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This study comprehensively investigates the rheological properties of self-compacting concrete (SCC) and their impact on critical parameters, including the migration coefficient, penetration depth of chlorine ions, specific electrical resistance, and compressive strength. A total of 43 mix designs were meticulously examined to explore the relationships between these properties. Quantitative analysis employed a backpropagation neural network model with a single hidden layer to accurately predict the resistant and durable characteristics of self-compacting concrete. The optimal number of neurons in the hidden layer was determined using a fitting component selection method, implemented in MATLAB software(2021b). Additionally, qualitative analysis was conducted using sensitivity analysis and expert opinions to determine the priority of research additives. The main contributions of this paper lie in the exploration of SCC properties, the utilization of a neural network model for accurate prediction, and the prioritization of research additives through sensitivity analysis. The neural network model demonstrated exceptional performance in predicting test results, achieving a high accuracy rate using 14 neurons for predicting parameters such as chlorine penetration depth, compressive strength, migration coefficient, and specific electrical resistance. Sensitivity analysis revealed that xanthan gum emerged as the most influential additive, accounting for 43% of the observed effects, followed by nanomaterials at 35% and micro-silica at 21%.
Masoumi et al. (Mon,) studied this question.