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A Physics-Informed Neural Network for the Nonlinear Damage Identification in a Reinforced Concrete Bridge Pier Using Seismic Responses

Auteur(s): ORCID
ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Structural Control and Health Monitoring, , v. 2024
Page(s): 1-22
DOI: 10.1155/2024/5532909
Abstrait:

The condition assessment of reinforced concrete (RC) bridge piers after an earthquake using measured responses is important for ensuring the safety of road and railway users. The problem is nonlinear, and the locations and extents of damages are various. However, previous research works focused on linear structural identification or model updating assuming a limited number of nonlinear materials for reasonable estimates. Leveraging the ability of deep learning (DL) for robustly estimating a large number of unknown parameters, this study proposes an ALL nonlinear spring multi-degree-of-freedom (MDOF) damage identification algorithm based on a physics-informed neural network (PINN). The algorithm is applied to a stacked bilinear rotational spring and damper model of a pier. The number of unknown parameters reaches about 50. The errors of estimated elastic stiffnesses, damping coefficients, and ductility factors (DFs) using simulated responses added with noises are 0.4%, 0.6%, and 3.1%, respectively. Using full-scale RC bridge pier shaking table experiments, the algorithm revealed the distributions of elastic stiffnesses and DFs along the pier height and their deteriorations. The effects of different types of local damages are quantitatively evaluated and visualized on the distributions.

Structurae ne peut pas vous offrir cette publication en texte intégral pour l'instant. Le texte intégral est accessible chez l'éditeur. DOI: 10.1155/2024/5532909.
  • Informations
    sur cette fiche
  • Reference-ID
    10758463
  • Publié(e) le:
    15.03.2024
  • Modifié(e) le:
    15.03.2024
 
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