Assessment on long-term deflection of concrete beam bridges based on uncertainty quantification method. (December 2021)

• Record Type: Journal Article
• Authors: Guo, Qi; Sun, Yanbo; Mi, Tao
• Is Part Of: Structures. Volume 34(2021)
• Page Start: 3013
• Page End: 3027
• Publication Date: 2021-12
• Subjects: Bayesian inference -- Uncertainty coefficient -- Prior distribution -- Posterior distribution -- Creep -- Shrinkage -- Long-term deflection; Structural engineering -- Periodicals; 624.1
• DOI: 10.1016/j.istruc.2021.09.029
• Languages: English
• ISSNs: 2352-0124

Abstract

Abstract: Huge uncertainty exists in time dependent effects of deflection, especially for those concrete beam bridges in long-term service stage. The uncertainty has brought about great barriers to reliable assessment or reasonable prediction for the deflection. Most studies in this field focused on qualitative analysis rather than quantitative one. In order to realize the transformation from qualitative analysis to quantitative field of the uncertainty issue for bridge structure and expend the existing research, the Bayesian inference model was employed to conduct uncertainty quantification analysis based on the open-source database provided by Northwest University. The uncertainty coefficient was introduced to measure the differences between the test results and the theoretical ones for creep and shrinkage data. In the meantime, a surrogate model was proposed on deviation between long-term deflection for numerical value and experimental one, in which the former was calculated by ANSYS software and the latter was acquired by the in-door test on a simply supported beam, namely, Faber beam. After that, the uncertainty coefficient was introduced to update the model and the simulating process was performed again in which the corrected mean value of the posterior distribution was utilized. The results show that it narrows the error zone drastically, in which the relative reduction rate range from 49.83% to 82.76% for 95% lower confidence limit and from 11.46% to 98.73% for upper one. These outcomes prove the validity for the posterior distribution and also indicate that it could be a prospective way to improve the accuracy of the model significantly by utilizing the uncertainty coefficient. Besides, the method provided in this study could inspire the researchers who study in similar field and to do further exploration in the future.