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Prediction of Tunnel Face Stability Based on Support Vector Machine and Ensemble Learning
(1. School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063; 2. Road & Bridge International Co., Ltd., Beijing 100027)
Abstract The stability of a tunnel face is influenced by a variety of factors, and it is necessary to assess the stability of the tunnel face before tunnel excavation, so as to develop appropriate measures and ensure the safety of the tunnelling process. To improve the efficiency of prediction, this paper proposes a method for the fast prediction of tunnel face stability based on ensemble learning and support vector machine (SVM). First, the most typical training samples are selected based on the principle of orthogonal experimental design, and then the samples are calibrated through three-dimensional numerical calculations. Second, based on the SVM algorithm, different kernel functions are used to fit the prediction models of tunnel face stability and verify their prediction accuracy through the leave-one-out method. Finally, according to the ensemble learning mechanism, the prediction models are synthesized by the voting method to realize the integrated prediction of tunnel face stability. The results show that the ensemble learning mechanism can minimize the generalization error of individual prediction models and improve the reliability of the
prediction results of tunnel face stability.
Abstract：
The stability of a tunnel face is influenced by a variety of factors, and it is necessary to assess the stability of the tunnel face before tunnel excavation, so as to develop appropriate measures and ensure the safety of the tunnelling process. To improve the efficiency of prediction, this paper proposes a method for the fast prediction of tunnel face stability based on ensemble learning and support vector machine (SVM). First, the most typical training samples are selected based on the principle of orthogonal experimental design, and then the samples are calibrated through three-dimensional numerical calculations. Second, based on the SVM algorithm, different kernel functions are used to fit the prediction models of tunnel face stability and verify their prediction accuracy through the leave-one-out method. Finally, according to the ensemble learning mechanism, the prediction models are synthesized by the voting method to realize the integrated prediction of tunnel face stability. The results show that the ensemble learning mechanism can minimize the generalization error of individual prediction models and improve the reliability of the
prediction results of tunnel face stability.
LI Bin1 LAN Yuansheng1 ZHANG Congxu2
.Prediction of Tunnel Face Stability Based on Support Vector Machine and Ensemble Learning[J] MODERN TUNNELLING TECHNOLOGY, 2022,V59(3): 63-71
2022, Vol. 59 