Abstract
Artificial intelligence (AI) and statistical methods are used in various fields and have played a vital role in investigating the deflection and strain of reinforced green concrete beams made with partial replacement of recycled concrete aggregates under sustained loading. The methods used to assess structural contributors are time-saving and cost-effective compared to experimental evaluation. This study investigated the numerical modeling of reinforced concrete beams produced by replacing 50% of coarse natural aggregates with demolished vintage concrete under sustained loading. Multivariate regression analysis was used to determine the mathematical equations for long-term deflection and stress from experimental data of 6, 9, and 12 months of loading. Three software suites were used for the regression analysis, namely NCSS, Matlab, and Microsoft Excel. Six beams were cast using demolished concrete as 50% of coarse aggregates to test and validate the regression equations, where three of them were examined for two months of sustained loading and the other three for three months. The regression results were in accordance with the experimental observations with a maximum error of 10.34%. Therefore, the provided regression equations for deflection and pressure could be used to estimate the parameters of reinforced concrete beams.
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