Journal
MEASUREMENT
Volume 203, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2022.112006
Keywords
Reliability predict; Bayesian; Hilbert dynamic linear model; Regular -vine Copula; Time -varying correlations
Funding
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- key research and development project of Anhui province
- [51922036]
- [JZ2020HGPB0117]
- [1804a0802204]
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This study presents a novel dynamic real-time reliability prediction method by considering the time-varying correlation among the data at multiple measurement points. The Bayesian Hilbert dynamic linear model (BHDLM) is developed to predict the stress response, and the dynamic real-time reliability is then predicted by using the Pair-Copula theory. The feasibility of the proposed method is illustrated by predicting the dynamic real-time reliability of a steel-concrete composite girder bridge based on simulated stress data and a real bridge based on monitoring stress data.
This study presents a novel dynamic real-time reliability prediction method by considering the time-varying correlation among the data at multiple measurement points. The Bayesian Hilbert dynamic linear model (BHDLM) is developed to predict the stress response, and the dynamic real-time reliability is then predicted by using the Pair-Copula theory. To illustrate the feasibility of the proposed method, the dynamic real-time reliability of a steel-concrete composite girder bridge based on the simulated stress data due to a single vehicle with various weights and random traffic flow load is predicted. Finally, the dynamic reliability of a real bridge based on the monitoring stress data is predicted. The results show that the proposed BHDLM is effective for predicting coupled stresses, and the dynamic real-time reliability can be predicted. In addition, the predicted dynamic reliability of the real bridge demonstrates that the influence of the data correlation should be considered.
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