基于变分模态分解和极端梯度提升的公路边坡位移预测

doi:10.13229/j.cnki.jdxbgxb.20231197

Abstract

Abstract:

Facing the nonlinear， high noisy， and unstable characteristics of highway slope displacement monitoring data lead to insufficient accuracy of slope displacement prediction， this paper proposes a regression prediction algorithm that optimizes the variational mode decomposition（VMD） and extreme gradient boosting（XGBoost） for the processing and prediction of highway slope displacement data. Firstly， the particle swarm optimization is used to find the optimal number of decomposition layers and the penalty factor of VMD， and then the slope displacement data are subjected to VMD to obtain the trend displacement， periodic displacement and random fluctuation displacement features. Secondly， other monitoring data are added as regression prediction feature variables， and the Shapley additive explanation（SHAP） is used to interpret the importance of the input feature variables， and then the important features are screened and inputted into XGBoost model for prediction， and the grid search is used to determine the optimal parameters of the XGBoost model. Finally， the applicability and robustness of the slope displacement decomposition method and regression prediction model proposed in this paper are verified based on actual case analysis.The results show that in the real cases of this paper， for slope displacement data decomposition， VMD has stronger applicability than empirical modal decomposition（EMD） and ensemble empirical modal decomposition（EEMD）； for slope displacement prediction， the prediction accuracy of XGBoost is improved by 4.37% and 0.41% compared with the extreme learning machine（ELM） and support vector machine（SVM）， respectively. The regression model proposed in this paper has high prediction accuracy and strong robustness. Meanwhile， it is shown that the slope displacement characteristic variables （periodici displacement， random fluctuation displacement and trend displacement） extracted by VMD have a greater degree of SHAP importance for slope displacement prediction. The method proposed in this paper can provide some ideas for highway slope displacement prediction and safety warning research.

Key words: road engineering, highway slope displacement prediction, variational mode decomposition, particle swarm optimization, extreme gradient boosting, robustness test

CLC Number:

Zhi-you LONG,Zhao-long WAN,Shi DONG,Chao YANG,Xiao-yang LIU. Displacement prediction of highway slope based on variational mode decomposition and extreme gradient boosting[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(7): 2320-2332.

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监测项目	仪器名称	仪器型号	记录频率	单位
地表位移	GPS-RTK	GSTP-RTK11-1	30 min/次	m
深部位移	固定式测斜仪	GSTP-ME610-2	30 min/次	°
地下水压力	渗压计	GSTP-ZX450	30 min/次	kPa
雨量	一体化雨量站	-	1 d/次	mm

边坡类型	数据分解方式	预测模型	模型	R²	RMSE/mm	MAPE/%
A边坡	EMD	XGBoost	EMD-SHAP-XGBoost	0.969	0.148	4.93
		SVM	EMD-SHAP-SVM	0.968	0.149	12.03
		ELM	EMD-SHAP-ELM	0.935	0.212	6.57
	EEMD	XGBoost	EEMD-SHAP-XGBoost	0.979	0.123	4.00
		SVM	EEMD-SHAP-SVM	0.978	0.123	9.74
		ELM	EEMD-SHAP-ELM	0.967	0.152	4.77
	VMD	XGBoost	PSO-VMD-SHAP-XGBoost	0.980	0.119	3.74
		SVM	VMD-SHAP-SVM	0.958	0.166	5.17
		ELM	VMD-SHAP-ELM	0.959	0.170	5.46
	-	XGBoost	XGBoost^［5］	0.010	0.843	32.84
	-	SVM	SVM^［3］	0.944	0.197	6.27
	-	ELM	ELM^［2］	0.417	0.636	22.32
B边坡	EMD	XGBoost	EMD-SHAP-XGBoost	0.980	0.120	3.96
		SVM	EMD-SHAP-SVM	0.980	0.120	9.66
		ELM	EMD-SHAP-ELM	0.935	0.209	6.17
	EEMD	XGBoost	EEMD-SHAP-XGBoost	0.992	0.076	2.48
		SVM	EEMD-SHAP-SVM	0.992	0.077	6.12
		ELM	EEMD-SHAP-ELM	0.909	0.247	8.42
	VMD	XGBoost	PSO-VMD-SHAP-XGBoost	0.998	0.035	1.21
		SVM	VMD-SHAP-SVM	0.998	0.035	1.31
		ELM	VMD-SHAP-ELM	0.941	0.199	6.28
	-	XGBoost	XGBoost^［5］	0.010	0.819	30.98
	-	SVM	SVM^［3］	0.549	0.549	18.97
	-	ELM	ELM^［2］	0.557	0.545	18.93

Displacement prediction of highway slope based on variational mode decomposition and extreme gradient boosting

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