Effective post‐earthquake response requires a prompt and accurate assessment of earthquake‐induced damage. However, existing damage assessment methods cannot simultaneously meet these requirements. This study proposes a framework for real‐time regional seismic damage assessment that is based on a Long Short‐Term Memory (LSTM) neural network architecture. The proposed framework is not specially designed for individual structural types, but offers rapid estimates at regional scale. The framework is built around a workflow that establishes high‐performance mapping rules between ground motions and structural damage via region‐specific models. This workflow comprises three main parts—namely, region‐specific database generation, LSTM model training and verification, and model utilization for damage prediction. The influence of various LSTM architectures, hyperparameter selection, and dataset resampling procedures are systematically analyzed. As a testbed for the established framework, a case study is performed on the Tsinghua University campus buildings. The results demonstrate that the developed LSTM framework can perform damage assessment in real time at regional scale with high prediction accuracy and acceptable variance.

中文翻译：

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基于长短期记忆神经网络的区域实时震害评估框架

有效的地震后响应要求对地震引起的破坏进行迅速而准确的评估。但是，现有的损害评估方法不能同时满足这些要求。这项研究提出了一个基于长期短期记忆（LSTM）神经网络架构的实时区域地震破坏评估框架。提议的框架不是专门针对单个结构类型设计的，而是提供了区域范围内的快速估算。该框架围绕一个工作流构建，该工作流通过区域特定模型在地面运动和结构破坏之间建立了高性能的映射规则。该工作流程包括三个主要部分，即区域特定的数据库生成，LSTM模型训练和验证以及用于损害预测的模型利用。各种LSTM架构的影响，系统分析超参数选择和数据集重采样过程。作为建立框架的试验床，对清华大学校园建筑进行了案例研究。结果表明，所开发的LSTM框架可以在区域范围内实时进行损害评估，具有较高的预测准确性和可接受的方差。