基于钻进参数和岩性的钻爆法隧道围岩智能分级模型

doi:10.3973/j.issn.2096-4498.2024.07.008

隧道建设（中英文） ›› 2024, Vol. 44 ›› Issue (7): 1410-1421.DOI: 10.3973/j.issn.2096-4498.2024.07.008

基于钻进参数和岩性的钻爆法隧道围岩智能分级模型

夏覃永^{1, 2}，王明年^{1, 2, *}, 孙鸿强^{1, 2}, 林鹏^{1, 2}，赵思光³

(1. 西南交通大学极端环境岩土和隧道工程智能建养全国重点实验室，四川成都 610031；2. 西南交通大学土木工程学院，四川成都 610031； 3. 中国铁路经济规划研究院有限公司铁路工程技术标准所，北京 100038)

出版日期:2024-07-20 发布日期:2024-08-05
作者简介:夏覃永（2001—），男，湖北天门人，西南交通大学桥梁与隧道工程专业在读硕士，研究方向为隧道智能建造。E-mail： 578283276@qq.com。*通信作者：王明年， E-mail： 2241557566@qq.com。

Intelligent Classification for Surrounding Rock of Tunnel by Drilling and Blasting Method Based on Drilling Parameters and Lithology

XIA Qinyong^{1, 2}, WANG Mingnian^{1, 2, *}, SUN Hongqiang^{1, 2}, LIN Peng^1,
2, ZHAO Siguang³

(1. State Key Laboratory of Intelligent Geotechnics and Tunnelling, Southwest Jiaotong University, Chengdu 610031, Sichuan, China; 2. School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, Sichuan, China; 3. Railway Engineering Technical Standards Institut, China Railway Economic and Planning Research Institute, Beijing 100038, China)

Online:2024-07-20 Published:2024-08-05

摘要/Abstract

摘要： 为进一步提高基于钻进参数的围岩智能分级模型判识准确率，引入围岩岩性作为已知条件，对不同岩性分别建立基于钻进参数的围岩智能分级模型。首先，依托宜兴高铁、郑万高铁隧道工程中凿岩台车采集到的钻进参数，将其根据围岩岩性划分为白云岩、花岗闪长岩、灰岩、砂岩、页岩5类子数据集；然后，进行数据清洗、特征提取，对各子数据集及原始数据集按照4∶1的比例拆分为训练集和测试集，分别以提取的特征和原始特征均值2类特征集合作为输入；最后，使用机器学习随机森林算法建立5个基于钻进参数和岩性的围岩智能分级模型以及基于钻进参数的围岩智能分级模型，评估每个模型的泛化性能。结果显示： 1）以提取特征集合为输入的白云岩、花岗闪长岩、灰岩、砂岩、页岩5种围岩智能分级模型在测试集上的准确率分别为85.48%、92.16%、88.62%、85.00%、89.47%，不考虑岩性的围岩智能分级模型的测试集准确率为84.91%。2）相比不考虑岩性的围岩智能分级模型，基于钻进参数和岩性的围岩智能分级模型测试集准确率提高0.09%~7.25%；相比不进行钻进参数特征提取，围岩智能分级模型准确率提升1.61%~13.82%。由此表明：进行钻进参数特征提取能够有效提升围岩智能分级模型的准确率；相比不考虑岩性的围岩智能分级模型，考虑岩性的围岩智能分级模型稳定性提高，模型的泛化能力提升。

关键词: 隧道工程, 围岩智能分级, 随机森林算法, 钻进参数, 特征提取, 岩性

Abstract: An intelligent model that considers lithology in classifying surrounding rock is established based on drilling parameters so as to further improve the accuracy of predicting the surrounding rock. First, the drilling parameters collected by a drilling trolley in the Yichang-Xingshan and Zhengzhou-Wanzhou high-speed railway tunnels are divided into the five data subsets of dolomite, granodiorite, limestone, sandstone, and shale. Next, data cleaning and feature extraction are conducted, and each data subset and original data set are divided into a training set and test set in a 4∶1 ratio. Furthermore, the extracted features and the original features are used as inputs to establish five intelligent classification models for surrounding rock based on drilling parameters and lithology and an intelligent classification model for surrounding rock based on drilling parameters using a machine learning random forest algorithm. Finally, the generalization performance of each model is evaluated. The results demonstrate the following. (1) The models that are based on dolomite, granodiorite, limestone, sandstone, and shale achieve an accuracy on the test set of 85.48%, 92.16%, 88.62%, 85.00%, and 89.47%, respectively, and the accuracy of the model that does not consider lithology is 84.91%. (2) Compared with the improvement in the model that considers lithology, the improvement in the accuracy on the test set of the surrounding rock intelligent classification model based on drilling parameters and lithology is 0.09%－7.25%, and the improvement in model accuracy is 1.61%－13.82% compared with no feature extraction. This shows that extracting the features of the drilling parameters effectively improves the accuracy of the model. The intelligent model that considers lithology in surrounding rock classification is more stable and achieves better generalization performance than the model that does not consider lithology.

Key words: tunnel engineering, intelligent surrounding rock classification, random forest, drilling parameters, feature extraction, lithology

夏覃永, 王明年, 孙鸿强, 林鹏, 赵思光. 基于钻进参数和岩性的钻爆法隧道围岩智能分级模型[J]. 隧道建设, 2024, 44(7): 1410-1421.

XIA Qinyong, WANG Mingnian, SUN Hongqiang, LIN Peng, ZHAO Siguang. Intelligent Classification for Surrounding Rock of Tunnel by Drilling and Blasting Method Based on Drilling Parameters and Lithology[J]. Tunnel Construction, 2024, 44(7): 1410-1421.

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基于钻进参数和岩性的钻爆法隧道围岩智能分级模型

Intelligent Classification for Surrounding Rock of Tunnel by Drilling and Blasting Method Based on Drilling Parameters and Lithology

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