EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 248 (2021) E3S Web Conf., 248 (2021) 01023 References
Open Access
Issue
E3S Web Conf.
Volume 248, 2021
2021 3rd International Conference on Civil Architecture and Energy Science (CAES 2021)
Article Number 01023
Number of page(s) 6
Section Chemical Performance Structure Research and Environmental Pollution Control
DOI https://doi.org/10.1051/e3sconf/202124801023
Published online 12 April 2021
  1. Writing group, Guide for wind resistance design of Highway Bridges, People’s Communications Press, Beijing, China, (1996). [Google Scholar]
  2. E. SIMIU, R.H. SCANLAN. Effect of wind on structure. Liu Shangpei, Xiang Haifan and Xie Jiming, translation. Shanghai: Tongji University Press, (1992). [Google Scholar]
  3. TADELE AKEBA DIRIBA et al. Modelling dependence structures of extreme wind speed using bivariate distribution: a bayesian approach. Environmental and Ecological Statistics, 27:319–362, (2020). [Google Scholar]
  4. B. YAO, Y. QUAN, Study on the analysis method of extreme wind speed in mixed climate areas. Engineering Mechanics, 35(5) :86–92, (2018). [Google Scholar]
  5. Q. Li, Extreme Wind Speed Estimation in mixed wind climates and study on analysis models of wind-induced responses of a tall building, PhD Thesis, Zhejiang University, (2018). [Google Scholar]
  6. C. Lou, F. Dong, Research on extreme wind speed forecasting method of buildingstructure based on Bayesian theory, 46(s1) :986–988, (2016). [Google Scholar]
  7. Y. Ge, Research on wind-induced reliability and its application of bridge structure, Tongji university, Shanghai, (1998). [Google Scholar]
  8. H.C.S THOM, Distributions of extreme winds in the united states. Journal of the Structural Division, Proceedings of the ASCE, 86 (4):11–24 (1960). [Google Scholar]
  9. E. SIMIU, J. CHANGERYM, J.J. FILLIBEN. Extreme wind speeds at 129 airport station. Journal of the Structural Division, 106 (4): 809~817, (1980). [Google Scholar]
  10. F. Dong, J. Cheng, Wind speed prediction of extreme value type I distribution basedon the Bayes method, Journal of Harbin institute of technology, 49 (3):93–97, (2017). [Google Scholar]
  11. B. ZHANG, Y. QUAN, M. GU, A probabilistic method for estimating extreme wind pressure onstructures considering direction effect of extreme wind speed, 39 (4):1–7, (2018). [Google Scholar]
  12. Z. Duan, J. Ou, D. Zhou, The optimal probabilistic distribution for extreme wind speed, China civil engineering journal, 35(5):11–16, (2002). [Google Scholar]
  13. J. Deng, D. Gu, X. Li, Parameters and quantile estimation for fatigue life distributionusing probability weighted moments, 21(5) :609–613, (2004). [Google Scholar]
  14. A.A. Bartolucci, K.P. Singh, A. D. Bartolucc, et al., Applying medical survival data to estimate the three-parameter Weibull distribution by the method of probability weighted moments. Mathematics and Computers in Simulation, 48(4–6): 385–392, (1999). [Google Scholar]
  15. K. E. AHMAD. Modified weighted least-squares estimators for the three-parameter weibull distribution, Applied Mathematics Letters, 7(5) :53–56, (1994). [Google Scholar]
  16. M. Fu, Z. Cao, An optimization method of correlation coefficient for determining a three-parameter Weibull distribution, 11(7): A323-A327, (1990). [Google Scholar]
  17. H. Fei, D. Chen, Parameter Estimation of the Three-Parameter Weibull Distribution, Journal of Shanghai Normal University, 25(2) :1–8, (1996). [Google Scholar]
  18. H.Z. QIAO, C.P. TSOKOS, Estimation of the three parameter Weibull probability distribution. Mathematics and Computers in Simulation, 39(1–2):173–185, (1995). [Google Scholar]
  19. J.H. GOVE, S.E. FAIRWEATHER, Maximum-likelihood estimation of Weibull function parameters using a general interactive optimizer and grouped data. Forest Ecology and Management, 28(1) :61–69, (1989). [Google Scholar]
  20. Y. Qu, C. Zhang, S. Yan, Estimating the parameters for the three-parameter Weibull distribution, 45(3) :374–375, (1987). [Google Scholar]
  21. H. Wang, Z. Li, R. Lin, Maximum likelihood estimation for three-parameter Weibull distribution based on wear out fault, 25(5): 39–42, (2004). [Google Scholar]
  22. JRM. HOSKING, J.R. WALLIS, E.F. WOOD, Estimation of the generalized extreme value distribution by the method of probability-weighted moments. Technometrics, 27(3): 251–261, (1985). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.