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Structural Engineering and Mechanics Volume 71, Number 2, July25 2019 , pages 175-183 DOI: https://doi.org/10.12989/sem.2019.71.2.175 |
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Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks |
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Duong H. Nguyen, Thanh T. Bui, Guido De Roeck and Magd Abdel Wahab
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| Abstract | ||
| This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high. | ||
| Key Words | ||
| Structural Health Monitoring (SHM); transmissibility; Artificial Neural Networks (ANNs); bridge monitoring; Finite Element Method (FEM) | ||
| Address | ||
| Duong H. Nguyen: Department of Electrical energy, metals, mechanical constructions and systems, Faculty of Engineering and Architecture, Ghent University, Belgium Duong H. Nguyen: National University of Civil Engineering, Hanoi, Vietnam Thanh T. Bui: University of Transport and Communications, Hanoi, Vietnam Guido De Roeck: KU Leuven, Department of Civil Engineering, Structural Mechanics, B-3001 Leuven, Belgium Magd Abdel Wahab: Division of Computational Mechanics, Ton Duc Thang University, Ho Chi Minh City, Viet Nam Magd Abdel Wahab: Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam | ||