基于掌子面三维几何图像计算Jv值的方法研究

曹勇; 杨川; 仇文革; 王先毫; 白衡斌; 凌鹏

doi:10.3973/j.issn.2096-4498.2023.09.006

隧道建设 >

2023 , Vol. 43 >Issue 9: 1492 - 1500

DOI: https://doi.org/10.3973/j.issn.2096-4498.2023.09.006

研究与探索

基于掌子面三维几何图像计算Jv值的方法研究

曹勇 ,
杨川 ,
仇文革 ,
王先毫 ,
白衡斌 ,
凌鹏

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（1. 中铁上海设计院集团有限公司，上海 200070； 2. 皖赣铁路安徽有限责任公司，安徽合肥 230000； 3. 成都天佑智隧科技有限公司，四川成都 610031； 4. 西南交通大学交通隧道工程教育部重点实验室，四川成都 610031； 5. 中铁十局集团第三建筑有限公司，安徽合肥 250101）

曹勇（1983—），男，山东郓城人，2008年毕业于石家庄铁道学院，桥梁与隧道工程专业，硕士，高级工程师，主要从事隧道工程设计工作。 E-mail： caoyong@sty.sh.cn。

网络出版日期: 2023-10-16

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Calculation Method for Jv Value Based on Three-Dimensional Geometric Image of Tunnel Face

CAO Yong ,
YANG Chuan ,
QIU Wenge ,
WANG Xianhao ,
BAI Hengbin ,
LING Peng

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(1. China Railway Shanghai Design Institute Group Corporation Limited, Shanghai 200070, China;2. Anhui Jiangxi Railway Anhui Co., Ltd., Hefei 230000, Anhui, China; 3. Chengdu Tianyou Tunnel Key Company Ltd., Chengdu 610031, Sichuan, China; 4. Key Laboratory of Transportation Tunnel Engineering, the Ministry of Education, Southwest Jiaotong University, Chengdu 610031, Sichuan, China; 5. No.3 Construction Company of China Railway No.10 Engineering Group Co., Ltd., Hefei 250101, Anhui, China)

Online published: 2023-10-16

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摘要

为解决传统人工素描的诸多弊端，提出在掌子面三维几何图像的基础上提取掌子面围岩结构面特征。使用相机和激光雷达采集掌子面围岩数据，建立隧道掌子面三维实景模型；基于围岩结构面法向量的差异，在三维模型上对围岩结构面进行识别；采用卷积神经网络算法训练软件基于地质工程师地质素描识别围岩节理迹线；将三维模型识别结果与图像识别训练进行结合判识，实现掌子面围岩节理迹线的自动识别；基于自动识别结果得到掌子面围岩的岩体体积节理数Jv，从而实现对围岩完整程度的判识。将该方法应用于池黄铁路黄坑隧道和岭上村隧道进行掌子面围岩完整度分析，结果表明：该方法可获取高精度的掌子面三维模型，能够对隧道掌子面围岩的完整度进行快速、准确的判识，实现隧道地质信息编录。

关键词： 铁路隧道; 掌子面; 三维重建; 卷积神经网络; 岩石节理; 岩体完整程度

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曹勇 , 杨川 , 仇文革 , 王先毫 , 白衡斌 , 凌鹏 . 基于掌子面三维几何图像计算Jv值的方法研究[J]. 隧道建设, 2023 , 43(9) : 1492 -1500 . DOI: 10.3973/j.issn.2096-4498.2023.09.006

Abstract

To address the limitations of traditional artificial sketching, an extraction method for the surrounding rock structural surface features of the tunnel face based on a threedimensional(3D) geometric image is proposed. First, the surrounding rock data of the tunnel face is collected using a camera and LiDAR, enabling the creation of a 3D realistic model of the tunnel face. Then, the structural surfaces of the surrounding rocks are identified by analyzing the difference in their normal vectors within the 3D model. Furthermore, a convolutional neural network algorithm is used to train software in recognizing the surrounding rock joints based on geological engineers′ sketches. Subsequently, the recognition results of the 3D model and the image recognition training are combined with the recognition to achieve automatic recognition of the surrounding rock joints and tracks of the tunnel face. Finally, the integrity information of the surrounding rock at the tunnel face is calculated based on the joint number of rock volumn〖WT5BZ〗 Jv 〖WT５《TNR》〗of tunnel face′s surrounding rock. The method has been applied to the tunnel faces of Huangkeng and Lingshang village tunnels of the ChizhouHuangshan highspeed railway to analyze the integrity of the surrounding rock of the tunnel face. Results demonstrate that this method can obtain a highprecision 3D model of the tunnel face and quickly and accurately assess the integrity of the surrounding rock, facilitating the compilation of geological information about the tunnel.

Key words： ; railway tunnel; tunnel face; threedimensional reconstruction; convolutional neural network; rock joint; rock mass completeness

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