Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms—specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms—to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

Carbonation is one of the most critical deterioration phenomena to concrete structures exposed to high CO2 concentration, sheltered from rain. Lots of researches have been performed on evaluation of carbonation depth and changes in hydrate compositions, however carbonation modeling is limitedly carried out due to complicated carbonic reaction and diffusion coefficient. This study presents a simplified carbonation model considering diffusion coefficient, solubility of Ca(OH)2, porosity reduction, and carbonic reaction rate for low concentration. For verification, accelerated carbonation test with varying temperature and MIP (Mercury Intrusion Porosimetry) test are carried out, and carbonation depths are compared with those from the previous and the proposed model. Field data with low CO2 concentration is compared with those from the proposed model. The proposed model shows very reasonable results like carbonation depth and consuming Ca(OH)2 through reduced diffusion coefficient and porosity compared with the previous model.