Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
Samaei M. Massalow T. Abdolhosseinzadeh A. Yagiz S. Sabri M.M.S.
September 2022MDPI
Applied Sciences (Switzerland)
2022#12Issue 18
Due to the different challenges in rock sampling and in measuring their thermal conductivity (TC) in the field and laboratory, the determination of the TC of rocks using non-invasive methods is in demand in engineering projects. The relationship between TC and non-destructive tests has not been well-established. An investigation of the most important variables affecting the TC values for rocks was conducted in this study. Currently, the black-boxed models for TC prediction are being replaced with artificial intelligence-based models, with mathematical equations to fill the gap caused by the lack of a tangible model for future studies and developments. In this regard, two models were developed based on which gene expression programming (GEP) algorithms and non-linear multivariable regressions (NLMR) were utilized. When comparing the performances of the proposed models to that of other previously published models, it was revealed that the GEP and NLMR models were able to produce more accurate predictions than other models were. Moreover, the high value of R-squared (equals 0.95) for the GEP model confirmed its superiority.
gene expression programming (GEP) , geothermal systems , non-linear multivariable regression (NLMR) , P-wave , porosity , thermal conductivity
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Department of Civil Engineering, University of Tabriz, 29 Bahman Boulevard, Tabriz, 5166616471, Iran
Department of Mining Engineering, School of Mining and Geosciences, Nazarbayev University, Nur-Sultan, 01000, Kazakhstan
Peter the Great St. Petersburg Polytechnic University, Petersburg, 195251, Russian Federation
Department of Civil Engineering
Department of Mining Engineering
Peter the Great St. Petersburg Polytechnic University
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