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

作     者：Mingnian Wang Siguang Zhao Jianjun Tong Zhilong Wang Meng Yao Jiawang Li Wenhao Yi 

作者机构：Key Laboratory of Transportation Tunnel EngineeringMinistry of EducationSouthwest Jiaotong UniversityChengdu 610031China School of Civil EngineeringSouthwest Jiaotong UniversityChengdu 610031China 

出 版 物：《Underground Space》 (地下空间（英文）)

年 卷 期：2021年第6卷第5期

页      面：539-550页

核心收录：

学科分类：081406[工学-桥梁与隧道工程] 08[工学] 0818[工学-地质资源与地质工程] 0815[工学-水利工程] 0813[工学-建筑学] 0814[工学-土木工程] 082301[工学-道路与铁道工程] 0823[工学-交通运输工程] 

基　　金：supported by the National Natural Science Foundation of China(NSFC)[Grant Nos.51578458,and 51878568] the China Railway Corporation Science and Technology Research and Development Program[Grant Nos.2017G007-H,2017G007-F,P2018G007,K2018G014,and K2018G014-01] 

主　　题：Drilled and blasted tunnel Drilling parameter Machine learning Intelligent classification Surrounding rock 

摘      要：Classification of surrounding rock is the cornerstone of tunnel design and *** traditional methods are mainly qualitative and manual and require extensive professional knowledge and engineering *** minimize the effect of the empirical judgment on the accuracy of surrounding rock classification,it is necessary to reduce human *** intelligent classification technique based on information technology and artificial intelligence could overcome these *** 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 *** 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 *** approach provides a foundation for the dynamic design and construction(both conventional and intelligent)of tunnels.