• Title/Summary/Keyword: rotational end-spring

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Structural Joint Damage Assessment Using Neural Networks (신경망을 이용한 구조물 접합부의 손상도 추정)

  • 방은영;이진학;윤정방
    • Journal of the Earthquake Engineering Society of Korea
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    • v.2 no.1
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    • pp.35-46
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    • 1998
  • Structural damage is used to be modeled through reductions in the stiffness of structural elements for the purpose of damage estimation of structural system. In this study, the concept of joint damage is employed for more realistic damage assessment of a steel structure. The joint damage is estimated damage based on the mode shape informations using neural networks, The beam-to-column connection in a steel frame structure is represented by a rotational spring at the fixed end of a beam element. The severity of joint damage is defined as the reduction ratio of the connection stiffness with respect to the value of the intact joint. The concept of the substructural identification is used for the localized damage assessment in a large structure. The feasibility of the proposed method is examined using an example with simulated data. It has been found that the joint damages can be reasonably estimated for the case with the measurements of the mode vectors subjected to noise.

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A Study on Joint Damage Model and Neural Networks-Based Approach for Damage Assessment of Structure (구조물 손상평가를 위한 접합부 손상모델 및 신경망기법에 관한 연구)

  • 윤정방;이진학;방은영
    • Journal of the Earthquake Engineering Society of Korea
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    • v.3 no.3
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    • pp.9-20
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    • 1999
  • A method is proposed to estimate the joint damages of a steel structure from modal data using the neural networks technique. The beam-to-column connection in a steel frame structure is represented by a zero-length rotational spring of the end of the beam element, and the connection fixity factor is defined based on the rotational stiffness so that the factor may be in the range 0~1.0. Then, the severity of joint damage is defined as the reduction ratio of the connection fixity factor. Several advanced techniques are employed to develop the robust damage identification technique using neural networks. The concept of the substructural indentification is used for the localized damage assessment in the large structure. The noise-injection learning algorithm is used to reduce the effects of the noise in the modal data. The data perturbation scheme is also employed to assess the confidence in the estimated damages based on a few sets of actual measurement data. The feasibility of the proposed method is examined through a numerical simulation study on a 2-bay 10-story structure and an experimental study on a 2-story structure. It has been found that the joint damages can be reasonably estimated even for the case where the measured modal vectors are limited to a localized substructure and the data are severely corrupted with noise.

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