Rock and Soil Mechanics ›› 2020, Vol. 41 ›› Issue (7): 2494-2503.doi: 10.16285/j.rsm.2019.6632

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Intelligent recognition of tunnel stratum based on advanced drilling tests

FANG Yu-wei1, 2, WU Zhen-jun1, 2, SHENG Qian1,2, TANG Hua1, 2, LIANG Dong-cai1, 2   

  1. 1. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan, Hubei 430071, China 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Online:2020-07-10 Published:2020-12-22
  • Contact: WU Zhen-jun, male, born in 1977, PhD, Associate professor, focusing on research and development of geotechnical engineering test equipment. E-mail:
  • About author:FANG Yu-wei, male, born in 1992, PhD, mainly engaged in geotechnical mechanics and engineering research.
  • Supported by:
    the Science and Technology Program of Yunnan Transportation Department (Yunjiao Science and Education(2018)No.18).

Abstract: The reliable recognition of strata in front of tunnel face is significant for the stability and safety of the tunnel engineering project. Traditional advanced geological forecasting methods could not ensure high identification accuracy, low cost and short construction time simultaneously, and they can’t satisfy the universality of stratum identification under different geological conditions. The advanced forecasting efficiency could be significantly enhanced if the drilling data of surrounding rocks in front of the tunnel face can be obtained while performing the conventional advanced borehole to attain the rock conditions at different drilling depths in real time, which would be convenient and efficient by not affecting the construction period. However, no objective and accurate stratum identification methods are found. In this paper, we proposed an intelligence analysis of drilling data and stratum recognition method based on neural network. It is used to analyze the advanced drilling test data of Jiudingshan Tunnel of Chuxiong?Dali highway and the analysis method was verified by the strata exposed after tunnel excavation. The results show that the error rate of stratum recognition using the single drilling parameter is about 35%. The combination of blow energy and blow number, water supply pressure and water supply rate cannot significantly improve the accuracy of stratum recognition. The combination of drilling speed, torque, rotation speed and propulsion can reduce the error rate to 22% for stratum recognition. The error rate can be sharply decreased by 9%?12% when the standard deviation of drilling parameters is introduced into the neural network model. The error rate of stratum recognition is less than 10% for random sampled data and it is less than 14% for a single borehole using the neural network model with the combination of multiple drilling test parameters.

Key words: drilling test, neural networks, tunnel, stratum, intelligent recognition