ArticleMaterials (Basel, Switzerland)2025
HELIOS-Stack: A Novel Hybrid Ensemble Learning Approach for Precise Joint Roughness Coefficient Prediction in Rock Discontinuity Analysis.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Advanced Experimental Technology, Theory and Numerical Methods in Geomaterials and Concrete Materials.Materials (Basel, Switzerland) · 2026Article
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Abstract
Accurate joint roughness coefficient (JRC) estimation is crucial for understanding rock mass mechanical behavior, yet existing predictive models show limitations in capturing complex morphological characteristics of geological surfaces. This study developed an advanced hybrid ensemble learning methodology (HELIOS-Stack) to enhance JRC prediction accuracy by integrating multiple machine learning models and statistical analysis techniques. The research implemented a hybrid ensemble approach combining random forest regression, XGBoost, LightGBM, support vector regression, multilayer perceptron models, and meta-learner using LightGBM as the final estimator. The study analyzed 112 rock samples using eight statistical parameters. Model performance was evaluated against 12 empirical regression models using comprehensive statistical metrics. HELIOS-Stack achieved exceptional accuracy with R
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