An LSTM RNN proposal for surrogate modeling the dynamic response of buried structures to earthquake plane waves in soil half-spaces

This paper presents a variant of the long short-term memory (LSTM) recurrent neural network for surrogate modeling and predicting the seismic response of buried structures to in-plane inclined shear waves in heterogeneous soil profiles. The proposed model is designed to accept scalar and time-dependent random variables as input features to facilitate Monte Carlo (MC) based uncertainty quantification tasks where both the earthquake wave field and system properties are sources of uncertainty. To this end, it employs short-duration synthetic time histories for training instead of actual earthquake records to ensure efficiency. We discuss several configurations to evaluate the performance of the proposed methodology in predicting the tunnel response at different locations. We use two application examples concerning forward uncertainty quantification and sensitivity analysis to assess further the efficacy and accuracy of the proposed surrogate modeling approach.

Automated summary

This paper presents a variant of LSTM recurrent neural network for modeling and predicting the seismic response of buried structures to uncertain shear waves in heterogeneous soil profiles. The proposed model can accept uncertain input features and uses synthetic time histories for training to improve efficiency. The results demonstrate that this approach can effectively predict tunnel response at different locations and show high accuracy in forward uncertainty quantification and sensitivity analysis.