Abstract: In order to improve the reliability of prediction of large deformation level of layered soft rock tunnels under complex geological conditions, a particle swarm optimization(PSO)based support vector machine(SVM)was proposed. The large deformation prediction method of tunnel solved the problems of complex calculation of multiple evaluation index weights and diverse boundary values in large deformation prediction. In order to fully consider the influence of layered soft rock surrounding rock strength, surrounding rock structure type, in-situ stress and groundwater on the large deformation of the tunnel, the 6 sub-indices of value: uniaxial compressive strength, rock inclination angle, initial in-situ stress state, burial depth, rock mass correction quality correction index ［BQ］ and groundwater development, were used to predict the large deformation level. This method constructed a large deformation prediction model based on in-situ stress inversion and on-site large deformation monitoring information according to the classification standard of large deformation grades, and used particle swarm optimization algorithm to adjust the penalty parameter C and the kernel function parameter Gamma to improve the model's performance accuracy. The research results showed that the use of particle swarm optimization-support vector machine(PSO-SVM)algorithm could avoid errors caused by traditional prediction methods such as geological comprehensive judgment method and strength-stress ratio method due to a single index and subjective reasons, and the prediction accuracy was high; this method used the large deformation information of tunnels that had occurred, a PSO-SVM large deformation prediction model conforming to the actual law of the target tunnel site was constructed; the PSO-SVM model had an accuracy of 86.36% in the prediction of the sample test set, which was better than the SVM and GS-SVM models. Taking the Baima Tunnel, a typical layered soft rock tunnel of the Jiu-mian Expressway as the research object, the proposed PSO-SVM model was used to conduct large-scale research. The deformation prediction was compared with the field measurement, and it was found that the prediction accuracy reached 80%, which verified the feasibility of the method.
杨文波, 王宗学, 田浩晟, 吴枋胤, 杨自成. 基于PSO-SVM算法的层状软岩隧道大变形预测方法[J]. 隧道与地下工程灾害防治, 2022, 4(1): 29-37.
YANG Wenbo, WANG Zongxue, TIAN Haosheng, WU Fangyin, YANG Zicheng. Large deformation prediction method of layered soft rock tunnel based on PSO-SVM algorithm. Hazard Control in Tunnelling and Underground Engineering, 2022, 4(1): 29-37.
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2022, Vol. 4 
