Abstract
To reasonably predict the steel box girder reliability considering the dynamic dependence among the performance functions corresponding to the failure modes of the multiple monitoring points, this paper firstly adopts the dynamic monitoring extreme stresses of the multiple control points to build the Bayesian Dynamic Vine Copula Model (BDVCM) taking into account the dynamic dependence of the multiple monitoring variables through combining the vine copula technique with Bayesian Dynamic Linear Models (BDLM); secondly, with first-order second-moment method and the built BDVCM, the steel box girder reliability, taking into account dynamic dependence among the performance functions corresponding to the failure modes of the multiple monitoring points, is predicted; finally, the monitoring data from the five sections of an existing steel box girder were provided to illustrate the proposed model and approach. The analytical results illustrated that the predicted results, without considering the dynamic nonlinear dependence among the failure modes of the multiple monitoring points, are conservative.
Highlights
Structural Health Monitoring (SHM) systems have accumulated a large amount of monitored data in the long-term service periods
Advances in Civil Engineering flying swallow profiled concrete-filled steel tube arch bridge) taking into account the time-variant dependence between the performance functions of only one pair of failure modes is made [5, 6], where the correlation coefficients are not accurately solved and the built Bayesian Dynamic Linear Models (BDLM) does not consider the nonlinear dependence between a pair of variables. e above research studies show that the existing dynamic reliability prediction methods of bridge structures considered timevariant nonlinear dependence between one pair of performance functions, and the correlation coefficients are not accurately solved
Actual bridge systems commonly have multiple control monitoring points. erefore, building more accurate dynamic nonlinear dependence models among the multiple failure modes corresponding to the control monitoring points and further predicting the dynamic reliability of bridge systems should be further studied and become the aim of the present research
Summary
Structural Health Monitoring (SHM) systems have accumulated a large amount of monitored data in the long-term service periods. In view of the above problems, this study adopts the steel box girder as the research object, takes a newly built Bayesian Dynamic Vine Copula Model (BDVCM) to characterize the dynamic nonlinear correlations among the performance functions of the failure modes at multiple control monitoring points of the steel box girder based on BHM data, and further dynamically predicts the steel box girder reliability. With equation (6), it is known that two one-step prediction variables (y1, y2) both follow normal distribution; namely, y1,t+1∼N. where ρt is the dynamic relevant parameter of bivariate Gaussian copula function, which can be computed with equation (17). Because the multiple parallel systems composed of any two control monitoring points are serial, with equations (21)-(24), the predicted failure probability of the steel box girder considering the nonlinear correlation among performance functions of failure modes about multiple control monitoring points can be solved with the following equation: Pfsystem,t+1. E (b) Figure 3: (a) Fumin Bridge. (b) Five monitoring sections of Fumin Bridge girder system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
