Extension of REBMIX algorithm to von Mises parametric family for modeling joint distribution of wind speed and direction
| Autor: | X.W. Ye, P.S. Xi, Marko Nagode |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
značilnosti polja vetra
REBMIX algoritem 0211 other engineering and technologies 020101 civil engineering Probability density function 02 engineering and technology mixture estimation spremljanja stanja strukture udc:519.2(045) ocenjevanje mešanih porazdelitev Wind speed 0201 civil engineering Joint probability distribution 021105 building & construction Expectation–maximization algorithm Civil and Structural Engineering Mathematics Weibull distribution wind field characteristics structural health monitoring Wind direction REBMIX algorithm Mixture model joint probability density function povezana gostota porazdelitve verjetnosti Akaike information criterion Algorithm |
| Zdroj: | Engineering structures, vol. 183, pp. 1134-1145, 2019. |
| ISSN: | 0141-0296 |
| Popis: | A statistical analysis of the wind speed and wind direction serves as a solid foundation for the wind-induced vibration analysis. The probabilistic modeling of wind speed and direction can effectively characterize the stochastic properties of wind field. The joint distribution model of wind speed and direction involves a circular distribution and has a multimodal characteristic. In this paper, the finite mixture distribution model is introduced and used to represent the joint distribution model that is comprised of the mixture Weibull distributions and von Mises distributions. An extended parameters estimation algorithm for multivariate and multimodal circular distributions is proposed to construct the joint distribution model. The proposed algorithm estimates the component parameters, mixture weight of each component and the number of components successively by an iterative process. The major improvement is accomplished by adding a circular distribution model. The effectiveness of the proposed algorithm is verified with numerical simulations and one-year field monitoring data and compared with the expectation maximization algorithm-based angular-linear approach in terms of the Akaike’s information criterion and computing time. The results indicate that the finite mixture model represents the joint distribution model of wind speed and direction well and that the proposed algorithm has a good and time-saving performance in parameter estimation for multivariate and multimodal models. |
| Databáze: | OpenAIRE |
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