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Rockburst grade evaluation and parameter sensitivity analysis based on SSA-PNN framework: Considering rock mass
Xiaoyan Zhou1, Yimin Jiang1, Zhenyi Wang1
1School of Architectural Engineering, Zhengzhou University of Industrial Technology, Zhengzhou, China.
Rockburst prediction accuracy is enhanced using a Sparrow Search Algorithm (SSA) and Probabilistic Neural Network (PNN) model. This framework effectively evaluates rockburst grades by analyzing key rock mass parameters for tunnel safety.
Area of Science:
- Geotechnical Engineering
- Rock Mechanics
- Computational Intelligence
Background:
- Rockbursts pose significant risks in underground excavations, influenced by rock mass strength and stress conditions.
- Accurate rockburst grade evaluation is crucial for ensuring the safety and stability of tunnels.
Purpose of the Study:
- To develop and validate a novel rockburst grade evaluation system using advanced computational methods.
- To analyze the sensitivity of different rock mass parameters to rockburst occurrences.
Main Methods:
- Collected rock mass strength and stress data from 22 rockburst locations in the Lalin Railway tunnel.
- Utilized Sparrow Search Algorithm (SSA) to optimize parameters for a Probabilistic Neural Network (PNN).
- Constructed an SSA-PNN framework for rockburst grade evaluation and sensitivity analysis.
Main Results:
- The SSA-PNN framework demonstrated efficient and reliable rockburst grade evaluation.
- Maximum tangential stress and uniaxial compressive strength of rock mass were identified as the most influential parameters.
- Maximum in-situ stress and uniaxial compressive strength of rock showed lesser, but still relevant, influence.
Conclusions:
- The integrated SSA-PNN approach provides a highly reliable method for rockburst grade evaluation.
- The findings offer valuable insights for selecting critical parameters in rockburst assessment.
- This study contributes to improved safety measures in tunnel engineering projects prone to rockbursts.
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