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HELIOS-Stack: A Novel Hybrid Ensemble Learning Approach for Precise Joint Roughness Coefficient Prediction in Rock

Ibrahim Haruna Umar1,2, Hang Lin1, Hongwei Liu1

  • 1School of Resources and Safety Engineering, Central South University, Changsha 410083, China.

Materials (Basel, Switzerland)
|May 7, 2025
PubMed
Summary

A new hybrid ensemble learning method (HELIOS-Stack) accurately predicts joint roughness coefficient (JRC) for rock masses. This advanced technique improves geological surface analysis and rock mechanics understanding.

Keywords:
Gaussian mixture model (GMM)HELIOS-Stackjoint roughness coefficientmachine learningpredictive modelingrock discontinuity

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Area of Science:

  • Geotechnical Engineering
  • Rock Mechanics
  • Machine Learning Applications

Background:

  • Accurate joint roughness coefficient (JRC) estimation is vital for rock mass mechanical behavior analysis.
  • Existing predictive models struggle with complex geological surface morphology.
  • Limitations in current methods necessitate advanced JRC prediction techniques.

Purpose of the Study:

  • To develop an advanced hybrid ensemble learning methodology (HELIOS-Stack) for enhanced JRC prediction.
  • To integrate multiple machine learning models and statistical analysis for superior accuracy.
  • To establish a new benchmark for geological surface characterization.

Main Methods:

  • Implemented a hybrid ensemble approach combining Random Forest, XGBoost, LightGBM, Support Vector Regression, and Multilayer Perceptron.
  • Utilized a LightGBM meta-learner as the final estimator.
  • Analyzed 112 rock samples using eight statistical parameters and evaluated against 12 empirical models.

Main Results:

  • HELIOS-Stack achieved high accuracy with R² values of 0.9884 (training) and 0.9769 (testing).
  • Demonstrated superior performance across metrics like Mean Absolute Error and Concordance Index compared to other models.
  • Identified three distinct roughness clusters: high (JRC 16-20), moderate (JRC 8-15), and smooth (JRC 0.4-7).

Conclusions:

  • The HELIOS-Stack methodology significantly advances rock discontinuity characterization.
  • This approach offers unprecedented precision in JRC prediction for geological applications.
  • Transformed applications in geotechnical engineering, rock mass stability, and geological modeling are anticipated.