基于多元地质信息的钻爆法隧道围岩级别智能判识及软件系统  

Intelligent Classification and Software System for Surrounding Rock Based on Multivariate Geological Information

作　　者：王明年[1,2] 童建军 易文豪[1,2] 彭鑫 WANG Mingnian;TONG Jianjun;YI Wenhao;PENG Xin(School of Civil Engineering,Southwest Jiaotong University,Chengdu 610031,Sichuan,China;State Key Laboratory of Intelligent Geotechnics and Tunnelling,Southwest Jiaotong University,Chengdu 610031,Sichuan,China)

机构地区：[1]西南交通大学土木工程学院,四川成都610031 [2]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

出　　处：《隧道建设(中英文)》2024年第12期2451-2468,I0053-I0070,共36页Tunnel Construction

摘　　要：为提升施工阶段围岩级别判识的智能化水平,提升围岩级别智能判识的准确性,提出一种基于多元地质信息和信息融合的围岩级别智能判识方法,并开发基于多元地质信息的钻爆法隧道围岩级别智能判识软件系统,实现隧道工程多元信息的自动采集和围岩级别自动化判识。依托我国西部山区隧道工程,采集随钻参数、掌子面高清数码图像、超前地质预报信息、地勘信息等4项多元地质信息,开展数据标准化、结构化处理及特征提取,构建基于多元地质信息的围岩级别智能判识模型,共判定844个断面的围岩级别,模型平均准确率达到95.45%,平均精确率为95.05%,平均召回率为93.25%,平均F_(1)分数为94.14%,对软硬不均及局部破碎围岩具有较好的识别效果。In order to improve the intelligentization level of surrounding rock classification in the construction stage and enhance the accuracy of intelligent rock classification,an intelligent rock classification method based on multivariate geological information and information fusion is proposed.An intelligent classification software system based on multivariate geological information is developed to realize automatic acquisition of multivariate geological information and automatic classification of surrounding rock for drill-and-blast tunnels.Based on tunnel projects in the western mountainous area of China,four types of multivariate geological information,including drilling parameters,high-definition digital images of tunnel face,advance geological prediction information,and geological exploration information,are collected.Standardized and structured data processing and feature extraction are carried out.An intelligent classification model of surrounding rock based on multivariate geological information is constructed,and a web-end intelligent classification software system of surrounding rock based on multivariate geological information is developed for drill-and-blast tunnels.The accuracy of the intelligent classification model proposed in this study reached 95.45%,with an average accuracy of 95.05%,an average recall rate of 93.25%,and an average F_(1) score of 94.14%.Field applications of the software system have been carried out for rock classification at 844 sections,which had a good performance on identifying unevenly distributed soft and hard rock or locally fractured rock.

关 键 词：钻爆法隧道 多元地质信息 围岩级别 智能判识 图像识别 信息融合 

分 类 号：U45[建筑科学—桥梁与隧道工程]