A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures

TL;DR

This paper introduces a hybrid FEM-MPM approach to model and predict the failure and runout of liquefaction-induced tailings dams during earthquakes, validated on a historical case and highlighting the importance of strain-softening effects.

Contribution

The paper presents a novel hybrid FEM-MPM method that effectively models the entire failure process of liquefaction-induced tailings dam failures, including initiation and runout, with insights into transfer timing and material properties.

Findings

Hybrid FEM-MPM captures failure initiation and runout effectively.

Optimal transfer window depends on liquefaction depth and mesh stability.

Strain-softening beyond initial liquefaction is crucial for accurate runout prediction.

Abstract

This paper presents a hybrid Finite Element Method (FEM) and Material Point Method (MPM) approach for modeling liquefaction-induced tailings dam failures from initiation through runout. We apply this method to simulate the 1978 Mochikoshi tailings dam failure, which occurred due to seismic loading and liquefaction during an earthquake. Our approach leverages FEM to capture the initial failure mechanism and MPM to simulate the subsequent runout, exploiting the strength of each method in their respective phases of the failure process. We investigate the impact of the FEM-to-MPM transfer time on runout results, identifying an optimal transfer window. This window begins when liquefaction reaches a critical depth to fully trigger the failure and ends before excessive mesh deformation occurs. Our findings demonstrate that the properties of the liquefied tailings significantly influence runout predictions. Notably, we achieve runout distances comparable to the case history only when incorporating additional strain-softening beyond the initial liquefaction-induced strength reduction. Our results demonstrate that the hybrid FEM-MPM method effectively models tailings dam failures associated with complex failure mechanisms and large runouts. This approach offers a promising tool for predicting the runout of seismic liquefaction-induced tailings dam failures, improving risk assessment and mitigation strategies in tailings dam management.