Probabilistic Prediction of Consolidation Settlement and Pore Water Pressure Using Variational Autoencoder Neural Network
Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 149, Issue 1
Abstract
This paper explores the use of a variational autoencoder to predict the embankment settlement and pore water pressure based on monitoring data. A variational autoencoder can learn intrinsic patterns in embankment behavior through unsupervised deep machine learning. The proposed approach implemented the observational method efficiently because updating soil parameters was no longer a necessary step, unlike in previous research. The embankment response was predicted directly through Gibbs sampling, which involves an iterative encoding and decoding process in the variational autoencoder. The variational autoencoder was trained using simulated embankment responses from the numerical (Plaxis) model. The approach was applied at the Ballina site to predict the embankment response, based on monitoring data with varying time periods. The prediction intervals captured the actual trends satisfactorily, with the intervals becoming more aligned with actual values as more monitoring data were incorporated. The predictions were also more reasonable, compared to those based entirely on representative soil parameters from laboratory or in-situ tests. The variational autoencoder was also applied to another case involving synthetic monitoring data based on the Ballina site, which demonstrates the capability of the variational autoencoder to predict multiple scenarios of embankment behavior.
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Data Availability Statement
The code for the variational autoencoder is provided in Github (https://github.com/Horace-Lo/Variational-autoencoder), which includes examples on MNIST digits and Plaxis simulated responses.
Acknowledgments
The authors appreciate the financial support from the Singapore Ministry of Education (MOE), Award No. R-302-000-194-114. The authors would like to thank TC304/TC304 (ISSMGE) for providing the synthetic data and would like to thank Dr. Guan Lim for valuable advice on the Barron and Hansbo method.
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Published In
Journal of Geotechnical and Geoenvironmental Engineering
Volume 149 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Nov 13, 2021
Accepted: Aug 31, 2022
Published online: Oct 31, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 31, 2023
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