TBM岩-机参数动态交互机制及不良地质实时超前判识方法研究

TBM tunneling efficiency is closely related to tunnel geological conditions and reasonable matching of rock-machine parameters. How to obtain the geological parameters in real time and effectively match the rock-machine parameters is a key problem to be solved in TBM tunneling process. In this project, the dynamic interaction mechanism of TBM rock –machine parameters and the method of real-time advance identification of bad geology are studied by means of theoretical analysis, field test and depth data mining. Based on the TBM tunneling test and numerical simulation, the main control mechanical parameters and surrounding rock parameters affecting the TBM tunneling efficiency are obtained, and the correlation between the rock-machine parameters and the tunneling efficiency are studied. The nonlinear feedback model of TBM rock-machine parameters is established, with the real-time acquisition of surrounding rock parameters. The feedback model of rock-machine parameters is established, the dynamic correlation model of surrounding rock parameters - mechanical parameters - tunneling efficiency is studied, the dynamic interaction mechanism of TBM rock - machine parameters is revealed. The mechanical parameters are collected when TBM tunneling through typical bad geological sections, the characterizing method of the TBM mechanical parameters through bad geological tunnel sections with the optimal tunneling efficiency as the criterion is established, the TBM advance identification criteria of bad geology is established, the real-time advance identification and early warning of bad geology are realized. The results of this research are of great significance on the efficiency of TBM tunneling.

TBM掘进效率与隧道地质条件以及岩机参数合理匹配有着密切联系，如何实时获取地质参数，进行岩机参数有效匹配是TBM掘进过程中一个亟待解决的关键问题。本项目以理论分析，现场试验、深度数据挖掘为手段，开展TBM岩-机参数动态交互机制及不良地质实时超前判识方法研究。通过现场TBM掘进试验和数值模拟，获取影响TBM掘进效率的主控机器参数和围岩参数，研究围岩参数、机器参数和掘进效率的相关关系，建立TBM岩-机参数约束非线性预测模型，实时获取围岩参数。建立围岩参数-机器参数反馈模型，研究TBM围岩参数-机器参数-掘进效率三者动态关联模式，揭示TBM岩-机参数动态交互机制。收集TBM穿越典型不良地质段的机器参数，建立以掘进效率最优为评判标准的隧道不良地质TBM机器参数表征方法，建立TBM临近不良地质超前判识准则，实现对不良地质实时超前判识及预警。课题研究成果对于提高TBM掘进效率，具有重要意义。

TBM在掘进过程中对地质条件非常敏感，并且在不同的地质条件下，如何对TBM掘进参数进行合理匹配，也直接影响到TBM的掘进效率和施工安全。本项目针对如何高效判定掌子面近前方地质参数，并对TBM掘进参数进行科学决策这一关键科学问题，开展TBM岩-机参数动态交互机制及不良地质超前判识方法的研究。采用现场TBM掘进试验和岩石力学试验，获取了影响TBM掘进效率的主控机器参数和隧道围岩指标参数，采用模型试验和数值模拟手段模拟了不同地层、不同掘进参数的TBM掘进过程，得到了TBM掘进过程中围岩参数、机器参数和掘进效率的相关关系。采用离散元软件对滚刀侵入破岩的整个过程进行受力情况模拟，结合现场掘进试验数据和模型试验数据，构建了TBM岩-机参数掘进加载速率约束非线性预测模型，通过工程现场实测数据对模型进行了适用性验证。设计研发了TBM模拟掘进系统，进行了模型试验相似材料配比研究，开展了不同工况条件下TBM掘进参数动态交互机制模拟掘进试验，探索了TBM破岩掘进中围岩参数-机器参数-掘进效率三者动态关联模式，揭示了TBM岩-机参数动态交互机制。利用TBM现场掘进数据和TBM模拟掘进试验数据，获取了TBM穿越典型地质段的机器参数，构建了TBM隧道围岩可掘性分级方法，获得了不同可掘性等级下的控制参数取值范围。提出了修正比能理论公式，建立了TBM不良地质超前判识准则，研发了RUSBoost-ANN不良地质界面识别系统并在工程实践进行了验证，实现对不良地质的判识及预警。研究成果对于提高TBM地层适应性、充分发挥TBM掘进效率具有重要的科学理论意义与实用价值，成果成功应用于多项TBM施工的国家重大工程。