JOURNAL OF ROCK MECHANICS

JOURNAL OF ROCK MECHANICS

Predicting the Abrasive Resistance of Building Stones Using Regression Models and Random Forest Algorithm

Document Type : Original Article

Authors
1 PhD Candidate of Isfahan University of Technology, Isfahan, Iran.
2 Associate Professor of Mining Engineering, Isfahan University of Technology, Isfahan, Iran.
Abstract
This study aims to predict the Böhme abrasion value (BAV) as a measure of the abrasive resistance of building stones using regression models and machine learning algorithms. A total of 160 datasets, including physical and mechanical parameters of stones such as porosity (N), water absorption (Wa), and Shore hardness value (SHV), were collected and analyzed. The results indicated that all three parameters significantly impact abrasive resistance: increased porosity and water absorption lead to lower abrasive resistance, while higher Schmidt hardness results in greater abrasive resistance. Multivariate regression models and the Random Forest algorithm were used to predict BAV with high accuracy, particularly the Random Forest model, which achieved superior performance with higher precision indicators, including a normalized root mean square error (NRMSE) of 0.110 and a variance account for (VAF) of 0.839. These models not only reduce the need for costly and time-consuming physical tests but also support designers and engineers in making more informed decisions when selecting suitable building materials for high-traffic and harsh environmental conditions
Keywords
Subjects

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