Please use this identifier to cite or link to this item: https://doi.org/10.3390/s18061879
Title: Dynamic model updating for bridge structures using the kriging model and PSO algorithm ensemble with higher vibration modes
Authors: Qin, S
Zhang, Y
Zhou, Y.-L 
Kang, J
Keywords: Bridges
Computation theory
Dynamic models
Interpolation
Modal analysis
Particle swarm optimization (PSO)
Regression analysis
Time domain analysis
Bridge structures
Higher mode
Kriging model
Latin hypercube sampling
Model updating
Objective functions
Operational modal analysis
Particle swarm optimization algorithm
Finite element method
Issue Date: 2018
Publisher: MDPI AG
Citation: Qin, S, Zhang, Y, Zhou, Y.-L, Kang, J (2018). Dynamic model updating for bridge structures using the kriging model and PSO algorithm ensemble with higher vibration modes. Sensors (Switzerland) 18 (6) : 1879. ScholarBank@NUS Repository. https://doi.org/10.3390/s18061879
Rights: Attribution 4.0 International
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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Sensors (Switzerland)
URI: https://scholarbank.nus.edu.sg/handle/10635/179033
ISSN: 14248220
DOI: 10.3390/s18061879
Rights: Attribution 4.0 International
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