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.

Microstructural evolution and the intermetallic compound (IMC) growth kinetics in an Au stud bump were studied via isothermal aging at 120, 150, and 180˚C for 300hrs. The AlAu4 phase was observed in an Al pad/Au stud interface, and its thickness was kept constant during the aging treatment. AuSn, AuSn2, and AuSn4 phases formed at interface between the Au stud and Sn. AuSn2, AuSn2/AuSn4, and AuSn phases dominantly grew as the aging time increased at 120˚C, 150˚C, and 180˚C, respectively, while (Au,Cu)6Sn5/Cu3Sn phases formed at Sn/Cu interface with a negligible growth rate. Kirkendall voids formed at AlAu4/Au, Au/Au-Sn IMC, and Cu3Sn/Cu interfaces and propagated continuously as the time increased. The apparent activation energy for the overall growth of the Au-Sn IMC was estimated to be 1.04 eV.