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基于深度学习技术的公路隧道围岩分级方法

柳厚祥, 李汪石, 查焕奕, 蒋武军, 许腾

柳厚祥, 李汪石, 查焕奕, 蒋武军, 许腾. 基于深度学习技术的公路隧道围岩分级方法[J]. 岩土工程学报, 2018, 40(10): 1809-1817. DOI: 10.11779/CJGE201810007
引用本文: 柳厚祥, 李汪石, 查焕奕, 蒋武军, 许腾. 基于深度学习技术的公路隧道围岩分级方法[J]. 岩土工程学报, 2018, 40(10): 1809-1817. DOI: 10.11779/CJGE201810007
shu
LIU Hou-xiang, LI Wang-shi, ZHA Zhuan-yi, JIANG Wu-jun, XU Teng. Method for surrounding rock mass classification of highway tunnels based on deep learning technology[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(10): 1809-1817. DOI: 10.11779/CJGE201810007
Citation: LIU Hou-xiang, LI Wang-shi, ZHA Zhuan-yi, JIANG Wu-jun, XU Teng. Method for surrounding rock mass classification of highway tunnels based on deep learning technology[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(10): 1809-1817. DOI: 10.11779/CJGE201810007
shu

基于深度学习技术的公路隧道围岩分级方法  English Version

  • 1. 长沙理工大学土木工程学院,湖南 长沙 410114;
    2. 湖南省永吉高速公路建设开发有限公司,湖南 吉首 416000;
    3. 湖南省建筑设计院有限公司,湖南 长沙 410000

基金项目: 湖南省交通科技项目（201331）; 长沙理工大学优势学科项目（17ZDXK01）
详细信息
    作者简介:

    柳厚祥(1965- ),男,湖南省邵阳人,硕士生导师,博士。E-mail: liuhoux1@163.com。

  • 中图分类号: U451.2

Method for surrounding rock mass classification of highway tunnels based on deep learning technology

  • 1. School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China;
    2. Hunan Yongzhou to Jishou Expressway Construction and Development Co., Ltd., Jishou 416000, China;
    3. Hunan Province Architectural Design Institute Co., Ltd., Changsha 410000, China

摘要

    摘要
  • 摘要: 通过深度学习技术提取公路隧道掌子面图片中的围岩分级相关信息。训练以掌子面图片和特征标签为数据集的深度卷积神经网络模型,识别围岩的节理、裂隙、破碎程度、粗糙程度、光滑程度、泥夹石和涌水等分布式特征;结合深度学习技术和岩体裂隙图像智能解译方法统计围岩节理组数和间距来描述结构面完整程度;再利用色彩模型确定岩石种类描述出岩石坚硬程度;最后将围岩分级各判别因子转换为BQ值进行分级,获得围岩分级最终结果。结果表明：深度学习模型适用于识别围岩不同形态特征,利用图像识别技术获取的围岩分级参数能够实现对公路隧道围岩等级的综合判定。该处理结果与传统BQ分级结果相吻合,验证了深度学习围岩分级的可行性和准确性。
    Abstract: By extracting the relevant information of surrounding rock classification of road tunnel face using the deep learning technology, a multilayer convolution neural network model is established to recognize the distributive features of surrounding rock including joints, cracks, broken situations, rough degrees, smooth degrees, mud stone and water burst, etc. The deep learning AlexNet model is modified to count the number and spacing of rock joints. The deep convolution is used to extract different rock boundaries, and the specific species of rock are determined by the comprehensive color model. The degree of development of structural plane, rock hardness, structural plane roughness, groundwater development, structural types and degree of grade factors of the surrounding rock classification are qualitatively described for the results of the surrounding rock classification so as to obtain the final results of the surrounding rock classification. The results show that the deep learning model is applicable to identify different morphological characteristics of the surrounding rock. Based on the Matlab interface technology, image recognition technology, boundary extraction technology and HIS color model, the comprehensive judgement of surrounding rock classification of highway tunnels is realized. In order to verify its feasibility and accuracy, the classification results of the deep learning technology are compared with those of the traditional BQ classification.
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    • 参考文献(16)
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出版历程
  • 收稿日期:  2017-08-08
  • 发布日期:  2018-10-24

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