基于TBM掘进性能的岩体分级及可掘性等级感知识别方法

doi:10.13722/j.cnki.jrme.2021.0386

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Abstract The traditional surrounding rock classification method is mainly based on the drill and blast method，which is not suitable for evaluating and predicting the TBM tunneling performance. Therefore，the purpose of this paper is to establish a rock mass boreability classification method suitable for TBM construction and achieve accurate perception and recognition of the boreability grade. For this purpose，based on the rock mass and TBM machine data sourced from four tunnels，the correlations between the rock mass parameters and TBM boreability evaluation indices were analyzed，and the multi-criteria decision-making(MCDM) method TOPSIS was used to establish the rock boreability classification system for TBM tunnel. Since it was difficult to obtain the parameters of rock mass in actual TBM excavation directly，the proposed rock mass boreability grade was recognized by using TBM driving parameters，including the thrust force per cutter(Th)，penetration rate(PR)，cutterhead revolutions per minutes(RPM) and penetration rate per revolution(PRev). In the perception and recognition，Bayesian optimization was used to determine the optimal combination of input parameters of various machine learning classification algorithms to achieve the maximum accuracy of grade recognition. The applicability of different optimized algorithms for the boreability grade recognition was compared，and then the optimal perception and recognition method was determined. Finally，based on the real-time TBM data of Jilin Yinsong tunnel project，the accuracy and effectiveness of the proposed classification，as well as the perception and recognition method of rock mass boreability were verified. The research results can be used to predict the boreability of rock mass in the stable operation sections of TBM projects，and provide reference for the optimization of TBM driving parameters and the establishment of intelligent control system.

Key words： tunnelling engineering TBM classification of rock mass Bayesian optimization perception and recognition

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	Articles by authors
	WU Zhijun1
	2
	3
	FANG Liqun1
	2
	WENG Lei1
	2
	LIU Quansheng1
	2
	3

Cite this article:

WU Zhijun1,2,3, et al. A classification and boreability perception and recognition method for rock mass based on TBM tunneling performance[J]. , 2022, 41(S1): 2684-2699.

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https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0386 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/IS1/2684

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