Biologically inspired adaptive crack network reconstruction based on slime mould algorithm

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.