Abstract
Thermal conductivity is an important property of rocks which is considered for energy-efficient building construction. This paper is aimed to predict the thermal conductivity of rocks utilizing the adaptive neuro-fuzzy inference system (ANFIS) and multivariate regression (MVR) analysis. In this regard, 44 datasets including the most effective parameters on thermal conductivity of rocks were collected from the literature. The physico-mechanical properties of rocks including uniaxial compressive strength, P-wave velocity, bulk density and porosity were used to develop the predictive models. The correlation of determination equal to 0.99 and 0.95 were obtained by ANFIS and MVR models respectively. The obtained results suggest that the ANFIS model outperforms the MVR model and is an applicable tool to predict thermal conductivity of rocks with high degree of accuracy.
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