Explainable AI models for predicting liquefaction-induced lateral spreading

TL;DR

This paper employs explainable machine learning, specifically SHAP with XGB, to predict earthquake-induced lateral spreading, improving transparency and aligning model insights with geotechnical engineering knowledge.

Contribution

It introduces an explainable ML approach for lateral spreading prediction, enhancing interpretability and validation against domain expertise.

Findings

XGB model accurately predicts lateral spreading using CPT data.

SHAP analysis identifies key soil factors influencing predictions.

Model results align with established geotechnical knowledge.

Abstract

Earthquake-induced liquefaction can cause substantial lateral spreading, posing threats to infrastructure. Machine learning (ML) can improve lateral spreading prediction models by capturing complex soil characteristics and site conditions. However, the "black box" nature of ML models can hinder their adoption in critical decision-making. This study addresses this limitation by using SHapley Additive exPlanations (SHAP) to interpret an eXtreme Gradient Boosting (XGB) model for lateral spreading prediction, trained on data from the 2011 Christchurch Earthquake. SHAP analysis reveals the factors driving the model's predictions, enhancing transparency and allowing for comparison with established engineering knowledge. The results demonstrate that the XGB model successfully identifies the importance of soil characteristics derived from Cone Penetration Test (CPT) data in predicting lateral spreading, validating its alignment with domain understanding. This work highlights the value of explainable machine learning for reliable and informed decision-making in geotechnical engineering and hazard assessment.