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Shear stress history controls the pore pressure response in liquefiablesoils. The excess pore pressure does not increase under cyclic loading whenshear stress amplitude is lower than the peak prior amplitude -- the shieldingeffect. Many sophisticated constitutive models fail to capture the shieldingeffect observed in the cyclic liquefaction experiments. We develop adata-driven machine learning model based on the LSTM neural network to capturethe liquefaction response of soils under cyclic loading. The LSTM model istrained on 12 laboratory cyclic simple shear tests on Nevada sand in loose anddense conditions subjected to different cyclic simple shear loading conditions.The LSTM model features include the relative density of soil and the previousstress history to predict the pore water pressure response. The LSTM modelsuccessfully replicates the pore pressure response for three cyclic simple testresults considering the shielding and density effects.

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