Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm
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Department of Electrical Energy, Metals, Mechanical Constructions, and Systems, Faculty of Engineering and Architecture, Ghent University, 9000 Gent, Belgium
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Department of Bridge and Tunnel Engineering, Faculty of Civil Engineering, University of Transport and Communications, Hanoi, Vietnam
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KU Leuven, Department of Civil Engineering, B-3001 Leuven, Belgium
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Institute of Research and Development, Duy Tan University, 03 Quang Trung, Da Nang, Vietnam
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Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 903, B-9052 Zwijnaarde, Belgium
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Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4131; https://doi.org/10.3390/s18124131
Received: 29 October 2018
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Revised: 20 November 2018
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Accepted: 21 November 2018
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Published: 26 November 2018
(This article belongs to the Special Issue Bridge Structural Health Monitoring and Damage Identification)
Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.
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Keywords:
model updating; particle swarm optimization; genetic algorithm; large-scale bridges; stiffness of truss joints
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MDPI and ACS Style
Tran-Ngoc, H.; Khatir, S.; De Roeck, G.; Bui-Tien, T.; Nguyen-Ngoc, L.; Abdel Wahab, M. Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm. Sensors 2018, 18, 4131. https://doi.org/10.3390/s18124131
AMA Style
Tran-Ngoc H, Khatir S, De Roeck G, Bui-Tien T, Nguyen-Ngoc L, Abdel Wahab M. Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm. Sensors. 2018; 18(12):4131. https://doi.org/10.3390/s18124131
Chicago/Turabian StyleTran-Ngoc, H., S. Khatir, G. De Roeck, T. Bui-Tien, L. Nguyen-Ngoc, and M. Abdel Wahab. 2018. "Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm" Sensors 18, no. 12: 4131. https://doi.org/10.3390/s18124131
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