| Summary: | Carbonation, a process involving the reaction of carbon dioxide and moisture, results in the for-mation of powdery calcium carbonate, a critical durability issue causing reinforcement corrosion. The study analyzed carbonation data from coastal and inland buildings in the Turkish Republic of Northern Cyprus, re-vealing higher carbonation rates than anticipated within their lifespan. An artificial intelligence model named Support Vector Machine (SVM) was applied to predict carbonation time (T) to penetrate concrete cover of 25mm in the TRNC. Subsequently used two ensemble techniques, namely Neural Network Ensembles (NNE) and Support Vector Machine Ensembles (SVME) to enhance the performance of the prediction of T. Four performance criteria namely Correlation Coefficient (CC), Root Mean Square Error (RMSE), Correlation Co-efficient (R2), Mean Absolute Error (MAE) was applied to verify the modelling accuracy. The Values of R2 of Ensemble techniques indicated significant increase in the performance, greater than the SVM model. This shows that using ensemble techniques is promising in getting better predictions of carbonation time (T) to penetrate concrete cover. The results obtained showed that NNE and SVME combination demonstrated the best performance under the evaluation criteria of R2 = 0.8721 and R2 = 0.8644 in testing phases respectively in comparison SVM-M1 to SVM-M3.
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