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<p>This study integrated the Rock Mass Rating (RMR) classification system with the Distinct Element Method (DEM) to quantify post excavation deformation in a tunnel associated with the Rishikesh–Karnprayag broad-gauge rail link project in Uttarakhand, India. Block displacement was used as the primary deformation indicator, supplemented by strength-to-stress ratio assessment and principal stress evaluation. RMR classes ranging from poor to good conditions were investigated across three numerical design scenarios, pessimistic, medial, and optimistic, to account for geotechnical uncertainty. Fifty four numerical models were developed with overburden depths varying from 25&#xa0;m to 500&#xa0;m, to examine stress induced deformation patterns and associated failure mechanisms. Hierarchical clustering was employed to group geotechnical attributes, while Random Forest, Extreme Gradient Boosting, and Shapley Additive Explanations were utilized to identify critical parameters influencing deformation behaviour. The results demonstrated a systematic decrease in block displacement with an increase in RMR value, demonstrating its efficiency as a deformation predictor. Strength-to-stress ratio assessment and principal stress distributions corroborated these results. This research successfully bridged empirical classification and numerical modelling, providing a transferable framework for deformation assessment in complex geological environments, such as the Himalaya.</p>

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Numerical Evaluation and Validation of Ground Deformation as a Function of Characterized Rock Mass and Stress Regime

  • 期刊论文

  • 发表日期：

    2026年1月15日

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  • Vikas Yadav,
  • Ashutosh Kainthola,
  • Vijay Dangwal,
  • Harish Bahuguna

摘要

This study integrated the Rock Mass Rating (RMR) classification system with the Distinct Element Method (DEM) to quantify post excavation deformation in a tunnel associated with the Rishikesh–Karnprayag broad-gauge rail link project in Uttarakhand, India. Block displacement was used as the primary deformation indicator, supplemented by strength-to-stress ratio assessment and principal stress evaluation. RMR classes ranging from poor to good conditions were investigated across three numerical design scenarios, pessimistic, medial, and optimistic, to account for geotechnical uncertainty. Fifty four numerical models were developed with overburden depths varying from 25 m to 500 m, to examine stress induced deformation patterns and associated failure mechanisms. Hierarchical clustering was employed to group geotechnical attributes, while Random Forest, Extreme Gradient Boosting, and Shapley Additive Explanations were utilized to identify critical parameters influencing deformation behaviour. The results demonstrated a systematic decrease in block displacement with an increase in RMR value, demonstrating its efficiency as a deformation predictor. Strength-to-stress ratio assessment and principal stress distributions corroborated these results. This research successfully bridged empirical classification and numerical modelling, providing a transferable framework for deformation assessment in complex geological environments, such as the Himalaya.