李华舟, 胡斌, 姚文敏, 和大钊, 毛元静, 冉秀峰. 2016: 八里扁隧道围岩变形监测与位移反分析. 工程地质学报, 24(s1): 61-66. DOI: 10.13544/j.cnki.jeg.2016.s1.009
    引用本文: 李华舟, 胡斌, 姚文敏, 和大钊, 毛元静, 冉秀峰. 2016: 八里扁隧道围岩变形监测与位移反分析. 工程地质学报, 24(s1): 61-66. DOI: 10.13544/j.cnki.jeg.2016.s1.009
    LI Huazhou, HU Bin, YAO Wenmin, HE Dazhao, MAO Yuanjing, RAN Xiufeng. 2016: SURROUNDING ROCK DEFORMATION MONITOR AND DISPLACEMENT BACK ANALYSIS FOR BALIBIAN TUNNEL. JOURNAL OF ENGINEERING GEOLOGY, 24(s1): 61-66. DOI: 10.13544/j.cnki.jeg.2016.s1.009
    Citation: LI Huazhou, HU Bin, YAO Wenmin, HE Dazhao, MAO Yuanjing, RAN Xiufeng. 2016: SURROUNDING ROCK DEFORMATION MONITOR AND DISPLACEMENT BACK ANALYSIS FOR BALIBIAN TUNNEL. JOURNAL OF ENGINEERING GEOLOGY, 24(s1): 61-66. DOI: 10.13544/j.cnki.jeg.2016.s1.009

    八里扁隧道围岩变形监测与位移反分析

    SURROUNDING ROCK DEFORMATION MONITOR AND DISPLACEMENT BACK ANALYSIS FOR BALIBIAN TUNNEL

    • 摘要: 为了判定八里扁隧道在掘进过程中断面处围岩及其支护体系的稳定状态,揭示隧道围岩变形、破坏及稳定的机理,根据隧道断面ZK77+835处围岩变形的监测数据,建立指数函数回归模型,进行精度判定和监测数据分析,同时进行BP神经网络位移反分析与数值模拟,结果表明:采用指数函数模型对断面ZK77+835处围岩变形值进行回归分析的精确度较高;围岩变形存在3个发展时期,从位移值和位移速率两方面可以判定,在监测15d后,该断面处围岩基本处于稳定状态,可以进行二次衬砌施工;利用BP神经网络位移反分析法反演的参数比较准确,将其带入FLAC3D数值模型中,模拟结果与现场监测数据相差不大。

       

      Abstract: To predicate the steady state of fracture surface of surrounding rock and support system in terms of Bali flat tunnel in the course of excavation and,uncover the mechanism of the tunnel surrounding rock in the aspect of deformation, destruction and stability. Exponential function model is established by the use of the data which is gained on spot in the way of testing the section of surrounding rock ZK77+835according to the horizontal convergence displacement and vault crown settlement displacement. The result, after the regression analysis, precise judgment and monitoring data analysis, Meanwhile conducting BP Neural Network displacement back analysis and numerical simulation, shows that by adopting the exponential function model, it is relatively precise to analysis of regression of the data acquired aiming at surrounding rockZK77+835 displacement; there are three periods of the deformation of surrounding rock, in the light of displacement and the dislocation rate, there will provide a basis for judging the stability of the fracture surface of surrounding rock and can start the secondary lining conduction after monitoring the 15 day; it is accurate to get the parameter of inversion by BP Neural Network displacement back analysis, which is taken to FLAC3D numerical model, there will be no different between the simulation results and the monitoring data in field.

       

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