What question did this study set out to answer?

The aim is to reliably estimate unconfined compressive strength and Young’s modulus in clay-rich rock formations for geotechnical applications.

June 8, 2026Scientific Reports0 citationsOpen Access

Explainable ensemble learning for estimating strength and stiffness of clay-bearing rocks: A case study from the Gurpi formation, Iran

SWSuhaib Rasool WaniChandigarh University KWKenue Abdul WarisIndian Institute of Technology Hyderabad SHSaeedeh HosseiniPayame Noor University

Key Points

The aim is to reliably estimate unconfined compressive strength and Young’s modulus in clay-rich rock formations for geotechnical applications.
Developed an integrated experimental–machine learning framework using non-destructive indices.
Trained four ensemble algorithms: LightGBM, AdaBoost, CatBoost, and Random Forest.
Utilized explainable AI tools to interpret model predictions and identify key predictors.
AdaBoost achieved the highest UCS prediction performance (test R² = 0.90, RMSE = 0.70 MPa).
CatBoost provided the most accurate Young’s modulus estimates (test R² = 0.82, RMSE = 0.22 GPa).
Identified seismic velocities and dry density as positive predictors, while porosity and water absorption negatively influenced estimates.

Abstract

Reliable estimation of unconfined compressive strength (UCS) and Young’s modulus (E) is critical for geotechnical design in weak, clay-rich formations. This study develops an integrated experimental–machine learning (ML) framework to predict UCS and E for marl and shale from the Godarkhosh Dam site (Iran). Four ensemble ML algorithms (LightGBM, AdaBoost, CatBoost, Random Forest) are trained using non-destructive indices (density, porosity, water absorption, and P-/S-wave velocities). AdaBoost achieves the highest UCS prediction performance (test R² = 0.90, RMSE = 0.70 MPa), while CatBoost provides the most accurate E estimates (test R² = 0.82, RMSE = 0.22 GPa). Explainable AI (SHAP, LIME, partial dependence plots) consistently identifies seismic velocities and dry density as dominant positive predictors, and porosity and water absorption as negative influences. Monotonic, physically coherent trends across all XAI tools confirm that the models capture meaningful geomechanical relationships rather than statistical artefacts. The proposed ML–XAI workflow enables accurate, interpretable estimation of mechanical properties in soft rock terrains using a minimal set of rapid, non-destructive measurements, supporting efficient site characterization for dam foundations and similar weak sedimentary formations.

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