Modeling of compressive strength of HPC mixes using a combined algorithm of genetic programming and orthogonal least squares

In this study, a hybrid search algorithm combining genetic programming with orthogonal least squares (GP/OLS) is utilized to generate prediction models for compressive strength of high performance concrete (HPC) mixes. The GP/OLS models are developed based on a comprehensive database containing 1133 experimental test results obtained from previously published papers. A multiple least squares regression (LSR) analysis is performed to benchmark the GP/OLS models. A subsequent parametric study is carried out to verify the validity of the models. The results indicate that the proposed models are effectively capable of evaluating the compressive strength of HPC mixes. The derived formulas are very simple, straightforward and provide an analysis tool accessible to practicing engineers.