Open Access
| Issue |
E3S Web Conf.
Volume 675, 2025
International Scientific Conference on Geosciences and Environmental Management (GeoME’5.5 2025)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 10 | |
| Section | Artificial Intelligence and Smart Modeling for Resilient Civil Infrastructure and Environmental Systems | |
| DOI | https://doi.org/10.1051/e3sconf/202567503009 | |
| Published online | 11 December 2025 | |
- R. Rackwitz; B. Flessler. Structural reliability under combined random load sequences. Computers & Structures. 9 (5). 489–94. (1978). https://doi.org/10.1016/0045-7949(78)90046-9 [Google Scholar]
- F. Lindsten; T. B. Schön. Backward Simulation Methods for Monte Carlo Statistical Inference. MAL. 6 (1). 1–143. (2013). https://doi.org/10.1561/2200000045 [Google Scholar]
- K. Mouzoun; A. Bouyahyaoui; H. Abdelali; T. Cherradi; K. Baba; I. Masrour; et al. How machine learning can transform the future of concrete. Asian Journal of Civil Engineering. 26. 1395–411. (2025). https://doi.org/10.1007/s42107-025-01281-3 [Google Scholar]
- J.-M. Bourinet. Rare-event probability estimation with adaptive support vector regression surrogates. Reliab. Eng. Syst. Saf. 150. 210–21. (2016). https://doi.org/10.1016/j.ress.2016.01.023 [Google Scholar]
- L. Cao; S. G. Gong; Y. R. Tao; S. Y. Duan. A RBFNN based active learning surrogate model for evaluating low failure probability in reliability analysis. Probabilistic Eng Mech. 74 (103496). (2023). https://doi.org/10.1016/j.probengmech.2023.103496 [Google Scholar]
- B. Echard; N. Gayton; M. Lemaire. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation. Structural Safety. 33 (2). 145–54. (2011). https://doi.org/10.1016/j.strusafe.2011.01.002 [Google Scholar]
- J. B. Cardoso; J. R. de Almeida; J. M. Dias; P. G. Coelho. Structural reliability analysis using Monte Carlo simulation and neural networks. Advances in Engineering Software. 39 (6). 505–13. (2008). https://doi.org/10.1016/j.advengsoft.2007.03.015 [Google Scholar]
- B. Sudret; S. Marelli; J. Wiart. Surrogate models for uncertainty quantification: An overview. 2017 11th European Conference on Antennas and Propagation (EUCAP). 793– 7. (2017). https://doi.org/10.23919/EuCAP.2017.7928679 [Google Scholar]
- W. Fauriat; N. Gayton. AK-SYS: An adaptation of the AK-MCS method for system reliability. Reliability Engineering & System Safety. 123. 137–44. (2014). https://doi.org/10.1016/j.ress.2013.10.010 [Google Scholar]
- E. H.C. F; Y. R; M. H; H. C. F. Time-dependent reliability analysis for a set of RC T- beam bridges under realistic traffic considering creep and shrinkage. Eur J Environ Civ Eng. 26. 6480–504. (2022). https://doi.org/10.1080/19648189.2021.1946720 [Google Scholar]
- F. Li; J. Liu; Y. Yan; J. Rong; J. Yi; G. Wen. A time-variant reliability analysis method for non-linear limit-state functions with the mixture of random and interval variables. Eng Struct. 213 (110588). (2020). https://doi.org/10.1016/j.engstruct.2020.110588 [Google Scholar]
- N. Zemed; H. M. Abdelali; T. Cherradi; A. Bouyahyaoui; K. Mouzoun. Time-Dependent Reliability and Sensitivity Analysis of Reinforced Concrete Bridges Considering Creep, Shrinkage, and Evolving Traffic Using Active Learning. Journal of Bridge Engineering. 31 (1). 04025093. (2026). https://doi.org/10.1061/JBENF2.BEENG-7698 [Google Scholar]
- H. Hammoum; K. Bouzelha; Y. Sellam; L. Haddad. Structural reliability of elevated water reservoirs under wind loading. Procedia Structural Integrity. 22. 235–42. (2019). https://doi.org/10.1016/j.prostr.2020.01.030 [Google Scholar]
- A. C. Way; C. Viljoen. Reliability performance in tension governed reinforced concrete reservoirs as a function of target crack width. Structural Concrete. 24 (6). 7726–41. (2023). https://doi.org/10.1002/suco.202201159 [Google Scholar]
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