@article{8670425,
  abstract     = {{This paper proposes the use of transmissibility functions combined with a machine learning algorithm, Artificial Neural Networks (ANNs), to assess damage in a truss bridge. A new approach method, which makes use of the input parameters calculated from the transmissibility function, is proposed. The network not only can predict the existence of damage, but also can classify the damage types and identity the location of the damage. Sensors are installed in the truss joints in order to measure the bridge vibration responses under train and ambient excitations. A finite element (FE) model is constructed for the bridge and updated using FE software and experimental data. Both single damage and multiple damage cases are simulated in the bridge model with different scenarios. In each scenario, the vibration responses at the considered nodes are recorded and then used to calculate the transmissibility functions. The transmissibility damage indicators are calculated and stored as ANNs inputs. The outputs of the ANNs are the damage type, location and severity. Two machine learning algorithms are used; one for classifying the type and location of damage, whereas the other for finding the severity of damage. The measurements of the Nam O bridge, a truss railway bridge in Vietnam, is used to illustrate the method. The proposed method not only can distinguish the damage type, but also it can accurately identify damage level.}},
  author       = {{Nguyen, Huong Duong and Tran, Ngoc Hoa and Bui-Tien, T. and De Roeck, G. and Abdel Wahab, Magd}},
  issn         = {{1738-1584}},
  journal      = {{SMART STRUCTURES AND SYSTEMS}},
  keywords     = {{transmissibility,machine learning algorithm,Artificial Neural Networks (ANNs),Structural Health Monitoring (SHM),large-scale truss bridge,ARTIFICIAL NEURAL-NETWORKS,MODAL PARAMETERS,IDENTIFICATION,QUANTIFICATION,EXCITATION}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{35--47}},
  title        = {{Damage detection in truss bridges using transmissibility and machine learning algorithm : application to Nam O bridge}},
  url          = {{http://doi.org/10.12989/sss.2020.26.1.035}},
  volume       = {{26}},
  year         = {{2020}},
}

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