A novel prediction model construction and result interpretation method for slope deformation of deep excavated expansive soil canals

Abstract

Several giant water diversion projects go through large expansive soil areas in China. It is challenging to ensure the slope stability of the deep excavated expansive soil canal segments due to its undesirable geologic, and physical and mechanical properties. This study develops a novel deformation prediction method for deep excavated expansive soil canal slopes based on the eXtreme Gradient Boosting (XGBoost) algorithm, and provides a result interpretation using SHAP (SHapley Additive exPlanations). Firstly, the main factors influencing the slope deformation of deep excavated expansive soil canal segments are discussed. Subsequently, the integrated XGBoost-SHAP method is proposed for the prediction model construction of slope deformation and its result interpretation, the related the methodologies including the variational mode decomposition (VMD), XGBoost, and SHAP are also presented. The ten-fold cross-validation is employed to find the hyperparameter combination for the deformation prediction models, and the model performances are compared with common validation measurements. The effectiveness of the explanatory deformation prediction method is verified through one typical deep excavated expansive canal segment of the mid-route of the South-to-North Water Diversion Project in China. The case study shows that the VMD algorithm can well decompose the trend, periodic and fluctuating displacements. Compared with random forest and least squares-support vector machines, the XGBoost-algorithm based model achieves the better prediction performance. SHAP provides prediction result interpretations at both global and local levels. Time dependent effect and groundwater have a significant impact on the slope deformation of deep excavated expansive soil canal segments. The integrated SHAP-XGBoost method can provide a reference for predicting slope deformation of the similar projects. It can also quantify the contribution and mechanism of influencing factors on slope deformation at both global and local levels, which is conducive to implement reinforcement measures.