ISSN 1000-3665 CN 11-2202/P

    基于BP神经网络反演分析的隧道塌方机理研究

    A study of the tunnel collapse mechanism based on the BP neural network inversion analysis

    • 摘要: 岩溶区隧道在施工过程中易发生崩塌,针对力学方面的隧道塌方机理分析较多,而针对岩溶软弱破碎带等地层方面的隧道塌方机理研究分析较少。为保证隧道施工的安全性、经济性和可行性,掌握隧道施工中的塌方机理非常有必要。依托贵州某岩溶破碎地层隧道在开挖过程中发生的坍塌现象,结合隧道的监测数据,运用BP神经网络的构建原理,对隧道的地层参数进行反演。将反演土体力学参数输入到FLAC3D有限元软件构建的不同施工方法模型中,对典型断面的崩塌破坏机制和风险进行判断和分析。结果表明:施工方法对隧道开挖的稳定性影响较大,针对围岩等级为Ⅴ级的隧道,采用三台阶七步法和单侧壁导坑法施工较安全,隧道塌方与隧道双向同时开挖没有关系;反演所得的隧道拱顶位移预测值为2.3 cm,地表位移预测值为1.2 cm,与监测数据偏差13%左右,反演结果具有一定的可信度。研究结果对岩溶区软弱破碎地层断面隧道公路建设具有重要指导意义。

       

      Abstract: Tunnels in karst areas are prone to collapse during construction. There are many analyses on the mechanism of tunnel collapse in mechanical aspects, but the mechanism of tunnel collapse in karst weak fracture zones and other strata were seldom examined. In order to ensure the safety, economy and feasibility of tunnel construction, it is necessary to master the mechanism of collapse in the tunnel construction. Relying on a tunnel project in a karst broken stratum in Guizhou, where the collapse phenomenon occurred during the excavation process, the monitoring data of the tunnel are examined, and the construction principle of the BP neural network is used to invert the stratum parameters of the tunnel. The inversion soil mechanical parameters are input into different construction models constructed by using the FLAC3D finite element software, and the collapse failure mechanism and risk of typical sections are judged and analyzed. The results show that the construction method has a great influence on the stability of the tunnel excavation, and for the tunnel with the surrounding rock grade V , the three-step seven-step method and the single-side wall pilot pit method are safer for construction, and the tunnel collapse has no relationship with the simultaneous excavation of the tunnel in both directions. The predicted value of the tunnel vault displacement obtained by the inversion is 2.3 cm, and the predicted value of the surface displacement is 1.2 cm. The deviation from the monitoring data is about 13%, and the inversion result has certain reliability. The research results are of important guiding significance for the construction of tunnels and highways in weak and broken strata in karst areas.

       

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