Intelligent prediction of the frost resistance of high-performance concrete: a machine learning method
Abstract
Frost resistance in very cold areas is an important engineering issue for the durability of concrete, and the efficient and accurate prediction of the frost resistance of concrete is a crucial basis for determining reasonable design mix proportions. For a quick and accurate prediction of the frost resistance of concrete, a Bayesian optimization (BO)-random forest (RF) approach was used to establish a frost resistance prediction model that consists of three phases. A case study of a key national engineering project results show that (1) the RF can be used to effectively screen the factors that influence concrete frost resistance. (2) R2 of BO-RF for the training set and the test set are 0.967 and 0.959, respectively, which are better than those of the other algorithms. (3) Using the test data from the first section of the project for prediction, good results are obtained for the second section. The proposed BO-RF hybrid algorithm can accurately and quickly predict the frost resistance of concrete, and provide a reference basis for intelligent prediction of concrete durability.
Keyword : frost resistance, durability of concrete, random forest, Bayesian optimization, mix proportion
How to Cite
Zhang, J., Cao, Y., Xia, L., Zhang, D., Xu, W., & Liu, Y. (2023). Intelligent prediction of the frost resistance of high-performance concrete: a machine learning method. Journal of Civil Engineering and Management, 29(6), 516–529. https://doi.org/10.3846/jcem.2023.19226
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Gao, Y., Jing, H. W., Zhao, Z. L., Shi, X. S., & Li, L. (2021). Influence of ultrasonication energy on reinforcing-roles of CNTs to strengthen ITZ and corresponding anti-permeability properties of concrete. Construction and Building Materials, 303, 124451. https://doi.org/10.1016/j.conbuildmat.2021.124451
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