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Pre- and post-earthquake regional loss assessment using deep learning

机译：使用深度学习的地震后和地震后区域损失评估

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摘要

As urban systems become more highly sophisticated and interdependent, their vulnerability to earthquake events exhibits a significant level of uncertainties. Thus, community-level seismic risk assessments are indispensable to facilitate decision making for effective hazard mitigation and disaster responses. To this end, new frameworks for pre- and post-earthquake regional loss assessments are proposed using deep learning methods. First, to improve the accuracy of the response prediction of individual structures during the pre-earthquake loss assessment, a widely used nonlinear static procedure is replaced by the recently developed probabilistic deep neural network model. The variabilities of the nonlinear responses of a structural system given the seismic intensity can be quantified during the loss assessment process. Second, to facilitate near-real-time post-earthquake loss assessments, an adaptive algorithm, which identifies the optimal number and locations of sensors in a given urban area, is proposed. Using a deep neural network that estimates area-wide structural damage given the spatial distribution of the seismic intensity levels as a surrogate model, the algorithm adaptively places additional sensors at property lots at which errors from surrogate estimations of the structural damage are the greatest. Note that the surrogate model is constructed before earthquake events using simulated datasets. To test and demonstrate the proposed frameworks, the paper introduces thorough numerical investigations of two hypothetical urban communities. The proposed frameworks using the deep learning methods are expected to make critical advances in pre- and post-earthquake regional loss assessments.
机译：随着城市系统变得更加复杂和相互依存的，他们对地震事件的脆弱性表现出显着的不确定性。因此，社区级地震风险评估是必不可少的，以促进有效危害缓解和灾害反应的决策。为此，采用深入学习方法提出了地震前和地震后区域损失评估的新框架。首先，为了提高地震预损失评估期间各个结构的响应预测的准确性，广泛使用的非线性静态程序被最近开发的概率深神经网络模型所取代。在损失评估过程中可以量化赋予地震强度的结构系统的非线性响应的变形性。其次，为了促进近实时地震后损失评估，提出了一种自适应算法，其识别给定城区的传感器的最佳数量和位置。使用深神经网络估计区域宽的结构损坏，所述抗震强度水平作为代理模型的空间分布，该算法适自适应地将额外的传感器放置在结构批次的诸如结构损伤的逆价估计的误差中最大。请注意，使用模拟数据集在地震事件之前构建代理模型。为了测试和展示所提出的框架，本文介绍了两个假想城市社区的彻底数值调查。建议使用深层学习方法的框架预计将在地震前和后地区区域损失评估中产生关键进展。

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