Journal of Korean Society of Steel Construction (한국강구조학회 논문집)

Korean Society of Steel Construction (한국강구조학회)
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Damage Assessment of Steel Box-girder Bridge using Neural Networks

신경망을 이용한 강박스거더교의 손상평가

  • 이인원 (한국과학기술원 토목공학과) ;
  • 오주원 (한남대학교 토목환경공학과) ;
  • 박선규 (성균관대학교 토목공학과) ;
  • 김주태 (한국과학기술원 토목공학과)
  • Received : 1998.12.17
  • Published : 1999.02.27
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Abstract

Damages of a steel box girder bridge are detected using neural networks. Damage detection using neural networks has increasing momentum in structural engineering. It is a new effort to overcome the limitations of the conventional analytical approaches and applied to the damage detection of a steel box-girder bridge. Data sets for training neural networks are obtained from the acceleration response of the bridge under moving load. Finite element model is first defined and damages of 5, 10, 15 and 20% are assumed in the model. Not only the trained damages but untrained damages are detected in the assessment stage. The untrained damages can be detected with acceptable errors. Because the number of damaged locations are limited to a few parts, more researches are needed to put this technique into practice.

신경망을 이용하여 강박스거더교의 손상을 평가하였다. 최근, 신경망을 이용한 손상평가는 구조공학분야에서 많은 연구가 진행되고 있다. 이것은 기존방법의 한계를 극복하고자 하는 새로운 시도로서 본 연구에서는 강교의 손상평가에 적용되었다. 신경망 학습을 위한 자료는 이동하중에 의한 교량의 가속도 응답으로부터 얻었다. 유한요소모델이 우선 정의되고 여기에 5, 10, 15 및 20%의 손상을 가정하였다. 평가단계에서는 학습한 손상은 물론 학습하지 않은 손상도 잘 감지하였다. 본 연구에서는 손상부위가 몇 개의 부위로 한정되었으며 현장의 적용을 위해서는 보다 많은 연구가 필요하다.

Keywords

Acknowledgement

Supported by : 포항종합제철주식회사

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