KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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Estimation of Dynamic Vertical Displacement using Artificial Neural Network and Axial strain in Girder Bridge

인공신경망과 축방향 변형률을 이용한 거더 교량의 동적 수직 변위 추정

  • 옥수열 (현대건설 토목환경사업본부 토목환경기술개발실) ;
  • 문현수 (연세대학교 사회환경시스템공학부 통합과정) ;
  • 전방조 (에히메(Ehime)대학교 사회환경공학부) ;
  • 임윤묵 (연세대학교 사회환경시스템공학부)
  • Received : 2011.12.20
  • Accepted : 2014.07.18
  • Published : 2014.12.01
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Abstract

Dynamic displacements of structures shows general behavior of structures. Generally, It is used to estimate structure condition and trustworthy physical quantity directly. Especially, measuring vertical displacement which is affected by moving load is very important part to find or identify a problem of bridge in advance. However directly measuring vertical displacement of the bridge is difficult because of test conditions and restriction of measuring equipment. In this study, Artificial Neural Network (ANN) is used to suggest estimation method of bridge displacement to overcome constrain conditions, restriction and so on. Horizontal strain and vertical displacement which are measured by appling random moving load on the bridge are applied for learning and verification of ANN. Measured horizontal strain is used to learn ANN to estimate vertical displacement of the bridge. Numerical analysis is used to acquire learning data for axis strain and vertical displacement for applying ANN. Moving load scenario which is made by vehicle type and vehicle distance time using Pearson Type III distribution is applied to analysis modeling to reflect real traffic situation. Estimated vertical displacement in respect of horizontal strain according to learning result using ANN is compared with vertical displacement of experiment and it presents vertical displacement of experiment well.

구조물의 변위이력은 구조물의 전체적인 거동을 나타내는 인자의 시간에 대한 이력이므로 이를 추정하는 것은 매우 중요하며, 일반적으로 구조물의 상태를 평가하는데 있어 직관적으로 신뢰할 수 있는 물리량이다. 특히, 교량의 경우 차량 하중에 의해 발생되는 수직 변위를 알아내는 것은 교량에 발생할 수 있는 문제점을 미연에 확인할 수 있어 매우 중요한 부분이다. 하지만 시공된 교량의 수직 변위를 측정하는 것은 실험여건 및 장비의 제약조건 등으로 인해서 직접적으로 측정하는 것이 매우 힘든 실정이다. 본 연구에서는 대상 교량들에 대한 제약조건을 극복하고 변위응답을 추정할 수 있는 방안을 제시하기 위해 임의의 차량하중에 의해서 측정되는 변형률과 변위를 인공신경망에 적용하였다. 인공신경망에 적용하는 축방향 변형률과 수직방향 변위에 대한 학습 자료를 획득하기 위해서 수치해석을 수행하였으며, 실제 교통 상황을 반영하기 위해서 교량을 통과하는 차량의 종류와 차간 거리에 대한 차량이동하중 시나리오를 작성하여 시공된 교량의 실제 교통상황에 따른 차량 이동 하중이 가해지도록 모델링하였다. 인공신경망을 이용한 학습 결과에 따라 임의의 하중에 의해 발생되는 교량의 변형률에 대한 변위를 추정하였고, 인공신경망을 사용하여 추정된 변위 결과가 수치해석을 통한 변위를 잘 표현하는 것을 확인하였다.

Keywords

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