Core Concepts
Explainable machine learning models, specifically XGBoost with SHAP analysis, can effectively predict the occurrence of liquefaction-induced lateral spreading by capturing complex soil characteristics and site conditions, while also providing insights into the key factors driving the model's predictions.
Abstract
The study develops an XGBoost (XGB) classifier model to predict the occurrence of liquefaction-induced lateral spreading based on data from the 2011 Christchurch earthquake. To enhance the interpretability of the XGB model, the authors employ SHapley Additive exPlanations (SHAP), a model-agnostic explainable AI technique.
The key highlights and insights from the study are:
The XGB model achieves high predictive accuracy on the testing dataset, correctly classifying 50.1% of no-lateral-spreading cases and 34.2% of lateral-spreading cases.
SHAP analysis reveals that the proximity to the river (L) and groundwater depth (GWD) are the most influential factors in the model's predictions, aligning with established domain knowledge.
However, the model exhibits counterintuitive behavior regarding the impact of peak ground acceleration (PGA), where high PGAs are associated with a lower likelihood of lateral spreading, contrary to expectations.
Incorporating additional soil characteristics from Cone Penetration Test (CPT) data, such as the median and standard deviation of soil type index (Ic) and normalized cone resistance (qc1Ncs), does not significantly improve the model's performance. The SHAP analysis shows that the CPT features are the least important among the input variables.
By excluding the least important features (median and standard deviation of qc1Ncs, and slope) and retaining only the median and standard deviation of Ic, the authors develop an improved model (Model C) with higher predictive accuracy on the validation and testing datasets.
The SHAP analysis of Model C demonstrates that it has effectively learned the underlying physics, where low Ic values (indicating coarse-grained soil) are associated with a higher likelihood of lateral spreading, while high Ic values (fine-grained soil) are linked to a lower likelihood.
The study highlights the value of explainable machine learning for reliable and informed decision-making in geotechnical engineering and hazard assessment, as the SHAP analysis provides transparency into the model's decision-making process and identifies areas for further improvement.
Stats
The site is located 6 meters from the river.
The peak ground acceleration (PGA) at the site is 0.444 g.
The groundwater depth (GWD) at the site is 1.055 meters.
The median soil type index (Ic) at the site is 2.546.
The median normalized cone resistance (qc1Ncs) at the site is 69.031.
Quotes
"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."