Real-time regional seismic damage assessment framework based on long short-term memory neural network
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 frame- work for real-time regional seismic damage assessment that is based on a Long Short-Term Memory (LSTM) neural network architecture. The proposed frame- work is not specially designed for individual structural types, but offers rapid estimates at regional scale. The framework is built around a workflow that estab- lishes 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 verifica- tion, andmodel utilization for damage prediction. The influence ofvarious 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 demon- strate that the developed LSTM framework can perform damage assessment in real time at regional scalewith high prediction accuracy and acceptable variance.
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