Application of Simplified Dynamic Response Analysis and Examinations of Interaction Spring and Damping Factor of Existing Bridges

Okada, Yoshihisa; Ogawa, Yoshimi; Hiroshima, Minoru; Iwatate, Takahiro

doi:10.2208/jsceja.65.879

Cited by 1 publication

(4 citation statements)

References 9 publications

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“…To demonstrate the sensitivity of the algorithm, the 1-2 m section of the pier is deteriorated; k 2 is 10% smaller. Some previous research works indicated that damping coefcients of damaged structures increase [38][39][40]. Terefore, c 2 is 10% larger.…”

Section: Simulation Casementioning

confidence: 89%

“…Previous link models are limited to linear systems [38][39][40]. Nonlinear MDOF systems are rarely discussed possibly because of the complexity of the systems.…”

Section: Dynamic Model and Solvermentioning

confidence: 99%

“…Te modelling of this study is inspired by [38][39][40], which proposed pier and foundation pile models combining swayrock springs between lumped masses. To reproduce a fexure behavior of a pier, this study adopts a link model with rocking springs and dampers between lumped masses instead of a frame model.…”

mentioning

confidence: 99%

“…Te reason is detailed in the following section. Okada et al approximated Japanese standard RC bridge piers using sway-rock models [40]. Tey indicated that pier and pile horizontal spring stifnesses are 1 • 10 8 -1 • 10 9 N/m.…”

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confidence: 99%

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

Yamaguchi,

Mizutani

2024

Structural Control and Health Monitoring

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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.

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Section: Simulation Casementioning

confidence: 89%

“…Previous link models are limited to linear systems [38][39][40]. Nonlinear MDOF systems are rarely discussed possibly because of the complexity of the systems.…”

Section: Dynamic Model and Solvermentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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