Statistical Analysis of Slump Flow Using Gene Expression Programming (GEP) for Self-Consolidated Concrete

Abstract

Statistical analysis of the slump flow prediction by the application of Gene Expression Programming on the data points of 953 related to Self-Consolidated Concrete. SCC is the acronym for Self-Consolidated Concrete, high-flow concrete, supposed to self-consolidate. This type of concrete really demonstrates its superb applicability especially in the dense and complex structures with reinforcement. Several design variables, namely the water-to-cement ratio, aggregate properties, and admixtures affect this characteristic: therefore, making it rather difficult to predict precisely its slump flow behavior. GEP was applied to analyze a dataset obtained from a sequence of experiments and yielded a predictive model for slump flow. Descriptive analysis tools, regression techniques, as well as error metrics, MSE, RMSE, and R², have been used to test the robustness and reliability of the model. The results have also indicated that, based solely on the mixed design parameters, it is possible to predict slump flow by GEP. Significant relationships have also been found between values of slump flow and input factors.