Probabilistic Calibration of Stress-Strain Models for Confined Normal-Strength Concrete
Publication: Journal of Structural Engineering
Volume 147, Issue 8
Abstract
A probabilistic calibration for traditional deterministic stress-strain models of square confined concrete columns was conducted based on the proposed probabilistic stress-strain model and a large number of experimental data. The probabilistic models for both peak stress and peak strain (strain corresponding to peak stress) of confined normal-strength concrete (NSC) were established first based on the Bayesian theory and the Markov chain Monte Carlo method. Then, a probabilistic stress-strain model of confined NSC was established based on the proposed probabilistic models for peak stress and peak strain. Finally, the confidence level and computational accuracy of four typical deterministic stress-train models of confined NSC were calibrated based on the proposed probabilistic models and a large amount of experimental data. The proposed probabilistic models not only describe the probabilistic characteristics of peak stress, peak strain, and the stress-strain curve, but also provide an efficient approach to calibrate the confidence level and computational accuracy of traditional deterministic 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.
Acknowledgments
The financial support received from the National Natural Science Foundation of China (Grant Nos. 51668008 and 51738004), the Guangxi Science Fund for Distinguished Young Scholars (2019GXNSFFA245004), and the Natural Science Foundation of Guangxi Province (Grant No. 2018GXNSFAA281344) is gratefully acknowledged.
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Published In
Journal of Structural Engineering
Volume 147 • Issue 8 • August 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jul 23, 2020
Accepted: Apr 7, 2021
Published online: Jun 11, 2021
Published in print: Aug 1, 2021
Discussion open until: Nov 11, 2021
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