Fatigue lifespan assessment of stay cables by a refined joint probability density model of wind speed and direction

2021 ◽  
pp. 113608
Author(s):  
Xiaodong Liu ◽  
Wanshui Han ◽  
Xuelian Guo ◽  
Yangguang Yuan ◽  
Shizhi Chen
Keyword(s):  
Wind Speed ◽  
Probability Density ◽  
Joint Probability ◽  
Density Model ◽  
Stay Cables ◽  
Joint Probability Density

scholarly journals Modeling of Future Extreme Storm Surges at the NW Mediterranean Coast (Spain)

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 472
Author(s):  
Jue Lin-Ye ◽  
Manuel García-León ◽  
Vicente Gràcia ◽  
María Ortego ◽  
Piero Lionello ◽  
...  
Keyword(s):  
Probability Density ◽  
Large Scale ◽  
Joint Probability ◽  
Storm Surges ◽  
Additive Models ◽  
Change Scenario ◽  
Event Duration ◽  
Extreme Storm ◽  
Climate Patterns ◽  
Joint Probability Density

Storm surges are one of the main drivers for extreme flooding at the coastal areas. Such events can be characterized with the maximum level in an extreme storm surge event (surge peak), as well as the duration of the event. Surge projections come from a barotropic model for the 1950–2100 period, under a severe climate change scenario (RCP 8.5) at the northeastern Spanish coast. The relationship of extreme storm surges to three large-scale climate patterns was assessed: North Atlantic Oscillation ( N A O ), East Atlantic Pattern ( E A W R ), and Scandinavian Pattern ( S C ). The statistical model was built using two different strategies. In Strategy #1, the joint probability density was characterized by a moving-average series of stationary Archimedean copula, whereas in Strategy #2, the joint probability density was characterized by a non-stationary probit copula. The parameters of the marginal distribution and the copula were defined with generalized additive models. The analysis showed that the mean values of surge peak and event duration were constant and were independent of the proposed climate patterns. However, the values of N A O and S C influenced the threshold and the storminess of extreme events. According to Strategy #1, the variance of the surge peak and event duration increased with a fast shift of negative S C and a positive N A O , respectively. Alternatively, Strategy #2 showed that the variance of the surge peak increased with a positive E A W R . Both strategies coincided in that the joint dependence of the maximum surge level and the duration of extreme surges ranged from low to medium degree. Its mean value was stationary, and its variability was linked to the geographical location. Finally, Strategy #2 helped determine that this dependence increased with negative N A O .

scholarly journals Relationship Between Kendall's tau Correlation and Mutual Information

2020 ◽  
Vol 43 (1) ◽  
pp. 3-20
Author(s):  
Mohammad Bolbolian Ghalibaf
Keyword(s):  
Probability Density Function ◽  
Mutual Information ◽  
Probability Density ◽  
Density Function ◽  
Joint Probability ◽  
Joint Probability Density Function ◽  
Kendall’S Tau ◽  
Kendall's Tau ◽  
Joint Histogram ◽  
Joint Probability Density

Mutual information (MI) can be viewed as a measure of multivariate association in a random vector. However, the estimation of MI is difficult since the estimation of the joint probability density function (PDF) of non Gaussian distributed data is a hard problem. Copula function is an appropriate tool for estimating MI since the joint probability density function ofrandom variables can be expressed as the product of the associated copula density function and marginal PDF’s. With a little search, we find that the proposed copulas-based mutual information is much more accurate than conventional methods such as the joint histogram and Parzen window-based MI. In this paper, by using the copulas-based method, we compute MI forsome family of bivariate distribution functions and study the relationship between Kendall’s tau correlation and MI of bivariate distributions. Finally, using a real dataset, we illustrate the efficiency of this approach.

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