Title: Multi-scale finite element model validation method of cable-stayed bridge based on the support vector regression
Authors: Pei Juan Zheng; Zhou Hong Zong; Qi Qi Liu; Jie Niu; Hai Fei Zhou; Ru Mian Zhong
Addresses: Department of Civil Engineering, Southeast University, Nanjing, 211189, China ' Department of Civil Engineering, Southeast University, Nanjing, 211189, China ' Department of Civil Engineering, Southeast University, Nanjing, 211189, China ' Department of Civil Engineering, Southeast University, Nanjing, 211189, China ' Department of Civil Engineering, Southeast University, Nanjing, 211189, China ' Department of Civil Engineering, Southeast University, Nanjing, 211189, China
Abstract: In this paper, the multi-scale finite element model (FEM) of a composite cable-stayed bridge, Guanhe Bridge, was established based on the Arlequin method firstly. Then a two-step multi-scale FE model updating method was proposed. Furthermore, based on structural health monitoring (SHM) system of Guanhe Bridge, support vector regression (SVR) method was employed to analyse the uncertainty quantification and transmission. It was shown that the errors between the calculated frequencies from the updated multi-scale FEM and the measured frequencies from SHM were less than 3%. In the procedure of inverse uncertainty propagation, the coincidence indexes of the structural parameters were larger than 65%. The deviations between the optimal values of the updated parameters and the corresponding statistical mean values were very small (<5%). Finally, the analysis results indicate that the distributions of the parameters agree well with the assumed normal distribution.
Keywords: multi-scale simulation; finite element model; FEM; validation; support vector regression; SVR; uncertainty quantification and propagation; composite cable-stayed bridge.
DOI: 10.1504/IJLCPE.2019.099887
International Journal of Lifecycle Performance Engineering, 2019 Vol.3 No.1, pp.35 - 58
Received: 23 May 2018
Accepted: 17 Oct 2018
Published online: 26 May 2019 *