BiLSTM-Based Soil–Structure Interface Modeling
Publication: International Journal of Geomechanics
Volume 21, Issue 7
Abstract
Deep learning (DL) algorithm bidirectional long short-term memory (BiLSTM) neural network is employed to model behaviors of the soil–structure interface in this study, as a pioneer research work to investigate the feasibility of using DL to model interface behaviors. Datasets are collected from 12 constant normal stress and 20 constant normal stiffness sand–structure interface tests. A modeling framework with the integration of BiLSTM is thereafter proposed. The results indicate that the BiLSTM-based model can accurately capture the responses of interface behaviors including volumetric dilatancy and strain hardening on the dense samples and volumetric contraction and strain softening on the loose samples, respectively. The effects of surface roughness, soil relative density, and normal stiffness on the interface behaviors are also investigated using the BiLSTM-based model. The predicted normal stress, shear stress, and normal displacement show good agreement with measured results.
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Acknowledgments
This research was financially supported by the Research Grants Council (RGC) of Hong Kong Special Administrative Region Government (HKSARG) of China (Grant Nos. UGC/FDS13/E02/20, 15217220, and N_PolyU534/20).
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International Journal of Geomechanics
Volume 21 • Issue 7 • July 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 29, 2020
Accepted: Feb 9, 2021
Published online: Apr 21, 2021
Published in print: Jul 1, 2021
Discussion open until: Sep 21, 2021
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