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Finite element model updating considering boundary conditions using neural networks
https://orcid.org/0000-0001-6157-0419
HighlightsFE model updating considering the boundary condition is proposed.Boundary’s constraining effects were represented by artificial rotational springs.The relationship between rotational spring and bridge response has been derived.Initial FE model was updated using Neural Networks.The proposed method was verified through laboratory and field experiments.The proposed method can estimate the boundary condition of bridges very accurately.
AbstractA novel technique to evaluate the bridge boundary condition using neural networks is proposed. It can be used to establish a more accurate finite element (FE) model considering the behaviors of boundary conditions. In the proposed method, the aging and constraining effect of the boundary condition is represented by an artificial rotational spring at each support. A relationship between the responses of the bridge and the rotational spring constant is analytically investigated. This relationship can be used to estimate the rotational spring constant of the bridge using neural networks. The proposed method was verified through laboratory tests and field tests on a steel girder bridge. The proposed method can estimate the bridge boundary conditions directly from the actual behaviors of bridge supports, and this can effectively reduce the uncertainty of boundary conditions in FE model updating.
Finite element model updating considering boundary conditions using neural networks
https://orcid.org/0000-0001-6157-0419
HighlightsFE model updating considering the boundary condition is proposed.Boundary’s constraining effects were represented by artificial rotational springs.The relationship between rotational spring and bridge response has been derived.Initial FE model was updated using Neural Networks.The proposed method was verified through laboratory and field experiments.The proposed method can estimate the boundary condition of bridges very accurately.
AbstractA novel technique to evaluate the bridge boundary condition using neural networks is proposed. It can be used to establish a more accurate finite element (FE) model considering the behaviors of boundary conditions. In the proposed method, the aging and constraining effect of the boundary condition is represented by an artificial rotational spring at each support. A relationship between the responses of the bridge and the rotational spring constant is analytically investigated. This relationship can be used to estimate the rotational spring constant of the bridge using neural networks. The proposed method was verified through laboratory tests and field tests on a steel girder bridge. The proposed method can estimate the bridge boundary conditions directly from the actual behaviors of bridge supports, and this can effectively reduce the uncertainty of boundary conditions in FE model updating.
Finite element model updating considering boundary conditions using neural networks
https://orcid.org/0000-0001-6157-0419
HighlightsFE model updating considering the boundary condition is proposed.Boundary’s constraining effects were represented by artificial rotational springs.The relationship between rotational spring and bridge response has been derived.Initial FE model was updated using Neural Networks.The proposed method was verified through laboratory and field experiments.The proposed method can estimate the boundary condition of bridges very accurately.
AbstractA novel technique to evaluate the bridge boundary condition using neural networks is proposed. It can be used to establish a more accurate finite element (FE) model considering the behaviors of boundary conditions. In the proposed method, the aging and constraining effect of the boundary condition is represented by an artificial rotational spring at each support. A relationship between the responses of the bridge and the rotational spring constant is analytically investigated. This relationship can be used to estimate the rotational spring constant of the bridge using neural networks. The proposed method was verified through laboratory tests and field tests on a steel girder bridge. The proposed method can estimate the bridge boundary conditions directly from the actual behaviors of bridge supports, and this can effectively reduce the uncertainty of boundary conditions in FE model updating.
Finite element model updating considering boundary conditions using neural networks
Park, Young-Soo (author) / Kim, Sehoon (author) / Kim, Namgyu (author) / Lee, Jong-Jae (author)
Engineering Structures ; 150 ; 511-519
2017-07-11
9 pages
Article (Journal)
Electronic Resource
English
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