Modeling and Sensitivity Analysis of Concrete Creep with Machine Learning Methods
Publication: Journal of Materials in Civil Engineering
Volume 33, Issue 8
Abstract
Although machine learning algorithms to predict the mechanical properties of concrete have been studied extensively, most of the research focused on the prediction of the strength of concrete and only a few studies have focused on concrete creep. This paper analyzed the maximum information correlation (MIC) between concrete creep influence parameters based on the updated Infrastructure Technology Institute of Northwestern University (NU-ITI) database, and the parameters in the database were adopted in the classical creep prediction models for calculation. Three machine learning algorithms (MLAs)—back-propagation artificial neural network (BPANN), support vector regression (SVR), and extreme learning machine (ELM)—were trained with the NU-ITI database to model concrete creep. The SVR-based model achieved high predictive accuracy. Sensitivity analysis of the parameters and feature selection of concrete creep were carried out based on the SVR and the Sobol method. By retraining the SVR model after feature selection, it was demonstrated that low-sensitivity and strongly correlated parameters will increase the robustness of machine learning models.
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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 (testing data, codes for implemented models, and evaluation metrics).
Acknowledgments
The authors acknowledge the financial support from the National Natural Science Foundation (NSFC) of PR China (No. 51778044).
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Journal of Materials in Civil Engineering
Volume 33 • Issue 8 • August 2021
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© 2021 American Society of Civil Engineers.
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Received: Sep 22, 2020
Accepted: Jan 13, 2021
Published online: Jun 11, 2021
Published in print: Aug 1, 2021
Discussion open until: Nov 11, 2021
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