Vilnius Gediminas Technical UniversityRiga Technical UniversityTallinn University of TechnologyBaltic Road Association


Abstracted in databases:
Thomson SCIE: Science Citation Index ExpandedTM (Web of Science), INSPEC, EBSCO, TRIS/TRIS Online, VINITI, CSA's ERD, CSA/ASCE (CSA's TRD), SCOPUS (Elsevier Database)

2015 Impact Factor: 0.519 ©2015 Thomson Reuters, 2015 Journal Citation Report®

ISSN 1822-427X print
ISSN 1822-4288 online



"The Baltic Journal of Road and Bridge Engineering"
Vilnius: Technika, 2017, Vol XII, No 3, p. 193-202

Zhiyuan Xia, Aiqun Li, Jianhui Li, Maojun Duan

Comparison of hybrid methods with different meta model used in bridge model-updating

DOI: 10.3846/bjrbe.2017.24
Two hybrid model updating methods by integration of Gaussian mutation particle swarm optimization method, Latin Hypercube Sampling technique and meta models of Kriging and Back-Propagation Neural Network respectively were proposed, and the methods make the convergence speed of the model updating process faster and the Finite Element Model more adequate. Through the application of the hybrid methods to model updating process of a self-anchored suspension bridge in-service with extra-width, which showed great necessity considering the ambient vibration test results, the comparison of the two proposed methods was made. The results indicate that frequency differences between test and modified model were narrowed compared to results between test and original model after model updating using both methods as all the values are less than 6%, which is 25%−40% initially. Furthermore, the Model Assurance Criteria increase a little illustrating that more agreeable mode shapes are obtained as all of the Model Assurance Criteria are over 0.86. The particular advancements indicate that a relatively more adequate Finite Element Model is yielded with high efficiency without losing accuracy by both methods. However, the comparison among the two hybrid methods shows that the one with Back-Propagation Neural Network meta model is better than the one with Kriging meta model as the frequency differences of the former are mostly under 5%, but the latter ones are not. Furthermore, the former has higher efficiency than the other as the convergence speed of the former is faster. Thus, the hybrid method, within Gaussian mutation particle swarm optimization method and Back-Propagation Neural Network meta model, is more suitable for model updating of engineering applications with large-scale, multi-dimensional parameter structures involving implicit performance functions.
Keywords: Back-Propagation Neural Network meta model, Gaussian mutation, Kriging meta model, Latin Hypercube Sampling, model updating, particle swarm optimization, self-anchored suspension bridge.

Read full article (restricted access)

view contents of entire journal number    |    switch abstract language to Lithuanian, Latvian or Estonian




© Vilnius Gediminas Technical University, 2006 - 2018   |   Web Design - Interneto reitingai, lankomumo statistika, lankytojų skaitliukai