Predicting the Rheological Performance of Self-Compacting Mortar and Concrete Using Artificial Neural Network

Self-compacting mortar and concrete are high-performance building materials used in the construction industry because of their excellent rheological and mechanical properties. However, the absence of specific standards for mix design presents hindrance for researchers, motivating this study. A prediction model was developed in this study to assess the suitability of mix designs to produce robust and stable SCC with desired viscosity and yield stress characteristics. Utilizing artificial neural network technique, a powerful machine learning tool for solving complex nonlinear problems, bibliographic and experimental data on composition proportions and material properties were collected. The model architecture was optimized through multiparametric analysis, testing around 22,000 models to achieve approximately 85% prediction accuracy. The particle size distribution of fine aggregates, along with the content and specific surface area of fine filler materials, emerged as the most significant predictive variables. This model could serve as a reliable tool for researchers and industries to design self-compacting mixtures, conserving laboratory time, as well as financial and natural resources.