Intelligent Surrounding Rock Grade Identification Combining XGBoost Algorithm and Drilling Parameters of Drill Jumbo

DOI

10.2991/978-94-6463-022-0_10How to use a DOI?

Keywords

drilling parameters; Surrounding rock grade; XGBoost; Intelligent identification

Abstract

Surrounding rock classification represents distinguishing the different grades of surrounding rock according to the hardness and integrity of surrounding rock. Accurately obtaining the surrounding rock grade of drill jumbo working face is not only the basis for selecting the tunnel position and support type, but also the key to ensure the safety of the drill jumbo’s construction site. As the traditional classification methods, engineering drilling and geological mapping are time-consuming and labor-intensive. Aiming at this situation, this paper proposes an intelligent identification method of surrounding rock grade combine drilling parameters with machine learning algorithm XGBoost. Firstly, adequately analyse the correlation between drilling parameters and rock label, and select six drilling parameters as feature vectors for surrounding rock grade recognition. Then outlier processing and data screening are carried out on the data recorded by the drill jumbo. Next, we construct a model based on XGBoost to realize the rapid and accurate identification of surrounding rock grade. Finally, the effectiveness and superiority of the proposed method are demonstrated by the actual data collected by the drill jumbo in Gao Jiaping tunnel, and mix the partial data of Alianqiu tunnel together to construct 5 datasets to compare the identification performance of other classical algorithms. The results show that the recognition capability of the proposed method is superior to those of other algorithms, and the recognition accuracy of surrounding rock along the tunnel can reach 99.68%.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Guoqiang Huang
AU  - Chengjin Qin
AU  - Feixiang Liu
AU  - Ruihong Wu
AU  - Jianfeng Tao
AU  - Chengliang Liu
AU  - Jinjun Liao
PY  - 2022
DA  - 2022/12/07
TI  - Intelligent Surrounding Rock Grade Identification Combining XGBoost Algorithm and Drilling Parameters of Drill Jumbo
BT  - Proceedings of the International Conference of Fluid Power and Mechatronic Control Engineering (ICFPMCE 2022)
PB  - Atlantis Press
SP  - 96
EP  - 114
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-022-0_10
DO  - 10.2991/978-94-6463-022-0_10
ID  - Huang2022
ER  -