Intelligent classification model of surrounding rock of tunnel using drilling and blasting method

Underground Space

Jiawang Li¹  Zhilong Wang²  Siguang Zhao²  Wenhao Yi²  Meng Yao³  Mingnian Wang⁴  Jianjun Tong⁴ 

[1] Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China;School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China;Corresponding author at: Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China.;Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Southwest Jiaotong University, Chengdu 610031, China;

关键词: Drilled and blasted tunnel;    Drilling parameter;    Machine learning;    Intelligent classification;    Surrounding rock;   

DOI  :  

来源: DOAJ

【 摘 要 】

Classification of surrounding rock is the cornerstone of tunnel design and construction. The traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering experience. To minimize the effect of the empirical judgment on the accuracy of surrounding rock classification, it is necessary to reduce human participation. An intelligent classification technique based on information technology and artificial intelligence could overcome these issues. In this regard, using 299 groups of drilling parameters collected automatically using intelligent drill jumbos in tunnels for the Zhengzhou–Wanzhou high-speed railway in China, an intelligent-classification surrounding-rock database is constructed in this study. Based on a machine learning algorithm, an intelligent classification model is then developed, which has an overall accuracy of 91.9%. Finally, using the core of the model, the intelligent classification system for the surrounding rock of drilled and blasted tunnels is integrated, and the system is carried by intelligent jumbos to perform automatic recording and transmission of drilling parameters and intelligent classification of the surrounding rock. This approach provides a foundation for the dynamic design and construction (both conventional and intelligent) of tunnels.

【 授权许可】

Unknown