Probabilistic Prediction of Consolidation Settlement and Pore Water Pressure Using Variational Autoencoder Neural Network

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.