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Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP)
Authors:Murat Karakus
Affiliation:School of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA 5005, Australia
Abstract:Symbolic Regression (SR) analysis, employing a genetic programming (GP) approach, was used to analyse laboratory strength and elasticity modulus data for some granitic rocks from selected regions in Turkey. Total porosity (n), sonic velocity (vp), point load index (Is) and Schmidt Hammer values (SH) for test specimens were used to develop relations between these index tests and uniaxial compressive strength (σc), tensile strength (σt) and elasticity modulus (E). Three GP models were developed. Each GP model was run more than 50 times to optimise the GP functions. Results from the GP functions were compared with the measured data set and it was found that simple functions may not be adequate in explaining strength relations with index properties. The results also indicated that GP is a potential tool for identifying the key and optimal variables (terminals) for building functions for predicting the elasticity modulus and the strength of granitic rocks.
Keywords:Genetic programming (GP)  Symbolic regression (SR)  Elasticity modulus  Compressive strength  Tensile strength  Granitic rocks
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