Using Artificial Intelligent Technique to Predict the Compressive Strength of Cementitious Materials Concrete
The compressive strength (Fc) is considered as the main important property of concrete. To measure this property, intensive laboratory works are needed as well as it takes a lot of effort and time. This study introduces significant idea to estimate concrete compressive strength through applied the Artificial Neural Network (ANN). Moreover, many attends has been done to investigate the properties of concrete including ordinary Portland cement (OPC), fly ash (FA), and silica fume (SF). The ANN trained based on the experimental results, which investigate the influence of FA and SF with different water/binder (w/b) materials ratios on the Fc results of concrete up to 360 days. MATLAB 2019b software was used for developing the ANN model with ten input and one output data set. The predicted results compared with experimental results to validate the established model. The performance of using application of ANN has been determined for all steps of investigation the datasets. Obtained results indicate that’s ANN output performance is complied with the laboratory output via evaluating the concrete compressive-strength. In addition, the efficiency, robustness and reliability of ANN for predicting concrete compressive-strength at varying ages when the FA and SF are used as replacing materials. In addition, they recommend that ANN model is an encouraging tool for determining the concrete Fc of concrete containing cementitious materials.
Keywords: Artiﬁcial Neural Network; MATLAB; Compressive-Strength; Water to Cement Ratio; Cement Type.