Scholarlink

<p>This study proposes a seismic wave image recognition-based model to predict rock mass fracturing ahead of TBM faces, enhancing tunneling safety and efficiency. By integrating the Grey Wolf Optimization (GWO) algorithm with maximum entropy segmentation (optimal at population size 20), the model achieved superior image segmentation. Comparative analysis against GWO-Otsu, GA-Otsu, SSA-Otsu, GA-Kapur, and SSA-Kapur methods revealed that the maximum entropy method outperformed Otsu in PSNR and SSIM metrics, with GWO consistently attaining higher fitness values. Image slicing and feedback area analysis refined predictions, linking geological forecasts to TBM advancement increments. A total feedback area ratio of 0.3 and a positive-to-negative feedback ratio of 0.25–4 indicated poor rock integrity, triggering feature point detection. The ORB algorithm effectively extracted and matched feature points in SAP image feedback masks, enabling fracture classification based on point density. Engineering validation in a tunnel section confirmed model accuracy: post-reinforcement deformations aligned with predictions. The framework supports intelligent TBM tunneling by optimizing fracture prediction, balancing computational efficiency (via GWO) with precision (via maximum entropy), and establishing quantitative thresholds for rock integrity assessment. This approach advances automated geological hazard mitigation in TBM-driven tunnels.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on a Predictive Method for Rock Mass Fracturing in TBM Tunnels Based on Seismic Wave Image Recognition

  • 期刊论文

  • 发表日期：

    2025年8月6日

查看全文
  • Daohong Qiu,
  • Tao Shao,
  • Yiguo Xue,
  • Wenqing Zhang,
  • Kang Fu

摘要

This study proposes a seismic wave image recognition-based model to predict rock mass fracturing ahead of TBM faces, enhancing tunneling safety and efficiency. By integrating the Grey Wolf Optimization (GWO) algorithm with maximum entropy segmentation (optimal at population size 20), the model achieved superior image segmentation. Comparative analysis against GWO-Otsu, GA-Otsu, SSA-Otsu, GA-Kapur, and SSA-Kapur methods revealed that the maximum entropy method outperformed Otsu in PSNR and SSIM metrics, with GWO consistently attaining higher fitness values. Image slicing and feedback area analysis refined predictions, linking geological forecasts to TBM advancement increments. A total feedback area ratio of 0.3 and a positive-to-negative feedback ratio of 0.25–4 indicated poor rock integrity, triggering feature point detection. The ORB algorithm effectively extracted and matched feature points in SAP image feedback masks, enabling fracture classification based on point density. Engineering validation in a tunnel section confirmed model accuracy: post-reinforcement deformations aligned with predictions. The framework supports intelligent TBM tunneling by optimizing fracture prediction, balancing computational efficiency (via GWO) with precision (via maximum entropy), and establishing quantitative thresholds for rock integrity assessment. This approach advances automated geological hazard mitigation in TBM-driven tunnels.