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.

中文翻译：

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使用深度学习进行地震前后的区域损失评估

随着城市系统变得越来越复杂和相互依存，它们对地震事件的脆弱性表现出极大的不确定性。因此，社区级地震风险评估对于有效地减轻灾害和应对灾害的决策至关重要。为此，提出了使用深度学习方法进行地震前和地震后区域损失评估的新框架。首先，为了提高地震前损失评估期间单个结构的响应预测的准确性，最近开发的概率深度神经网络模型代替了广泛使用的非线性静态程序。给定地震烈度的结构系统非线性响应的变化可以在损失评估过程中进行量化。第二，为了促进近实时地震后损失评估，提出了一种自适应算法，该算法可确定给定市区内传感器的最佳数量和位置。在给定地震烈度水平的空间分布的情况下，使用深度神经网络来估计区域结构破坏的替代模型，该算法可以自适应地在属性批次处放置额外的传感器，在这些属性批次中，来自结构破坏的替代估计的误差最大。请注意，替代模型是在地震事件发生之前使用模拟数据集构建的。为了测试和演示所提出的框架，本文介绍了对两个假设的城市社区的全面数值研究。