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<p>Grout veins formed by advance grouting influence bulk stiffness and permeability. We present a width‑centric, image‑to‑mechanics framework that performs SES preprocessing (HDMR‑based multi‑feature fusion and a moment‑based sub‑pixel localizer), extracts graph‑theoretic centerlines, and estimates vein widths via T<sub><i>MSE</i></sub>-adaptive segmentation, which directly supports property estimation. On a lab‑scale image set, SES improves segmentation across seven backbones, and the resulting width/orientation statistics yield laboratory‑scale estimates of elastic modulus and permeability. Worst‑case errors are 8.83% (modulus) and 10.42% (permeability); these do not meet design‑grade tolerances. The method is therefore positioned for screening/monitoring under controlled conditions, not for design decisions. We clarify assumptions (local monotonic edge profile, 2‑D effective analysis), provide stopping criteria and parameters for reproducibility.</p>

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A width centric image to mechanics framework for assessing advance grouting with TMSE adaptive width segmentation and lab scale validation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月20日

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  • Pengcheng Zhu,
  • Zhilei Zhang,
  • Tielin Chen,
  • Rongxin Wang,
  • Dingli Zhang,
  • Li Zhu,
  • Jing Guo

摘要

Grout veins formed by advance grouting influence bulk stiffness and permeability. We present a width‑centric, image‑to‑mechanics framework that performs SES preprocessing (HDMR‑based multi‑feature fusion and a moment‑based sub‑pixel localizer), extracts graph‑theoretic centerlines, and estimates vein widths via TMSE-adaptive segmentation, which directly supports property estimation. On a lab‑scale image set, SES improves segmentation across seven backbones, and the resulting width/orientation statistics yield laboratory‑scale estimates of elastic modulus and permeability. Worst‑case errors are 8.83% (modulus) and 10.42% (permeability); these do not meet design‑grade tolerances. The method is therefore positioned for screening/monitoring under controlled conditions, not for design decisions. We clarify assumptions (local monotonic edge profile, 2‑D effective analysis), provide stopping criteria and parameters for reproducibility.