Adaptive Sampling Method of Suspension-Bridge Finite Element Models Based on Coupled Modeling Approach

다국어 초록 (Multilingual Abstract)

This paper presents an accurate sampling method for suspension-bridge finite element (FE) model updating. The proposed method utilizes a coupled suspension-bridge modeling approach, which integrates a numerical shape-finding method with conventional FE analysis to efficiently achieve parametric modeling during the sampling process. This study also employs the Nelder-Mead algorithm (NMA) to ensure the efficiency and robustness of the numerical shape-finding calculation. First, the accuracy of the NMA-based coupled modeling approach is examined by numerical investigation of the baseline model of an actual suspension bridge. Then, the computational results of two groups of samples with different sample sizes are compared to test the efficiency of the proposed method. Finally, by relying on the structural health monitoring (SHM) system of this bridge, the measured natural frequencies over half a year are identified, and almost all of them are within the calculated sample space.Therefore, the feasibility of the proposed suspension-bridge FE model sampling method is validated by the uncertainties of the measured responses over half a year.

참고문헌 (Reference)

1 Karoumi R, "Some modeling aspects in the nonlinear finite element analysis of cable supported bridges" 71 : 397-412, 1999

2 Tang M, "Segmental catenary method of calculating the cable curve of suspension bridge" 25 (25): 87-91, 2003

3 Zhou L, "Response surface method based on radial basis functions for modeling large-scale structures in model updating" 28 (28): 210-226, 2013

4 Guo T, "Research in form-finding of suspension structures based on Newton-Raphson iteration and zero order optimization arithmetic" 24 (24): 142-146, 2007

5 Wang H, "Progressive finite element model calibration of a long-span suspension bridge based on ambient vibration and static measurements" 32 (32): 2546-2556, 2010

6 Liu Y, "Probabilistic baseline of finite element model of bridges under environmental temperature changes" 32 (32): 581-598, 2017

7 Shabbir F, "Particle swarm optimization with sequential niche technique for dynamic finite element model updating" 30 (30): 359-375, 2015

8 Jung M-R, "Nonlinear analysis methods based on the unstrained element length for determining initial shaping of suspension bridges under dead loads" 128 : 272-285, 2013

9 Marano GC, "Modified genetic algorithm for the dynamic identification of structural systems using incomplete measurements" 26 (26): 92-110, 2011

10 Zhang S, "Model updating with a neural network method based on uniform design" 16 (16): 1207-1222, 2013

11 Cao H, "Layout and size optimization of suspension bridges based on coupled modeling approach and enhanced particle swarm optimization" 146 : 170-183, 2017

12 Mottershead J, "Interval model updating with irreducible uncertainty using the Kriging predictor" 25 (25): 1204-1226, 2011

13 Chen Z, "Improved particle swarm optimization-based form-finding method for suspension bridge installation analysis" 29 : 04014047-, 2015

14 Ren W, "Finite element model updating in structural dynamics by using the response surface method" 32 (32): 2455-2465, 2010

15 Hu J, "Field monitoring and response characteristics of longitudinal movements of expansion joints in long-span suspension bridges" 162 : 107933-, 2020

16 Lagarias JC, "Convergence properties of the Nelder--Mead simplex method in low dimensions" 9 (9): 112-147, 1998

17 Fang K, "Centered L2-discrepancy of random sampling and Latin hypercube design, and construction of uniform designs" 71 (71): 275-296, 2002

18 Mao J, "Automated modal identification using principal component and cluster analysis: Application to a long-span cable-stayed bridge" 26 (26): 2019

19 Shan D, "A novel finite element model updating method based on substructure and response surface model" 103 : 147-156, 2015

20 Allemang R, "A correlation coefficient for modal vector analysis" 1982