基于凿岩台车钻进参数的岩石强度预测模型研究

张树才; 仇文革; 张齐芳; 张义江; 江书华; 罗杰

doi:10.3973/j.issn.2096-4498.2023.12.003

隧道建设 >

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

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

研究与探索

基于凿岩台车钻进参数的岩石强度预测模型研究

张树才 ,
仇文革 ,
张齐芳 ,
张义江 ,
江书华 ,
罗杰

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(1. 皖赣铁路安徽有限责任公司，安徽合肥 230011； 2. 成都天佑智隧科技有限公司，四川成都 610031； 3. 西南交通大学交通隧道工程教育部重点实验室，四川成都 610031； 4. 中铁上海设计院集团有限公司，上海 200070； 5. 中铁十局集团第三建筑有限公司，安徽合肥 250101)

张树才（1973—），男，吉林通榆人，1995年毕业于阜新矿业学院，采矿专业，本科，高级工程师，主要从事铁路工程建设管理工作。Email: 359466592@qq.com。*通信作者：罗杰， 1550279201@qq.com。

网络出版日期: 2024-01-04

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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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摘要

为及时、准确、全面获取掌子面岩石单轴饱和抗压强度，并为推进隧道智能化建造、实现围岩级别动态变更提供重要依据，依托池黄高铁2标段岭上村隧道工程，获取42组掌子面以及4组岩石相似材料的凿岩台车钻进参数和岩石强度数据。在对凿岩台车的破岩过程进行分析后，选取冲击压力、推进压力、回转压力、钻进速率共4项指标作为预测模型的输入参数，分别建立单因素回归、多元非线性回归和LSSVM算法的岩石强度预测模型。模型预测结果表明: 1)单因素预测模型中采用钻进速率作为输入参数具有最高的预测精度，预测值与实际值的相关系数为0.945 8; 2)采用多因素预测模型后，其预测准确率进一步提高，相关系数达到0.974 5; 3)LSSVM算法模型的预测值与实际值的相关系数为0.983 8，其预测准确率与多元非线性回归相当，均可用于实际工程中的岩石强度预测。

关键词： 凿岩台车; 岩石单轴饱和抗压强度; 钻进参数; 回归分析; 机器学习

本文引用格式

张树才 , 仇文革 , 张齐芳 , 张义江 , 江书华 , 罗杰 . 基于凿岩台车钻进参数的岩石强度预测模型研究[J]. 隧道建设, 2023 , 43(12) : 2007 -2017 . DOI: 10.3973/j.issn.2096-4498.2023.12.003

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

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