硬岩隧道掘进机性能预测模型研究进展

doi:10.13722/j.cnki.jrme.2014.1601

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摘要由于硬岩隧道掘进机(TBM)对地质条件极其敏感，且前期投资较大，准确预测特定地质条件下的TBM性能对于隧道施工方法选择、施工进度安排和成本估计至关重要，同时也是隧道行业内的热门话题。首先回顾国内外TBM性能预测模型的研究进展，对目前常用的TBM性能预测模型进行较为详细地介绍。基于对理论模型和复杂经验模型(总计17个)中使用的岩体参数和机器参数的频率统计，发现影响TBM性能的岩体参数主要包括不连续面间距、岩石单轴抗压强度、不连续面和隧道轴向之间的夹角、隧道直径和岩石脆性等；影响TBM性能的机器参数主要包括单刀推力、刀盘转速、刀具直径、刀间距、刀尖宽度和滚刀岩石接触角等。基于统计结果论述各参数对TBM性能的影响，最后根据国外TBM性能预测模型的发展历程对国内开发TBM性能预测模型提出一些建议。

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关键词 ：隧道工程, 隧道掘进机(TBM), 性能预测模型, 岩体参数, 机器参数

Abstract：With significant sensibility to the varying geological conditions and enormous financial investment in the earlier phase of tunneling project，predicting of TBM performance accurately with special geological conditions has a crucial importance for selecting tunnel construction method，arranging construction schedule and assessing excavation cost. In this study，the hot topic on research advances of TBM performance prediction models both at home and abroad are systematically reviewed firstly，and the widely adopted ones are introduced detailedly. The results based on the statistical counting of the occurrence frequency of rock mass properties and machine parameters used in 17 models including theoretical models and complex empirical models show that the dominating rock mass properties affecting TBM performance include discontinuity spacing，intact rock uniaxial compressive strength， angle between the discontinuity and tunnel axial，tunnel diameter and rock brittleness，etc. And the main machine parameters contributed in the performance predictions are mostly thrust per cutter，rotating speed of cutterhead，cutter diameter，cutter spacing，cutter tip width and angle of contact area between rock and disc cutter，etc. The influence of each controlling parameters on TBM performance prediction is discussed based on the statistical results. Finally，according to the developing history of the foreign prediction models，some useful suggestions are put forward for the development of TBM performance prediction models in China.

Key words： tunnel engineering tunnel boring machine(TBM) performance prediction model rock mass parameters machine parameters

引用本文:

刘泉声1，2，刘建平1，潘玉丛1，孔晓璇2，崔先泽1，黄诗冰1，魏莱1. 硬岩隧道掘进机性能预测模型研究进展[J]. 岩石力学与工程学报, 2016, 35(S1): 2766-2786.
LIU Quansheng1，2，LIU Jianping1，PAN Yucong1，KONG Xiaoxuan2，. Research advances of tunnel boring machine performance prediction models for hard rock. , 2016, 35(S1): 2766-2786.

链接本文:

https://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2014.1601 或 https://rockmech.whrsm.ac.cn/CN/Y2016/V35/IS1/2766

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