Abstract
This study applied the kriging model and particle swarm optimization (PSO) algorithm for the dynamic model updating of bridge structures using the higher vibration modes under large-amplitude initial conditions. After addressing the higher mode identification theory using time-domain operational modal analysis, the kriging model is then established based on Latin hypercube sampling and regression analysis. The kriging model performs as a surrogate model for a complex finite element model in order to predict analytical responses. An objective function is established to express the relative difference between analytically predicted responses and experimentally measured ones, and the initial finite element (FE) model is hereinafter updated using the PSO algorithm. The Jalón viaduct—a concrete continuous railway bridge—is applied to verify the proposed approach. The results show that the kriging model can accurately predict the responses and reduce computational time as well.
Highlights
Vibration-based structural health monitoring (SHM) for large-scale civil structures has been an ongoing research topic in both the scientific and engineering community in recent decades [1].By placing different types of sensors on test structures and monitoring structural dynamic behavior, vibration-based SHM provides an attractive solution for health condition evaluations and operational safety management [1,2,3,4,5,6]
To resolve the previously mentioned problems involved in the model updating of bridge structures, this study combines the kriging model and particle swarm optimization (PSO) using the higher vibration modes from ambient vibration tests
This study presented a dynamic model-updating process for bridge structures based on the
Summary
Vibration-based structural health monitoring (SHM) for large-scale civil structures has been an ongoing research topic in both the scientific and engineering community in recent decades [1]. Numerous successful applications of FE model updating have been reported, two problems still exist: (1) difficulty in extracting the higher modes; and (2) low computational efficiency. These problems are of particular concern to the dynamic model updating of bridge structures because they are more likely to sustain local damage. To resolve the previously mentioned problems involved in the model updating of bridge structures, this study combines the kriging model and particle swarm optimization (PSO) using the higher vibration modes from ambient vibration tests. The outline of this study is as follows: Section 2 presents the theoretical basis for higher mode identification based on time-domain operational modal analysis (OMA) considering large-amplitude initial conditions.
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
