Smart Structures and Systems

  • 제25권4호
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  • Pages.487-499
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  • 2020
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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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)
  • 투고 : 2019.08.15
  • 심사 : 2019.10.25
  • 발행 : 2020.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제번호 : Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures

연구 과제 주관 기관 : Flemish Government

This paper is sponsored by VLIR-UOS TEAM Project, VN2018TEA479A103, 'Damage assessment tools for Structural Health Monitoring of Vietnamese infrastructures' funded by the Flemish Government. The authors also acknowledge the assistance of Dr. Kristof Maes from KU Leuven, Belgium in participating in the measurement campaign of the My Thuan bridge in the framework of the VLIR-UOS research project ZEIN2014Z172. Moreover, the first author needs to acknowledge the financial supports from University of Transport and Communications (UTC) under the project research "T2019- 02TĐ" and from the Bijzonder Onderzoeksfonds (BOF) of Ghent University.

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피인용 문헌

  1. Damage detection in structures using Particle Swarm Optimization combined with Artificial Neural Network vol.28, pp.1, 2021, https://doi.org/10.12989/sss.2021.28.1.001