基于变分模态分解和灰狼优化极限学习机的隧道口边坡位移预测

doi:10.13229/j.cnki.jdxbgxb.20230074

Abstract

Abstract:

In view of the non-stationary and nonlinear characteristics of the slope displacement monitoring data of high-speed railway tunnel entrance and the poor prediction performance caused by random generation of initial parameters of extreme learning machine （ELM） model， an ELM displacement prediction model based on variational mode decomposition （VMD） and grey wolf optimizer （GWO） was established. The optimal decomposition number of VMD was determined by the adaptive decomposition layers of Empirical Mode Decomposition， and the displacement of periodic term， trend term and wave term were obtained by VMD. The GWO was used to search for the optimal weight matrix connecting the input and hidden layers and the threshold of the hidden layer neurons of ELM. Each subsequence was predicted and the cumulative displacement was obtained by combining the results. Example verification shows that the root-mean-square error， mean absolute percentage error and goodness of fit of VMD-GWO-ELM model are 0.3822 mm， 1.0047% and 0.9837， respectively. The VMD-GWO-ELM model has higher prediction accuracy and applicability.

Key words: road engineering, tunnel entrance slope, displacement prediction, variational mode decomposition, grey wolf optimized extreme learning machine

CLC Number:

X928.03

Bo LI,Xin LI,Hong RUI,Yuan LIANG. Displacement prediction of tunnel entrance slope based on variational modal decomposition and grey wolf optimized extreme learning machine[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1853-1860.

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预测模型	RMSE/mm	MAPE/%	R²
ELM	1.5883	3.6655	0.8767
GWO-ELM	0.9435	2.5179	0.9520
VMD-GWO-ELM	0.3822	1.0047	0.9837

Displacement prediction of tunnel entrance slope based on variational modal decomposition and grey wolf optimized extreme learning machine

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