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This study aims to accurately estimate Young's modulus of various rock types using advanced modeling techniques.

February 5, 2026AI in Civil EngineeringOpen Access

Prediction of Young’s modulus of rock materials by multivariate regression analysis and neuro-fuzzy model

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Multivariate regression and neuro-fuzzy models predict Young’s modulus, suggesting more accurate assessments in rock engineering.

This study aims to accurately estimate Young's modulus of various rock types using advanced modeling techniques.
Compiled a large database covering multiple rock types and parameters such as Schmidt rebound value, uniaxial compressive strength, and porosity.
Employed multivariate regression analysis and adaptive-neuro-fuzzy inference system (ANFIS) for predictions.
Validated the derived models using statistical tests like F- and t-tests for reliability.
Compared prediction performance of both models using RMSE and MAPE.
Neuro-fuzzy models showed significantly lower prediction errors compared to multivariate regression.
ANFIS yielded the highest prediction accuracy for Young's modulus when using input parameters like uniaxial compressive strength, Schmidt rebound value, and porosity.
The study confirms that neuro-fuzzy approaches outperform traditional multivariate methods in rock material predictions.

Hasan Karakul (2026) studied this question.

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