Prediction of the Sulfate Attack Resistance of Concrete Based on Machine-Learning Algorithms
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 6
Abstract
The thorough investigation into the evolution of concrete performance under sulfate attack environments holds significant importance for engineering applications in specific conditions. In this paper, a prediction model for the two evaluation indexes of sulfate attack resistance of concrete (SARC), namely compressive strength corrosion resistance coefficient and mass loss rate, is established based on four machine-learning algorithms: Support Vector Regression, Random Forest Regression, Gradient Boosting, and Extreme Gradient Boosting (XGB). A comparison of the various performances showed that the model based on the XGB algorithm had the strongest generalization ability and offered the best prediction of SARC (K test set , MLR test set ). Feature importance and partial correlation analyses were performed for the two XGB models separately, and a graphical user interface was designed based on the two predictive models. The results reveal that the number of cycles, water-binder ratio, and cement content significantly influence the SARC. Moderately increasing cement, fly ash, and coarse aggregate content can enhance the SARC. Increasing the number of cycles, drying time, water-binder ratio, sand, and solution concentration will reduce the SARC. Therefore, measures such as moderately increasing the amount of cement, reducing the water-binder ratio, and increasing the fly ash content can be increased to improve the SARC, but overuse has no significant effect.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to acknowledge the financial support of the National Natural Science Foundation of China-China National Railway Group Co., Ltd. Railway Basic Research Joint Fund Project (U2368207) and the Natural Science Foundation of Beijing, China (24JL003).
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© 2024 American Society of Civil Engineers.
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Received: Mar 22, 2024
Accepted: Jun 24, 2024
Published online: Sep 14, 2024
Published in print: Nov 1, 2024
Discussion open until: Feb 14, 2025
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