Predictive modeling of high-performance concrete with regression analysis

High-performance concrete (HPC) is a very complex material and hence very hard to predict its compressive strength. This paper deals with building a regression model for predicting concrete's compressive strength. First of all, eight process variables are identified as determinants of Concrete Compressive Strength (CCS). These variables are Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, Fine Aggregate, and Age. Further, correlation among these variables is computed and it is found that a few of them are highly correlated. Therefore, interactions among these variables are taken into account. After that, a regression model is developed by regressing CCS against all process variables and significant interactions. Finally, diagnostics are conducted to fine tune the model and a parsimonious model is obtained with 84.37% coefficient of determination. Appropriateness of the model is investigated by testing it against unseen data points.