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Probabilistic Prediction Model for the Chloride Diffusion Coefficient of Concrete under Tensile and Compressive Stresses
https://orcid.org/https://orcid.org/0000-0002-2244-4644
https://orcid.org/https://orcid.org/0000-0001-9972-4014
To accurately describe the uncertainty of chloride diffusion coefficient of concrete under tensile and compressive stresses, an iterative algorithm is proposed to reduce the influence of the uncertainty of prior variance on the posterior results. Subsequently, a probabilistic prediction model is developed to take into account the stress effect on chloride diffusion coefficient by using a Bayesian algorithm and Markov Chain Monte Carlo (MCMC) method. The existing deterministic model is adopted as prior model for this probabilistic model, and 180 sets of chloride diffusion coefficient data under different stresses obtained from 23 published journal papers are used as posterior information for this probabilistic model. The accuracy of the proposed model is validated by comparing with the experimental samples and other existing models. Analysis results show that the proposed probabilistic prediction model provided a reasonable confidence interval of the correction coefficient of stress effect on chloride diffusion coefficient. The provided iterative algorithm reduce the length of confidence interval with the exact prediction precision.
Probabilistic Prediction Model for the Chloride Diffusion Coefficient of Concrete under Tensile and Compressive Stresses
https://orcid.org/https://orcid.org/0000-0002-2244-4644
https://orcid.org/https://orcid.org/0000-0001-9972-4014
To accurately describe the uncertainty of chloride diffusion coefficient of concrete under tensile and compressive stresses, an iterative algorithm is proposed to reduce the influence of the uncertainty of prior variance on the posterior results. Subsequently, a probabilistic prediction model is developed to take into account the stress effect on chloride diffusion coefficient by using a Bayesian algorithm and Markov Chain Monte Carlo (MCMC) method. The existing deterministic model is adopted as prior model for this probabilistic model, and 180 sets of chloride diffusion coefficient data under different stresses obtained from 23 published journal papers are used as posterior information for this probabilistic model. The accuracy of the proposed model is validated by comparing with the experimental samples and other existing models. Analysis results show that the proposed probabilistic prediction model provided a reasonable confidence interval of the correction coefficient of stress effect on chloride diffusion coefficient. The provided iterative algorithm reduce the length of confidence interval with the exact prediction precision.
Probabilistic Prediction Model for the Chloride Diffusion Coefficient of Concrete under Tensile and Compressive Stresses
https://orcid.org/https://orcid.org/0000-0002-2244-4644
https://orcid.org/https://orcid.org/0000-0001-9972-4014
To accurately describe the uncertainty of chloride diffusion coefficient of concrete under tensile and compressive stresses, an iterative algorithm is proposed to reduce the influence of the uncertainty of prior variance on the posterior results. Subsequently, a probabilistic prediction model is developed to take into account the stress effect on chloride diffusion coefficient by using a Bayesian algorithm and Markov Chain Monte Carlo (MCMC) method. The existing deterministic model is adopted as prior model for this probabilistic model, and 180 sets of chloride diffusion coefficient data under different stresses obtained from 23 published journal papers are used as posterior information for this probabilistic model. The accuracy of the proposed model is validated by comparing with the experimental samples and other existing models. Analysis results show that the proposed probabilistic prediction model provided a reasonable confidence interval of the correction coefficient of stress effect on chloride diffusion coefficient. The provided iterative algorithm reduce the length of confidence interval with the exact prediction precision.
Probabilistic Prediction Model for the Chloride Diffusion Coefficient of Concrete under Tensile and Compressive Stresses
KSCE J Civ Eng
Guo, Ruiqi (author) / Guo, Zengwei (author) / Shi, Yueyi (author)
KSCE Journal of Civil Engineering ; 26 ; 495-510
2022-02-01
16 pages
Article (Journal)
Electronic Resource
English
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