Design of concrete mixtures and prediction of their compressive strength using machine learning

The use of machine learning and neural networks in predicting the compressive strength of concrete promises to significantly improve the accuracy and reliability of models for the design and optimization of concrete mixtures. With rapid advances in this field, computational models will be able to handle even larger amounts of experimental data, increasing their ability to capture the complex relationships between input parameters and the mechanical properties of concrete. With the development of new neural network architectures and machine learning algorithms, it will be possible to create highly adaptive predictive models that can better respond to variability in concrete composition and production conditions, leading to more efficient and sustainable design in the construction industry. The submitted paper deals with the design of concrete mixtures and prediction of their compressive strength based on the compressive strength results of mixtures of known composition from other experiments using machine learning. Practical validation of the developed regression model will be carried out by testing the machine-designed mixtures for compressive strength after 28 days.