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Updated: Jun 23, 2026

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Biologically inspired adaptive crack network reconstruction based on slime mould algorithm.

Zeng Chen1,2,3, Xiaocong Yang4,5, Ping Wang4

  • 1Beijing General Research Institute of Mining & Metallurgy, Beijing, 100160, China. zschenzeng@126.com.

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|November 6, 2024
PubMed
Summary

This study uses a novel slime mould algorithm to accurately map internal rock cracks and their propagation paths. This method improves rock stability analysis by precisely characterizing fracture patterns.

Keywords:
Bond lengthCrack characterizationCracking levelsSlime mould algorithm

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Area of Science:

  • Geomechanics and Material Science
  • Computational Intelligence and Optimization

Background:

  • Visualizing internal crack propagation is challenging, hindering understanding of rock fracture mechanisms.
  • Biological networks offer insights into solving complex optimization problems, relevant for crack trajectory analysis.

Purpose of the Study:

  • To develop an accurate method for localizing internal cracks in rocks.
  • To construct crack propagation trajectories using advanced computational models.
  • To characterize crack evolution and correlate it with rock stability.

Main Methods:

  • Application of the slime mould algorithm for improved internal crack localization.
  • Utilizing Minimum Spanning Tree and Gaussian Mixture Model to construct crack trajectories.
  • Introduction of 'bond length' concept to characterize crack evolution.

Main Results:

  • The proposed approach effectively preserves crack localization data while reducing interference from extraneous parameters.
  • Crack characterization results demonstrate high consistency with observed fracture patterns.
  • Cumulative and relative bond length curves follow a Growth/Sigmoidal trend, correlating bond strength with crack propagation over time.

Conclusions:

  • The slime mould algorithm, MST, and GMM provide a robust framework for analyzing dynamic crack propagation.
  • Characterizing crack evolution via bond length offers valuable insights into temporal fracture processes.
  • This methodology enhances the prediction of rock stability by accurately analyzing crack trajectories.