A framework for predicting the carbonation depth of concrete incorporating fly ash based on a least squares support vector machine and metaheuristic algorithms. (15th April 2023)

• Record Type: Journal Article
• Authors: Zhang, Kai; Zhang, Ke; Bao, Rui; Liu, Xianghua
• Is Part Of: Journal of building engineering. Volume 65(2023)
• Publication Date: 2023-04-15
• Subjects: Carbonation depth of concrete -- Least squares support vector machine (LSSVM) -- Metaheuristic algorithms -- Global sensitivity analysis (GSA) -- Optimal division ratio; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.105772
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: The carbonation of concrete structures is becoming a more serious problem as the global CO2 level increases, and poses a great challenge to concrete durability research. In this study, a hybrid prediction framework on the basis of the least squares support vector machine (LSSVM) and metaheuristic algorithms is proposed to accurately predict the carbonation depth of concrete incorporating fly ash. The fly ash replacement level, cement content, water-to-binder ratio, CO 2 content, relative humidity of environmental and exposure time are treated as input variables of the framework. A database containing 500 sets of samples is created for model building and comparison. The results show that five hybrid models have superior accuracy in the prediction of concrete carbonation depth, where the R 2, RMSE, MAE and VAF range from 0.9683 to 0.9819, 1.7309–2.2936 mm, 1.2255–1.5478 mm, and 96.8317 to 98.1995, respectively. In addition, the proposed prediction framework has better performance (i.e., smaller RMSE ) compared to reported empirical prediction models. The Sobol' global sensitivity index analysis results indicate that the water-to-binder ratio, exposure time and cement content have higher sensitivity to predict the carbonation depth. The effect of dataset division ratio on the models (an aspect that has not been investigated in previous research) is further explored in this study, and the optimal dataset division ratio for each model is determined. Highlights: A new framework for predicting the carbonation depth is developed. Hybrid ML-based carbonation models have superior prediction power. The model with the best prediction performance for carbonation depth is GWO-LSSVM. The global sensitivities of the input factors to the model are obtained. The optimal dataset division ratio of the model is acquired.