Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153720
Title: Probabilistic calibration of stress-strain models for confined high-strength concrete
Authors: Yu, Bo
Qin, Chenghui
Chen, Zheng
Li, Bing
Keywords: Engineering::Civil engineering
Issue Date: 2021
Source: Yu, B., Qin, C., Chen, Z. & Li, B. (2021). Probabilistic calibration of stress-strain models for confined high-strength concrete. ACI Structural Journal, 118(5), 161-175. https://dx.doi.org/10.14359/51732826
Journal: ACI Structural Journal
Abstract: A comprehensive probabilistic calibration of traditional deterministic models for peak stress, peak strain, and stress-strain curves of confined high-strength concrete (HSC) was investigated. The probabilistic models for peak stress and peak strain of confined HSC were first established by combining the Markov chain Monte Carlo (MCMC) method with the Bayesian theory. A probabilistic stress-strain model of confined HSC was then proposed to provide a probabilistic approach to calibrate the confidence level and computational accuracy of four typical deterministic stress-strain models of confined HSC. Analysis results show that the randomness of the stress-strain curve in the ascending branch is not obvious, but that in the descending branch after peak stress is significant. Deterministic stress-strain models can better predict tested stress-strain curves in ascending branches with a greater confidence level than descending branches. The tested stress-strain curves generally fall within the 50% confidence interval of the probabilistic stress-strain model, which implies that the proposed probabilistic stress-strain models can adequately describe the probabilistic characteristic of stress-strain curves of confined HSC.
URI: https://hdl.handle.net/10356/153720
ISSN: 0889-3241
DOI: 10.14359/51732826
Rights: © 2021 American Concrete Institute. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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