Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression

Abstract

Currently, one of the topical areas of application of machine learning methods in the construction industry is the prediction of the mechanical properties of various building materials. In the future, algorithms with elements of artificial intelligence form the basis of systems for predicting the operational properties of products, structures, buildings and facilities, depending on the characteristics of the initial components and process parameters. Concrete production can be improved using artificial intelligence methods, in particular, the development, training and application of special algorithms to determine the characteristics of the resulting concrete. The aim of the study was to develop and compare three machine learning algorithms based on CatBoost gradient boosting, k-nearest neighbors and support vector regression to predict the compressive strength of concrete using our accumulated empirical database, and ultimately to improve the production processes in construction industry. It has been established that artificial intelligence methods can be applied to determine the compressive strength of self-compacting concrete. Of the three machine learning algorithms, the smallest errors and the highest coefficient of determination were observed in the KNN algorithm: MAE was 1.97; MSE, 6.85; RMSE, 2.62; MAPE, 6.15; and the coefficient of determination R2, 0.99. The developed models showed an average absolute percentage error in the range 6.15−7.89% and can be successfully implemented in the production process and quality control of building materials, since they do not require serious computing resources.

Keywords

• artificial intelligence
• machine learning
• regression
• CatBoost
• k-nearest neighbors
• support vector regression