Artificial intelligence in materials science and modern concrete technologies: analysis of possibilities and prospects

An analysis of current trends and opportunities for the application of artificial intelligence (AI) in materials science and concrete technology, including 3D printing in construction, is presented. The key role of AI in predicting material properties, developing new materials, and quality control is highlighted. By analyzing large volumes of data collected from numerous studies, AI can suggest optimal parameters to achieve desired material properties, thereby reducing costs and increasing production efficiency. Existing rheological models, such as the Bingham-Shvedov model or the Herschel-Bulkley model, describe material behavior based on specific equations and parameters. These models can be useful in predicting concrete properties, especially when data on its component composition is available. However, these models may be limited in their predictive accuracy, particularly for non-standard or novel materials. It has been found that machine learning and neural networks have the potential to provide accurate predictions of rheological and physico-mechanical properties of concrete materials, considering multiple parameters that influence material characteristics, including chemical and mineralogical composition, as well as structural features. The combination of experimental data and AI can successfully optimize compositions and properties during production, reducing costs and research/testing time, and opening new opportunities for researchers and engineers in the field of materials science. Machine learning algorithms such as XGBoost, LightGBM, Catboost, and NGBoost demonstrate high predictive accuracy and have become powerful tools in the design of concrete compositions and innovative technologies. The analysis of Shapley additive explanations (SHAP) allows us to understand which parameters of a concrete mixture have the greatest influence on its characteristics.