Abstract
To minimize the energy consumption and adverse impact of excessive waste accumulation on the environment, coconut shell (CA) became a potential (partial) replacement agent for fine aggregates in structural concrete production. Thus, systematic experimental and theoretical studies are essential to determine the thermal and structural properties of such concrete containing optimum level of CA. In this view, an artificial neural network (ANN) model, gene expression programming (GEP) model, and response surface method (RS) were used to predict and optimize the desired engineering characteristics of some concrete mixes designed with various levels of CA inclusion. Furthermore, the proposed model's performance was assessed in terms of different statistical parameters calculated using ANOVA. The results revealed that the proposed concrete mix made using 53% of CA as a partial replacement of fine aggregate achieved an optimum density of 2246 kg/m(3) and thermal conductivity of 0.5952 W/mK, which was lower than the control specimen (0.79 W/mK). The p-value of the optimum concrete mix was less than 0.0001 and the F-value was over 147.47, indicating the significance of all models. It is asserted that ANN, GEP, and RSM are accurate and reliable, and can further be used to predict a strong structural-thermal correlation with minimal error. In brief, the specimen composed with 53% of CA as a replacement for fine aggregate may be beneficial to develop environmentally amiable green structural concrete.