Classification of Concrete Compressive Strength Using Machine Learning Methods

The compressive strength of concrete is critical for the design, safety, and durability of structures. While traditional methods used to determine the compressive strength of concrete are costly and time-consuming, the compressive strength of concrete can be determined more quickly, efficiently, and accurately with the use of artificial intelligence-integrated methods. In the presented study, the compressive strength of concrete was classified using a Support Vector Machine, Random Forest, K-Nearest Neighbors, Logistic Regression, and Decision Tree algorithms based on 102 core drilling samples taken from 83 reinforced concrete buildings affected by the earthquake in Kahramanmaras in 2023. The results indicate that Random Forest and Decision Tree algorithms achieved a classification success rate of over 90% in determining the compressive strength of concrete. The high accuracy percentages emphasize the importance of cooperation between computer science and the construction sector.