Abstract
Nowadays, many more bridges with extra-width have been needed for vehicle throughput. In order to obtain a precise finite element (FE) model of those complex bridge structures, the practical hybrid updating method by integration of Gaussian mutation particle swarm optimization (GMPSO), Kriging meta-model and Latin hypercube sampling (LHS) was proposed. By demonstrating the efficiency and accuracy of the hybrid method through the model updating of a damaged simply supported beam, the proposed method was applied to the model updating of a self-anchored suspension bridge with extra-width which showed great necessity considering the results of ambient vibration test. The results of bridge model updating showed that both of the mode frequencies and shapes had relatively high agreement between the updated model and experimental structure. The successful model updating of this bridge fills in the blanks of model updating of a complex self-anchored suspension bridge. Moreover, the updating process enables other model updating issues for complex bridge structures
Highlights
Self-anchored suspension bridges have attracted lots of attention from bridge engineers in recent years
The architecture of the hybrid method consists of three main stages of analysis: (1) selecting reasonable samples of random parameters as input datasets using Latin hypercube sampling (LHS) and performing finite element (FE) analyses with the input parameters to obtain output datasets; (2) formulating the Kriging meta-model based on input and output datasets; and (3) applying Gaussian mutation particle swarm optimization (GMPSO) as an optimization technique with an objective function set as errors between experimental value and Kriging model to yield the updated values of selected parameters
It is evident that the results of direct GMPSO and hybrid method are highly consistent, which demonstrates that the proposed hybrid method is applicable to model updating of structures with multi-parameters
Summary
Self-anchored suspension bridges have attracted lots of attention from bridge engineers in recent years. The other is iterative method by modifying the selected parameters after analysis of sensitivity like material module, density, moment of inertia, area of section, etc This updating technique is common practice to transfer a model updating process into a bound-constrained optimization process. The architecture of the hybrid method consists of three main stages of analysis: (1) selecting reasonable samples of random parameters as input datasets using LHS and performing FE analyses with the input parameters to obtain output datasets; (2) formulating the Kriging meta-model based on input and output datasets; and (3) applying GMPSO as an optimization technique with an objective function set as errors between experimental value and Kriging model to yield the.
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