Rock Strength Prediction Model Based on Drilling&nbsp;Parameters
of Rock Drilling Jumbo

ZHANG Shucai; QIU Wenge; ZHANG Qifang; ZHANG Yijiang; JIANG Shuhua; LUO Jie

doi:10.3973/j.issn.2096-4498.2023.12.003

Tunnel Construction >

2023 , Vol. 43 >Issue 12: 2007 - 2017

DOI: https://doi.org/10.3973/j.issn.2096-4498.2023.12.003

Rock Strength Prediction Model Based on Drilling Parameters of Rock Drilling Jumbo

ZHANG Shucai ,
QIU Wenge ,
ZHANG Qifang ,
ZHANG Yijiang ,
JIANG Shuhua ,
LUO Jie

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(1.Anhui-Jiangxi Railway Anhui Co.,Ltd.,Hefei 230011,Anhui,China;2.Chengdu Tianyou Tunnel Key Company Ltd.,Chengdu 610031,Sichuan,China;3.Key Laboratory of Transportation Tunnel Engineering,the Ministry of Education,Southwest Jiaotong University, Chengdu 610031,Sichuan,China;4.China Railway Shanghai Design Institute Group Corporation Limited,Shanghai 200070,China;5.No.3 Construction Company of China Railway No.10 Engineering Group,Hefei 250101,Anhui,China)

Online published: 2024-01-04

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Abstract

To timely, accurately, and comprehensively acquire the uniaxial saturated compressive strength of rocks at the tunnel face and provide an important prerequisite for promoting intelligent tunnel construction and achieving dynamic changes in rock mass classification, a case study is conducted on the Lingshangcun tunnel of the ChizhouHuangshan highspeed railway. Rockdrilling jumbo parameters from 41 sets of tunnel face and surrounding rock strength data from four sets of rocksimilar materials are obtained, respecrtively. After analyzing the rockbreaking process of the rockdrilling jumbo, four indicators, impact pressure, propulsion pressure, rotation pressure, and drilling rate, are selected as input parameters for the prediction model. Strength prediction models are established based on singlefactor regression, multiple nonlinear regression, and the least squares support vector machine (LSSVM) algorithm. The prediction results of the model are as follows: (1) The singlefactor prediction model, using drilling rate as the input parameter, achieves the highest prediction accuracy with a correlation coefficient of 0.945 8 between the predicted and actual values. (2) The multiplefactor prediction model improves the prediction accuracy further, with a correlation coefficient of 0.974 5. (3) The LSSVM algorithm models demonstrates a correlation coefficient of 0.983 8 between the predicted and actual values, with a prediction accuracy close to that of multiple nonlinear regression. This indicates that both models can be used for rock strength prediction in practical applications.

Key words： rock drilling jumbo; uniaxial saturated compressive strength of rock; drilling parameters; regression analysis; machine learning

Cite this article

ZHANG Shucai , QIU Wenge , ZHANG Qifang , ZHANG Yijiang , JIANG Shuhua , LUO Jie .

Rock Strength Prediction Model Based on Drilling Parameters of Rock Drilling Jumbo

[J]. Tunnel Construction, 2023 , 43(12) : 2007 -2017 . DOI: 10.3973/j.issn.2096-4498.2023.12.003

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