Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model - M., Sivaraja; A. N., Swaminathen; M. S., Kuttimarks; J., Rajprasad; M., Sakthivel; J., Rex (Journal of Environmental Engineering and Landscape Management, 2025)
Concrete carbonation is a critical factor influencing the durability and longevity of concrete structures, particularly in urban environments. Traditional methods for predicting the anti-carbonation performance (ACP) of concrete often lack precision and fail to account for complex interactions between influencing factors. In this study, a novel hybrid model combining random forest (RF) regression with a least squares support vector machine (LSSVM) is proposed to enhance the accuracy of ACP predictions. The RF regression is utilized for feature selection, identifying the most significant factors affecting ACP and optimizing the input features for the LSSVM model. Our hybrid model is validated against a comprehensive dataset, demonstrating superior performance in predicting concrete carbonation resistance compared to conventional methods. Quantitative results show that the RF-LSSVM model achieves a root mean square error (RMSE) of 5e–5 and a high coefficient of determination (R-squared) of 0.999, indicating robust predictive capability and accuracy. The main novelty of this work lies in the integration of RF and LSSVM to create a robust, accurate, and practical tool for assessing the durability of concrete structures.