2 citations · 2 across the 4 of their papers we have counts for
7 papers · 1 filter
Measuring the effective stress parameter using the multiphase lattice Boltzmann method and investigating the source of its hysteresis
Reihaneh Hosseini, Krishna Kumar
The effective stress parameter, , is essential for calculating the effective stress in unsaturated soils. Experimental measurements have captured different relationships betwee…
A hybrid Finite Element and Material Point Method for modeling liquefaction-induced tailings dam failures
Brent Sordo, Ellen Rathje, Krishna Kumar
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 ru…
Machine Learning Aided Modeling of Granular Materials: A Review
Mengqi Wang, Krishna Kumar, Y. T. Feng +2
Artificial intelligence (AI) has become a buzz word since Google's AlphaGo beat a world champion in 2017. In the past five years, machine learning as a subset of the broader catego…
Sequential hybrid finite element and material point method to simulate slope failures
Brent Sordo, Ellen Rathje, Krishna Kumar
Numerical modeling of slope failures seeks to predict two key phenomena: the initiation of failure and the post-failure runout. Currently, most modeling methods for slope failure a…
Inverse analysis of granular flows using differentiable graph neural network simulator
Yongjin Choi, Krishna Kumar
Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional h…
Explainable AI models for predicting liquefaction-induced lateral spreading
Cheng-Hsi Hsiao, Krishna Kumar, Ellen Rathje
Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by…