Water for future (물과 미래)

Korea Water Resources Association (한국수자원학회)
권호 표지

대설 취약 구조물의 구조신뢰성 기반 피해 예측과 활용 방안

  • 김동수 (서울대학교 지역시스템공학과) ;
  • 이상익 (서울대학교 글로벌 스마트팜 혁신인재양성 교육연구단/융합전공 글로벌 스마트팜 ) ;
  • 최원 (서울대학교 조경.지역시스템공학부 농업생명과학연구원 융합전공 글로벌 스마트팜 )
  • Published : 2024.01.31
Download PDF
⟨ Previous Next ⟩

Abstract

Keywords

References

  1. Finn, C., Abbeel, P., & Levine, S. (2017, July). Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning (pp. 1126-1135). PMLR.
  2. Jiang, C., Qiu, H., Yang, Z., Chen, L., Gao, L., & Li, P. (2019). A general failure-pursuing sampling framework for surrogate-based reliability analysis. Reliability Engineering & System Safety, 183, 47-59. https://doi.org/10.1016/j.ress.2018.11.002
  3. Kim, C., & Choi, K. K. (2008). Reliability-based design optimization using response surface method with prediction interval estimation. Journal of Mechanical Design, 130(2): 121401.
  4. Qin, Y., Zhang, Y., Liu, Z., Zhu, X., & Wang, P. (2021). Adaptive surrogate model for failure probability estimation. In The 5th International Conference on Computer Science and Application Engineering (pp. 1-11).
  5. Ren, C., Aoues, Y., Lemosse, D., & De Cursi, E. S. (2022). Ensemble of surrogates combining Kriging and Artificial Neural Networks for reliability analysis with local goodness measurement. Structural Safety, 96: 102186.
  6. Xiang, Z., Chen, J., Bao, Y., & Li, H. (2020). An active learning method combining deep neural network and weighted sampling for structural reliability analysis. Mechanical Systems and Signal Processing, 140: 106684.
  7. Xiao, N. C., Zhan, H., & Yuan, K. (2020). A new reliability method for small failure probability problems by combining the adaptive importance sampling and surrogate models. Computer Methods in Applied Mechanics and Engineering, 372: 113336.