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 framework for real‐time regional seismic damage assessment that is based on a Lo...

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Bibliographic Details
Published in:Computer-aided civil and infrastructure engineering 2021-04, Vol.36 (4), p.504-521
Main Authors: Xu, Yongjia, Lu, Xinzheng, Cetiner, Barbaros, Taciroglu, Ertugrul
Format: Article
Language:English
Subjects:
Computer architecture
Damage assessment
Earthquake damage
Earthquakes
Ground motion
Neural networks
Regional analysis
Resampling
Seismic response
Structural damage
Workflow
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Summary: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.
ISSN:1093-9687
1467-8667
DOI:10.1111/mice.12628