A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading

TL;DR

This paper introduces a machine learning model using LSTM neural networks to accurately predict pore pressure response in liquefiable sands under cyclic loading, capturing complex effects like shielding and density.

Contribution

The study develops a novel data-driven LSTM model that effectively predicts cyclic liquefaction response, outperforming traditional constitutive models in capturing shielding effects.

Findings

LSTM model accurately predicts pore pressure response in liquefiable sands.

Model captures shielding effect and density influence on liquefaction.

Successful validation on laboratory cyclic shear tests.

Abstract

Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude is lower than the peak prior amplitude -- the shielding effect. Many sophisticated constitutive models fail to capture the shielding effect observed in the cyclic liquefaction experiments. We develop a data-driven machine learning model based on the LSTM neural network to capture the liquefaction response of soils under cyclic loading. The LSTM model is trained on 12 laboratory cyclic simple shear tests on Nevada sand in loose and dense conditions subjected to different cyclic simple shear loading conditions. The LSTM model features include the relative density of soil and the previous stress history to predict the pore water pressure response. The LSTM model successfully replicates the pore pressure response for three cyclic simple test results considering the shielding and density effects.