Optimization of anchorage support parameters for soft rock tunnel based on displacement control theory

被引:5
作者
Li, Gan [1 ]
Ma, Wei-bin [2 ]
Yu, Chang-yi [3 ,4 ]
Tao, Zhi-gang [5 ]
Wang, Feng-nian [6 ]
机构
[1] Ningbo Univ, Rock Mech Res Inst, Sch Civil & Environm Engn & Geog Sci, Ningbo 315000, Peoples R China
[2] China Acad Railway Sci Corp Ltd, Railway Engn Res Inst, Beijing 100081, Peoples R China
[3] CCCC First Harbor Engn Co Co Ltd, Tianjin 300461, Peoples R China
[4] CCCC Tianjin Port Engn Inst Co Ltd, Tianjin 300222, Peoples R China
[5] State Key Lab Geomech & Deep Underground Engn, Beijing 100083, Peoples R China
[6] Shanxi Transportat Technol Res & Dev Co LTD, Taiyuan 030032, Peoples R China
基金
中国国家自然科学基金;
关键词
Displacement control theory; Anchorage support parameters; Numerical simulation; PSO-LSSVM; Tunnel construction; SURROUNDING ROCK; DEFORMATION; SIMULATION; BOLT; TECHNOLOGY; MODULUS; ROADWAY; DESIGN; MASSES; SET;
D O I
10.1007/s11629-023-7999-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In the construction of a soft rock tunnel, it is critical to accurately estimate the pre-stressed anchor support parameters for surrounding rock reinforcement; otherwise, engineering disasters may occur. This paper presents a support parameter selection method that aims to allow deformation as a control objective, which was applied to the tunnel located in Muzailing Highway, Min County, Dingxi City, Gansu Province, China. Through theoretical analysis, we have identified five factors that influence pre-stressing anchorages. The selection of mechanical parameters for the rock mass was carried out using an inverse analysis method. Compared with the measured data, the maximum displacement error of the numerical simulation results was only 0.07 m. The length of anchor cable, circumferential spacing of anchor cable, longitudinal spacing, and pre-stress index are adopted as the input parameters for the support vector machine neural network model based on particle swarm optimization (PSO-LSSVM). Besides, the vault subsidence and the maximum deformation of surrounding rock are considered as output values (performance indices). The goodness of fit between the predicted values and the simulated values exceeds 0.9. Finally, all support parameters within the acceptable deformation range are calculated. The optimal support variables are derived by considering the construction cost and duration. The field application results show that it is feasible to construct the sample database utilizing the numerical simulation approach by taking the displacement as the control target and using the neural network to specify the appropriate support parameters.
引用
收藏
页码:2076 / 2092
页数:17
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