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

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 R2 values of 0.9884 (training) and 0.9769 (testing), significantly outperforming traditional empirical models and alternative machine learning models. Also, the HELIOS-Stack statistical evaluation demonstrated superior performance across multiple metrics, including mean absolute error (training: 1.0165, testing: 1.4097) and concordance index (training: 0.99, testing: 0.987). The analysis identified three distinct roughness clusters: high (JRC 16–20), moderate (JRC 8–15), and smooth (JRC 0.4–7). The HELIOS-Stack methodology significantly advances rock discontinuity characterization, establishing a new benchmark for geological surface analysis. This innovative approach offers transformative applications in geotechnical engineering, rock mass stability assessment, and geological modeling through its unprecedented precision in JRC prediction.