Prediction of the Coefficient of Thermal Expansion and Other Characteristics of Concrete by Using Machine Learning

Predicting concrete's thermal expansion coefficient (CTE) is important for improving its performance in different environmental conditions.Traditional methods often fail to capture the complex relationships between concrete composition and thermal properties.Machine learning offers a better way to solve this problem by using data-driven models.This study used 192 datasets that included information on various concrete mixtures and their properties.Both simple and advanced machine learning models were tested to predict CTE.Simple methods, like Multiple Linear Regression (MLR) and Support Vector Regression (SVR), provided basic predictions.Advanced methods, such as AdaBoost, XGBoost, Bagging, and Random Forest, were used to handle the complex and non-linear nature of the data.These models were evaluated using methods like cross-validation and feature importance analysis to check their accuracy and reliability.When comparing the results, the study found that simpler models like MLR could only give limited insights.In contrast, advanced models, especially Random Forest, performed much better.The Random Forest model was highly accurate, identifying key factors affecting CTE and correctly predicting patterns in the data.It also reduced the number of experiments needed for accurate predictions, saving time and resources.This research shows how machine learning can be used to better predict and understand concrete properties.It highlights Random Forest as a powerful tool for accurately modeling CTE and other mechanical properties of concrete while reducing costs and improving efficiency