别精度。最后，通过监视不同预测的软玛克斯类方差来研究模型的不确定性输出。结果表明，类方差与模型犯错误的位置良好相关。该输出可以与预测结果结合使用，以提高结构检查中该数据驱动框架的可靠性。
Post-disaster inspections are critical to emergency management after earthquakes. The availability of data on the condition of civil infrastructure immediately after an earthquake is of great importance for emergency management. Stakeholders require this information to take effective actions and to better recover from the disaster. The data-driven SHM has shown great promises to achieve this goal in near real-time. There have been several proposals to automate the inspection process from different sources of input using deep learning. The existing models in the literature only provide a final prediction output, while the risks of utilizing such models for safety-critical assessments should not be ignored. This paper is dedicated to developing deep Bayesian U-Nets where the uncertainty of predictions is a second output of the model, which is made possible through Monte Carlo dropout sampling in test time. Based on a grid-like data structure, the concept of semantic damage segmentation (SDS) is revisited. Compared to image segmentation, it is shown that a much higher level of precision is necessary for damage diagnosis. To validate and test the proposed framework, a benchmark dataset, 10,800 nonlinear response history analyses on a 10-story-10-bay 2D reinforced concrete moment frame, is utilized. Compared to the benchmark SDS model, Bayesian models exhibit superior robustness with enhanced global and mean class accuracies. Finally, the model's uncertainty output is studied by monitoring the softmax class variance of different predictions. It is shown that class variance correlates well with locations where the model makes mistakes. This output can be used in combination with the prediction results to increase the reliability of this data-driven framework in structural inspections.
