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http://dx.doi.org/10.12989/sss.2020.25.4.487

An efficient approach for model updating of a large-scale cable-stayed bridge using ambient vibration measurements combined with a hybrid metaheuristic search algorithm  

Hoa, Tran N. (Soete Laboratoy, Department of Electrical Energy, Metals, Mechanical Constructions, and Systems, Faculty of Engineering and Architecture, Ghent University)
Khatir, S. (Soete Laboratoy, Department of Electrical Energy, Metals, Mechanical Constructions, and Systems, Faculty of Engineering and Architecture, Ghent University)
De Roeck, G. (Department of Civil Engineering, KU Leuven)
Long, Nguyen N. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications)
Thanh, Bui T. (Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications)
Wahab, M. Abdel (Division of Computational Mechanics, Ton Duc Thang University)
Publication Information
Smart Structures and Systems / v.25, no.4, 2020 , pp. 487-499 More about this Journal
Abstract
This paper proposes a novel approach to model updating for a large-scale cable-stayed bridge based on ambient vibration tests coupled with a hybrid metaheuristic search algorithm. Vibration measurements are carried out under excitation sources of passing vehicles and wind. Based on the measured structural dynamic characteristics, a finite element (FE) model is updated. For long-span bridges, ambient vibration test (AVT) is the most effective vibration testing technique because ambient excitation is freely available, whereas a forced vibration test (FVT) requires considerable efforts to install actuators such as shakers to produce measurable responses. Particle swarm optimization (PSO) is a famous metaheuristic algorithm applied successfully in numerous fields over the last decades. However, PSO has big drawbacks that may decrease its efficiency in tackling the optimization problems. A possible drawback of PSO is premature convergence leading to low convergence level, particularly in complicated multi-peak search issues. On the other hand, PSO not only depends crucially on the quality of initial populations, but also it is impossible to improve the quality of new generations. If the positions of initial particles are far from the global best, it may be difficult to seek the best solution. To overcome the drawbacks of PSO, we propose a hybrid algorithm combining GA with an improved PSO (HGAIPSO). Two striking characteristics of HGAIPSO are briefly described as follows: (1) because of possessing crossover and mutation operators, GA is applied to generate the initial elite populations and (2) those populations are then employed to seek the best solution based on the global search capacity of IPSO that can tackle the problem of premature convergence of PSO. The results show that HGAIPSO not only identifies uncertain parameters of the considered bridge accurately, but also outperforms than PSO, improved PSO (IPSO), and a combination of GA and PSO (HGAPSO) in terms of convergence level and accuracy.
Keywords
model updating; evolutionary algorithm; cable-stayed bridge; improved particle swarm optimization; ambient vibration measurements; genetic algorithm; hybrid algorithm;
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Times Cited By KSCI : 6  (Citation Analysis)
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1 Yang, M., Wang, W.P., Zhao, N., Yang, Y.H. and Ren, Y. (2014), "Comparison of Particle Swarm Algorithm and Ant Colony Algorithm in the Optimization of Uniform Quantizer", Appl. Mech. Mater., 602, 3608-3611. https://doi.org/10.4028/www.scientific.net/AMM.602-605.3608   DOI
2 Zhong, R., Zong, Z., Niu, J., Liu, Q. and Zheng, P. (2016), "A multiscale finite element model validation method of composite cable-stayed bridge based on Probability Box theory", J. Sound Vib., 370, 111-131. https://doi.org/10.1016/j.jsv.2016.01.055   DOI
3 Dooms, D., Jansen, M., De Roeck, G., Degrande, G., Lombaert, G., Schevenels, M. and Francois, S. (2010), "StaBIL: A finite element toolbox for MATLAB", VERSION 2.0 USER'S GUIDE.
4 Eberhart, R. and Kennedy, J. (1995), "A new optimizer using particle swarm theory. In MHS'95", Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, Japan, October. https://doi.org/10.1109/MHS.1995.494215
5 El-Borgi, S., Choura, S., Ventura, C., Baccouch, M. and Cherif, F. (2005), "Modal identification and model updating of a reinforced concrete bridge", Smart Struct. Syst., Int. J., 1(1), 83-101. https://doi.org/10.12989/sss.2005.1.1.083   DOI
6 Guo, H., Zhuang, X. and Rabczuk, T. (2019), "A deep collocation method for the bending analysis of Kirchhoff plate", CMC - Comput. Mater. Continua, 59(2), 433-456. https://doi.org/10.32604/cmc.2019.06660   DOI
7 Higashi, N. and Iba, H. (2003), "Particle swarm optimization with Gaussian mutation", Proceedings of the 2003 IEEE Swarm Intelligence Symposium. Indianapolis, IN, USA, USA. June. https://doi.org/10.1109/SIS.2003.1202250
8 Hjelmstad, K.D., Banan, M.R. and Banan, M.R. (1995), "On building finite element models of structures from modal response", Earthq. Eng. Struct. Dyn., 24(1), 53-67. https://doi.org/10.1002/eqe.4290240105   DOI
9 Khatir, S., Belaidi, I., Khatir, T., Hamrani, A., Zhou, Y.L. and Abdel Wahab, M. (2017), "Multiple damage detection in composite beams using Particle Swarm Optimization and Genetic Algorithm", Mechanika, 23(4), 514-521. https://doi.org/10.5755/j01.mech.23.4.15254
10 Khatir, S., Dekemele, K., Loccufier, M., Khatir, T. and Wahab, M.A. (2018), "Crack identification method in beam-like structures using changes in experimentally measured frequencies and Particle Swarm Optimization", Comptes Rendus Mecanique, 346(2), 110-120. https://doi.org/10.1016/j.crme.2017.11.008   DOI
11 Lu, Y., Liang, M., Ye, Z. and Cao, L. (2015), "Improved particle swarm optimization algorithm and its application in text feature selection", Appl. Soft Comput., 35, 629-636. https://doi.org/10.1016/j.asoc.2015.07.005   DOI
12 Kuok, S.C. and Yuen, K.V. (2016), "Investigation of modal identification and modal identifiability of a cable-stayed bridge with Bayesian framework", Smart Struct. Syst., Int. J., 17(3), 445-470. https://doi.org/10.12989/sss.2016.17.3.445   DOI
13 Liang, Y., Feng, Q., Li, H. and Jiang, J. (2019), "Damage detection of shear buildings using frequency-change-ratio and model updating algorithm", Smart Struct. Syst., Int. J., 23(2), 107-122. https://doi.org/10.12989/sss.2019.23.2.107
14 Lovbjerg, M., Rasmussen, T.K. and Krink, T. (2001), "Hybrid particle swarm optimiser with breeding and subpopulations", Proceedings of the 3rd Annual Conference on Genetic and Evolutionary Computation, San Francisco, CA, USA, July.
15 Meng, F., Yu, J., Alaluf, D., Mokrani, B. and Preumont, A. (2019), "Modal flexibility based damage detection for suspension bridge hangers: A numerical and experimental investigation", Smart Struct. Syst., Int. J., 23(1), 15-29. https://doi.org/10.12989/sss.2019.23.1.015
16 Parsopoulos, K. and Vrahatis, M. (2002), "Initializing the particle swarm optimizer using the nonlinear simple method", Adv. Intel. Syst. Fuzzy Syst. Evolut. Computat., 216, 1-6.
17 Peeters, B. and De Roeck, G. (1999), "Reference-based stochastic subspace identification for output-only modal analysis", Mech. Syst. Signal Process., 13(6), 855-878. https://doi.org/10.1006/mssp.1999.1249   DOI
18 Qin, S., Zhang, Y., Zhou, Y.L. and Kang, J. (2018), "Dynamic model updating for bridge structures using the kriging model and PSO algorithm ensemble with higher vibration modes", Sensors, 18(6), 1879. https://doi.org/10.3390/s18061879   DOI
19 Reynders, E., Schevenels, M. and Roeck, G.D. (2014), A MATLAB Toolbox for Experimental and Operational Modal Analysis, MACEC.
20 Reynders, E., Teughels, A. and De Roeck, G. (2010), "Finite element model updating and structural damage identification using OMAX data", Mech. Syst. Signal Process., 24(5), 1306-1323. https://doi.org/10.1016/j.ymssp.2010.03.014   DOI
21 Ribeiro, D., Calcada, R., Delgado, R., Brehm, M. and Zabel, V. (2012), "Finite element model updating of a bowstring-arch railway bridge based on experimental modal parameters", Eng. Struct., 40, 413-435. https://doi.org/10.1016/j.engstruct.2012.03.013   DOI
22 Teughels, A. and De Roeck, G. (2005), "Damage detection and parameter identification by finite element model updating", Revue europeenne de genie civil, 9(1-2), 109-158. https://doi.org/10.1080/17747120.2005.9692748   DOI
23 Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L. and Abdel Wahab, M. (2018), "Model updating for Nam O bridge using particle swarm optimization algorithm and genetic algorithm", Sensors, 18(12), 4131. https://doi.org/10.3390/s18124131   DOI
24 Anitescu, C., Atroshchenko, E., Alajlan, N. and Rabczuk, T. (2019), "Artificial neural network methods for the solution of second order boundary value problems", Comput. Mater. Continua, 59(1), 345-359. https://doi.org/10.32604/cmc.2019.06641   DOI
25 Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T. and Wahab, M.A. (2019a), "An efficient artificial neural network for damage detection in bridges and beam-like structures by improving training parameters using cuckoo search algorithm", Eng. Struct., 199, 109637. https://doi.org/10.1016/j.engstruct.2019.109637   DOI
26 Tran-Ngoc, H., Khatir, S., De Roeck, G., Bui-Tien, T., Nguyen-Ngoc, L. and Wahab, M.A. (2019b), "Stiffness Identification of Truss Joints of the Nam O Bridge Based on Vibration Measurements and Model Updating", Proceedings of International Conference on Arch Bridges, October, pp. 264-272. https://doi.org/10.1007/978-3-030-29227-0_26
27 Reynders, E. and De Roeck, G. (2008), "Reference-based combined deterministic-stochastic subspace identification for e$\times$perimental and operational modal analysis", Mech. Syst. Signal Process., 22(3), 617-637. https://doi.org/10.1016/j.ymssp.2007.09.004   DOI
28 Ali, A.F. and Tawhid, M.A. (2017), "A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems", Ain Shams Eng. J., 8(2), 191-206. https://doi.org/10.1016/j.asej.2016.07.00r8   DOI
29 Alqattan, Z.N. and Abdullah, R. (2013), "A comparison between artificial bee colony and particle swarm optimization algorithms for protein structure prediction problem", Proceedings of International Conference on Neural Information Processing, pp. 331-340. https://doi.org/10.1007/978-3-642-42042-9_42
30 Arangio, S. and Bontempi, F. (2015), "Structural health monitoring of a cable-stayed bridge with Bayesian neural networks", Struct. Infrastruct. Eng., 11(4), 575-587. https://doi.org/10.1080/15732479.2014.951867   DOI
31 Ashebo, D.B., Chan, T.H. and Yu, L. (2007), "Evaluation of dynamic loads on a skew box girder continuous bridge Part I: Field test and modal analysis"; Eng. Struct., 29(6), 1052-1063. https://doi.org/10.1016/j.engstruct.2006.07.014   DOI
32 Baskar, S. and Suganthan, P.N. (2004), "A novel concurrent particle swarm optimization", Proceedings of the 2004 Congress on Evolutionary Computation. Portland, OR, USA, September.
33 Deng, L. and Cai, C. (2009), "Bridge model updating using response surface method and genetic algorithm", J. Bridge Eng., 15(5), 553-564. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000092   DOI
34 Wu, B., Lu, H., Chen, B. and Gao, Z. (2017), "Study on finite element model updating in highway bridge static loading test using spatially-distributed optical fiber sensors", Sensors, 17(7), 1657. https://doi.org/10.3390/s17071657   DOI
35 Wang, X.H. and Li, J.-J. (2004), "Hybrid particle swarm optimization with simulated annealing", Proceedings of 2004 International Conference on Machine Learning and Cybernetics, Shanghai, China, January. https://doi.org/10.1109/ICMLC.2004.1382205