Analysis of key geological structures and rockburst prediction method

Area of Science:

• Geotechnical Engineering
• Mining Engineering
• Computational Mechanics

Background:

• Rockburst disasters are intrinsically linked to specific geological structures, necessitating a focus on the 'geological carrier' for accurate prediction.
• Existing methods often overlook the nuanced influence of geological structures on rockburst potential.
• Understanding geomechanical modes and structural effects is crucial for mitigating rockburst risks.

Purpose of the Study:

• To develop a novel prediction method for rockbursts that explicitly incorporates the influence of key geological structures.
• To establish a reliable evaluation index for rockburst energy release and parameter sensitivity.
• To create a rapid, dictionary-style prediction tool for rockbursts in underground engineering.

Main Methods:

• Numerical simulations were employed to establish an evaluation index for relative energy release and analyze parameter sensitivity.
• A surrogate model was developed using GA-BP neural network and Latin hypercube sampling to link structural parameters with rockburst effects.
• The Monte Carlo method and rockburst intensity classification were used to identify dangerous samples and interpret confidence intervals for key parameters.

Main Results:

• An evaluation index for rockburst energy release was established, enabling sensitivity analysis of key structural parameters.
• A GA-BP neural network surrogate model accurately represented the relationship between structural parameters and rockburst effects.
• Confidence intervals for key structural parameters were determined, leading to a predictive method for identifying hazardous geological structures.

Conclusions:

• The proposed method offers a novel pathway for precise rockburst disaster prediction by comprehensively considering geological structure impacts.
• This innovative approach facilitates rapid, dictionary-style rockburst prediction in underground engineering.
• The study provides a valuable tool for enhancing safety and mitigating risks in mining and underground construction.