理论与试验研究

基于双模型策略的隧道围岩完整性分析方法

  • 柳厚祥 ,
  • 喻娇
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  • 长沙理工大学 土木工程学院,长沙 410000
柳厚祥(1965—),男,湖南邵阳人,博士,教授,主要从事岩土工程、地下工程等领域的教学与科研工作。E-mail: liuhoux1@163.com
喻娇(2000—),女,湖南岳阳人,硕士生,主要研究方向为岩土工程。E-mail: 15074098712@163.com

收稿日期: 2025-09-15

  网络出版日期: 2026-09-07

基金资助

湖南省自然科学基金面上项目(2024JJ5036)

Analysis Method for Tunnel Surrounding Rock Integrity Based on Dual-Model Strategy

  • Liu Houxiang ,
  • Yu Jiao
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  • School of Civil Engineering, Changsha University of Science & Technology, Changsha 410000, P. R. China

Received date: 2025-09-15

  Online published: 2026-09-07

摘要

为了克服深度学习模型在进行隧道围岩完整性分析时面临的效率低、计算成本高等问题,提出了一种基于YOLO和DeepLab的双模型策略,实现了隧道围岩完整性的快速评估。该方法构建基于YOLO算法的围岩裂隙识别模型,再引入DeepLab图像语义分割算法,实现裂隙参数的智能化提取,并将结果可视化;通过整合处理后的图像信息,引入一种评估围岩裂隙发育程度的新指标——裂隙因子Ff,定量分析围岩裂隙的发育程度,并确定围岩完整性系数Kv。将国内典型工程的5 000张图片用于测试,结果表明:基于YOLO和DeepLab的双模型策略对于裂隙识别的准确率为96.41%,而裂隙分割的准确率为94.48%;与传统深度学习算法的结果相比,裂隙因子Ff对围岩完整性的评估更接近于BQ法中的完整性系数;双模型策略在确保准确率的同时,大幅降低计算成本,提升了深度学习模型在实际工程应用中的可行性。

本文引用格式

柳厚祥 , 喻娇 . 基于双模型策略的隧道围岩完整性分析方法[J]. 地下空间与工程学报, 2026 , 22(4) : 1179 -1187 . DOI: 10.20174/j.JUSE.2026.04.07

Abstract

To address the challenges of low efficiency and high computational costs faced by deep learning models in tunnel surrounding rock integrity analysis, a dual-model strategy based on YOLO and DeepLab is proposed, enabling rapid assessment of tunnel surrounding rock integrity. This method constructs a rock fracture recognition model based on the YOLO algorithm and introduces the DeepLab image semantic segmentation algorithm to intelligently extract fracture parameters, with results visualized. By integrating the processed image information, a new indicator for evaluating the development degree of rock fractures—the Fracture Factor (Ff)—is introduced to quantitatively analyze the development degree of surrounding rock fractures and determine the surrounding rock integrity coefficient (Kv). Using 5 000 images from typical domestic engineering projects for testing, the results show that the dual-model strategy based on YOLO and DeepLab achieves a fracture recognition accuracy of 96.41% and a fracture segmentation accuracy of 94.48%. Compared to traditional deep learning algorithms, the Fracture Factor (Ff) provides an assessment of surrounding rock integrity that is closer to the integrity coefficient in the BQ method. The dual-model strategy significantly reduces computational costs while ensuring accuracy, enhancing the feasibility of deep learning models in practical engineering applications.

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