基于集成CART算法的TBM掘进参数与围岩等级预测

doi:10.13722/j.cnki.jrme.2019.0924

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Abstract There are numerous data available to reflect the geological conditions during the operation period of full-section tunnel boring machine(TBM). The aims of this paper are to measure information of rockmasses in time and to intelligently optimize the tunneling parameters based on TBM operating data and CART-based ensemble learning algorithms，i.e. random forest and AdaCost. In order to establish models for rapidly and accurately predicting TBM tunneling parameters and surrounding rockmass conditions，a new pattern recognition method is proposed to divide a TBM tunneling cycle into empty pushing section，ascending section and stable section. Random forest model and the first 30 seconds data of ascending section are used to predict the values of the thrust and cutterhead torque during stable section，with the accuracy of 0.90 and 0.87 respectively. Moreover，a cost-sensitive AdaCost algorithm is used to predict the surrounding rockmass conditions with 16% and 50% precision improvements of grades IV and V compared to the random forest model，which solves the problem that the traditional machine learning algorithms are not applicable to imbalanced database. TBM operating parameters such as thrust，cutterhead power，cutterhead torque and advance rate are proved to be closely related to TBM tunneling，while cutterhead rotational speed，boot pressure，boot angle of pitch，advance rate and boot roll position and so on are shown to better reflect the surrounding rockmass conditions. These results are of great significance to TBM tunneling parameters optimizing and risk warning and will provide a reference to establish a data-based TBM intelligent decision and control platform in the future.

Key words： rock mechanics TBM tunnelling parameters surrounding rockmass conditions imbalanced database random forest AdaCost algorithm

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	ZHU Mengqi1
	ZHU Hehua1
	2
	WANG Xin1
	CHENG Panpan1

Cite this article:

ZHU Mengqi1,ZHU Hehua1,2, et al. Study on CART-based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rockmasses#br#[J]. , 2020, 39(9): 1860-1871.

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http://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.0924 OR http://rockmech.whrsm.ac.cn/EN/Y2020/V39/I9/1860

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