The Application of Interpretable Collapse Rockfall Risk Prediction Based on"XGBoost—SHAP"in Highway Engineering
Based on XGBoost and SHAP algorithm,this paper constructs a collapse rockfall risk prediction model,and applies it in the investigation and design of reconstruction and expansion of provincial highway S463 into G664 project.The combination of Artificial Intelligence technology and engineering practice has expanded the idea for the risk prediction of collapse rockfall.The prediction accuracy of the XGBoost model reached 91.04%-94.12%at this time,which basically met the needs of auxiliary engineering survey and design.At the same time,the SHAP algorithm was used to explain the black box of the prediction model.The main control factors of the model were found to be slope width,apparent dip,lithologic strength,slope area and slope angle of rock dip.At the same time,the quantitative analysis was given to the influence value of the prediction results,and the actual work direction was clarified.The model has high accuracy,strong operability and clear quantitative indicators,which can be used as a reference for similar engineering practice.
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