Establishing regression and artificial neural network model in predicting the performance of recycled aggregate concrete

Various developing countries are confronted with serious environmental difficulties due to excessive resource utilization and insufficient waste management system. In particular, construction and demolition waste poses a grave threat to the environment, contributing to escalating energy consumption, the depletion of landfill capacities, and the generation of harmful noise and dust pollution. Consequently, the research community is tasked with the daunting challenge of devising effective strategies to incorporate this waste material in producing concrete, without compromising the critical strength and durability characteristics. The investigation aims to attain the aforementioned objective by examining the effects of using recycled aggregates as a distinct partial replacement of 0%, 5%, 10%, 15%, and 20% on the compressive and split tensile strength traits, contingent upon 7 and 28 days of age of curing. Experimental test results show that the optimal concrete production is achieved when 10% of coarse aggregate is replaced with recycled aggregate, maintaining 98% of the materials compressive and split tensile strength. To further validate the obtained experimental data, model equations were derived through regression analysis and the framed model equation is further assessed for accuracy using error analysis. In this study, a MATLAB program was utilized for prediction of compressive and split tensile strength with five distinct network types and the Levenberg-Marquardt algorithm is used for optimization. A comparative analysis was conducted between the regression analysis values and the performance of the ANN modelling. The findings demonstrate that the Artificial Neural Network (ANN) serves as a highly effective model, offering significantly improved accuracy in predicting the optimal correlation between compressive strength and split tensile strength of concrete.