Structural Engineering and Mechanics

  • 제41권4호
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  • Pages.527-538
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  • 2012
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Evaluation of accidental eccentricity for buildings by artificial neural networks

  • Badaoui, M. (University of Djelfa) ;
  • Chateauneuf, A. (Clermont Universite, Universite Blaise Pascal, Institut Pascal) ;
  • Fournely, E. (Clermont Universite, Universite Blaise Pascal, Institut Pascal) ;
  • Bourahla, N. (University of Blida) ;
  • Bensaibi, M. (University of Blida)
  • 투고 : 2010.08.21
  • 심사 : 2012.02.01
  • 발행 : 2012.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

In seismic analyses of structures, additional eccentricity is introduced to take account for oscillations of random and unknown origins. In many codes of practice, the torsion about the vertical axis is considered through empirical accidental eccentricity formulation. Due to the random nature of structural systems, it is very difficult to evaluate the accidental eccentricity in a deterministic way and to specify its effect on the overall seismic response of structures. The aim of this study is to develop a procedure for the evaluation of the accidental eccentricity induced by uncertainties in stiffness and mass of structural members, using the neural network techniques coupled with Monte Carlo simulations. This method gives very interesting results for single story structures. For real structures, this method can be used as a tool to determine the accidental eccentricity in the seismic vulnerability studies of buildings.

키워드

참고문헌

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피인용 문헌

  1. Neural network-based prediction of ground time history responses 2017, https://doi.org/10.1080/19648189.2017.1367727
  2. Neural network based model for seismic assessment of existing RC buildings vol.12, pp.2, 2013, https://doi.org/10.12989/cac.2013.12.2.229
  3. Estimation of accidental eccentricities for multi-storey buildings using artificial neural networks vol.20, pp.5, 2012, https://doi.org/10.1007/s42107-019-00137-x