Kadkhodaei, M., Ghasemi, E. (2019). Development of a GEP model to assess CERCHAR abrasivity index of rocks based on geomechanical properties. Journal of Mining and Environment, 10(4), 917-928. doi: 10.22044/jme.2019.8141.1684
M. H. Kadkhodaei; E. Ghasemi. "Development of a GEP model to assess CERCHAR abrasivity index of rocks based on geomechanical properties". Journal of Mining and Environment, 10, 4, 2019, 917-928. doi: 10.22044/jme.2019.8141.1684
Kadkhodaei, M., Ghasemi, E. (2019). 'Development of a GEP model to assess CERCHAR abrasivity index of rocks based on geomechanical properties', Journal of Mining and Environment, 10(4), pp. 917-928. doi: 10.22044/jme.2019.8141.1684
Kadkhodaei, M., Ghasemi, E. Development of a GEP model to assess CERCHAR abrasivity index of rocks based on geomechanical properties. Journal of Mining and Environment, 2019; 10(4): 917-928. doi: 10.22044/jme.2019.8141.1684
Development of a GEP model to assess CERCHAR abrasivity index of rocks based on geomechanical properties
Department of Mining Engineering, Isfahan University of Technology, Isfahan, Iran
Abstract
The CERCHAR abrasivity test is very popular for determination of rock abrasivity. An accurate estimation of the CERCHAR abrasivity index (CAI) is useful for excavation operation costs. This paper presents a model to calculate CAI based on the gene expression programming (GEP) approach. This model is trained and tested based on a database collected from the experimental results available in the literature. The proposed GEP model predicts CAI based on two basic geomechanical properties of rocks, i.e. rock abrasivity index (RAI) and Brazilian tensile strength (BTS). Root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and coefficient of determination (R2) are used to measure the model performance. Furthermore, the developed GEP model is compared with linear and non-linear multiple regression and other existing models in the literature. The results obtained show that GEP is a strong technique for the prediction of CAI.
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