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Probabilistic model for ultimate displacement of reinforced concrete columns with flexural failure.

  • Published In: Structural Concrete, 2025, v. 26, n. 4. P. 4551 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Haoyu; Zhang, Pengfei; Li, Shaonan; Yu, Bo 3 of 3

Abstract

In order to describe the probabilistic characteristics of the ultimate displacement, a probabilistic model for ultimate displacement of reinforced concrete columns with flexural failure was proposed based on the Bayesian theory and the Markov Chain Monte Carlo (MCMC) method. An analytical model for probabilistic ultimate displacement was established first according to the plane cross‐section assumption and the equivalent plastic hinge theory. Then probabilistic model parameters were determined based on the Bayesian theory and the MCMC method. Finally, the proposed model was verified by comparison with 252 sets of experimental data and existing ultimate displacement models. Comparisons show that the proposed model not only provides reasonable prediction accuracy but also describes the probability distribution of ultimate displacement realistically. Furthermore, the proposed model is useful for the validation of the accuracy and applicability of available deterministic ultimate displacement models, which provide probabilistic validation methods based on the confidence interval and probability density function. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Structural Concrete. 2025/08, Vol. 26, Issue 4, p4551
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:1464-4177
  • DOI:10.1002/suco.202400763
  • Accession Number:187572527
  • Copyright Statement:Copyright of Structural Concrete is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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