Intelligent classification model of surrounding rock of tunnel using drilling and blasting method
Mingnian Wang, Siguang Zhao, Jianjun Tong, Zhilong Wang, Meng Yao, Jiawang Li, Wenhao Yi
Abstract
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.