Predicting the Strength of Heavy Concrete Exposed to Aggressive Environmental Influences by Machine Learning Methods

Currently, intelligent algorithms are becoming a reliable alternative source of data analysis in many areas of human activity. In materials science, the integration of machine learning methods is effectively applied to predictive modeling of building materials properties. This is particularly interesting and relevant for predicting the strength properties of building materials under aggressive environmental conditions. In this study, machine learning methods (Linear Regression, K-Neighbors, Decision Tree, Random Forest, CatBoost, Support Vector Regression, and Multilayer Perceptron) were used to analyze the relationship between the strength properties of heavy concrete depending on the freeze–thaw cycle, the average area of damaged areas during this cycle, and the number of damaged areas. The Random Forest and CatBoost methods demonstrate the smallest errors: deviations from actual values are 0.27 MPa and 0.25 MPa, respectively, with an average absolute percentage error of less than 1%. The determination coefficient R2 for both models is greater than 0.99. High values of this statistical measure indicate that the implemented models adequately describe changes in the observed data. The theoretical and practical development of intelligent algorithms in materials science opens up vast opportunities for the development and production of materials that are more resistant to aggressive influences.