Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracki...Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracking behavior through a numerous small-sample experiments.However,experimental studies alone cannot accurately describe soil cracking behavior.In this study,we firstly propose a modeling framework for predicting the surface crack ratio of soil desiccation cracking based on machine learning and interpretable analysis.The framework utilizes 1040 sets of soil cracking experimental data and employs random forest(RF),extreme gradient boosting(XGBoost),and artificialneural network(ANN)models to predict the surface crack ratio of soil desiccation cracking.To clarify the influenceof input features on soil cracking behavior,feature importance and Shapley additive explanations(SHAP)are applied for interpretability analysis.The results reveal that ensemble methods(RF and XGBoost)provide better predictive performance than the deep learning model(ANN).The feature importance analysis shows that soil desiccation cracking is primarily influencedby initial water content,plasticity index,finalwater content,liquid limit,sand content,clay content and thickness.Moreover,SHAP-based interpretability analysis further explores how soil cracking responds to various input variables.This study provides new insight into the evolution of soil cracking behavior,enhancing the understanding of its physical mechanisms and facilitating the assessment of potential regional development of soil desiccation cracking.展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos.2023YFC3707900 and 2024YFC3012700)the National Natural Science Foundation of China(Grant No.42230710).
文摘Soil desiccation cracking is ubiquitous in nature and has significantpotential impacts on the engineering geological properties of soils.Previous studies have extensively examined various factors affecting soil cracking behavior through a numerous small-sample experiments.However,experimental studies alone cannot accurately describe soil cracking behavior.In this study,we firstly propose a modeling framework for predicting the surface crack ratio of soil desiccation cracking based on machine learning and interpretable analysis.The framework utilizes 1040 sets of soil cracking experimental data and employs random forest(RF),extreme gradient boosting(XGBoost),and artificialneural network(ANN)models to predict the surface crack ratio of soil desiccation cracking.To clarify the influenceof input features on soil cracking behavior,feature importance and Shapley additive explanations(SHAP)are applied for interpretability analysis.The results reveal that ensemble methods(RF and XGBoost)provide better predictive performance than the deep learning model(ANN).The feature importance analysis shows that soil desiccation cracking is primarily influencedby initial water content,plasticity index,finalwater content,liquid limit,sand content,clay content and thickness.Moreover,SHAP-based interpretability analysis further explores how soil cracking responds to various input variables.This study provides new insight into the evolution of soil cracking behavior,enhancing the understanding of its physical mechanisms and facilitating the assessment of potential regional development of soil desiccation cracking.
