References
- KSSC, 2021. KSSC-21-02: Steel structure design. Goomibook. Korean Society of Steel Construction. Seoul, Korea.
- 국토교통부, 2016. 강구조 설계 일반사항 (허용응력설계법) KDS 14 30 05.
- 국토교통부, 2022. 건축구조기준 KDS 41 00 00.
- 농촌진흥청 국립농업과학원, 2015. 온실 구조설계기준(안). No. 11-1390802-001030-01.
- Hasofer, A. M., and N. C. Lind, 1974. Exact and invariant second-moment code format. Journal of the Engineering Mechanics Division, 100(1), 111-121.
- Der Kiureghian, A., H.-Z. Lin, and S.-J. Hwang, 1987. Second-order reliability approximations. Journal of Engineering Mechanics, 113(8), 1208-1225.
- Du, X., and A. Sudjianto, 2004. First order saddlepoint approximation for reliability analysis. AIAA Journal, 42(6), 1199-1207.
- Chojaczyk, A. A., A. P. Teixeira, L. C. Neves, J. B. Cardoso, and C. G. Soares, 2015. Review and application of artificial neural networks models in reliability analysis of steel structures. Structural Safety, 52, 78-89.
- Ang, G. L., A. H. Ang, and W. H. Tang, 1992. Optimal importance-sampling density estimator. Journal of Engineering Mechanics, 118(6), 1146-1163.
- Tokdar, S. T., and R. E. Kass, 2010. Importance sampling: A review. Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 54-60.
- Faravelli, L., 1989. Response-surface approach for reliability analysis. Journal of Engineering Mechanics, 115(12), 2763-2781.
- Romero, V., L. Swiler, and A. Giunta, 2004. Construction of response surfaces based on progressive-lattice-sampling experimental designs with application to uncertainty propagation. Structural Safety, 26(2), 201-219.
- Marelli, S., and B. Sudret, 2018. An active-learning algorithm that combines sparse polynomial chaos expansions and bootstrap for structural reliability analysis. Structural Safety, 75, 67-74.
- Xiang, Z., J. Chen, Y. Bao, and H. Li, 2020. An active learning method combining deep neural network and weighted sampling for structural reliability analysis. Mechanical Systems and Signal Processing, 140, 106684.
- Lieu, Q. X., K. T. Nguyen, K. D. Dang, S. Lee, J. Kang, and J. Lee, 2022. An adaptive surrogate model to structural reliability analysis using deep neural network. Expert Systems with Applications, 189, 116104.
- Efraimidis, P. S., and P. G. Spirakis, 2006. Weighted random sampling with a reservoir. Information Processing Letters, 97(5), 181-185.
- Schueremans, L., and D. Van Gemert, 2005. Benefit of splines and neural networks in simulation based structural reliability analysis. Structural Safety, 27(3), 246-261.
- Echard, B., N. Gayton, and M. Lemaire, 2011. Ak-mcs: An active learning reliability method combining kriging and monte carlo simulation. Structural Safety, 33(2), 145-154.