7 papers
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
Cheng-Hsi Hsiao, Krishna Kumar, Ellen M. Rathje
Explainable Boosting Machines (EBMs) provide transparent predictions through additive shape functions, enabling direct inspection of feature contributions. However, EBMs can learn…
Investigating the effect of CPT in lateral spreading prediction using Explainable AI
Cheng-Hsi Hsiao, Ellen Rathje, Krishna Kumar
This study proposes an autoencoder approach to extract latent features from cone penetration test profiles to evaluate the potential of incorporating CPT data in an AI model. We em…
Runout of liquefaction-induced tailings dam failure: Influence of earthquake motions and residual strength
Brent Sordo, Ellen Rathje, Krishna Kumar
This study utilizes a hybrid Finite Element Method (FEM) and Material Point Method (MPM) to investigate the runout of liquefaction-induced flow slide failures. The key inputs to th…
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…
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…
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…