Affiliation:
1. School of Highway Chang'an University Xi'an China
2. Key Laboratory of Concrete and Prestressed Concrete Structures of the Ministry of Education Southeast University Nanjing China
3. Department of Civil & Environmental Engineering University of California Los Angeles California USA
Abstract
AbstractStructural seismic safety assessment is a critical task in maintaining the resilience of existing civil and infrastructures. This task commonly requires accurate predictions of structural responses under stochastic intensive ground accelerations via time‐costly numerical simulations. While numerous studies have attempted to use machine learning (ML) techniques as surrogate models to alleviate this computing burden, a large number of numerical simulations are still required for training ML models. Therefore, this study proposes a prior knowledge‐infused neural network (PKNN) for achieving efficient structural seismic response predictions and seismic safety assessment at a low computation cost. In this approach, first, the prior knowledge inherently within a theoretical dynamic model of a structure is infused into a neural network. Then, by utilizing the few‐shot incremental learning technique, this network would be further fine‐tuned by only a few numerical simulations. The resulting PKNN would be able to accurately predict the seismic response of a structure and facilitate seismic safety assessment. In this study, the PKNN's accuracy and computational efficiency are validated on a typical frame structure. The results revealed that the proposed PKNN could be used for accurately predicting the structural seismic responses and assessing the structural safety under a low computational cost.
Funder
National Key Research and Development Program of China
National Natural Science Foundation of China
Natural Science Foundation of Jiangsu Province
Fundamental Research Funds for the Central Universities
Cited by
4 articles.
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