The Bayesian Forecasting of the Bridge Deflection Based on Constant Mean Discount Model
Shuangrui Chen, Quansheng Yan*
Identifiers and Pagination:Year: 2015
First Page: 1016
Last Page: 1021
Publisher Id: TOCIEJ-9-1016
Article History:Received Date: 3/2/2015
Revision Received Date: 3/4/2015
Acceptance Date: 25/5/2015
Electronic publication date: 29/10/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Subject to various factors under loading, bridges appear to be discrete. Thus, it is unavoidable to take the practical bridge into consideration with regard to the bridge deflection forecasting. Given this, the Bayesian dynamic forecasting theory is introduced to forecast the bridge deflection. Since the bridge deflection can stay stable in a long term, create constant mean discount Bayesian conditional equation and observational equation and deduce the Bayesian posterior probability of the bridge deflection conditional parameters on the basis of the prior information of the parameters. With recursive deduction, the conditional parameters keep updating as observational data are imported. The results of Bayesian forecasting comprise values and confidence interval, which makes it more instructive. Finally, practical examples are adopted to examine the superior performance of Bayesian dynamic forecasting theory.