Prediction of extreme value distribution of directional wind speeds and its application to reliability analysis of long-span bridges under strong wind.

The seismic behaviour of high-pier and long-span bridges in fault zones is significantly influenced by the near-fault impulsive effect. In view of this, this study evaluates the extreme value distribution and dynamic reliability of high-pier bridges built in fault zones. First, based on the improved maximum entropy method constrained with fractional moments, a new method is proposed to evaluate the extreme value distribution of nonlinear structure seismic response. In the proposed method, the fractional moments are evaluated by the Latin hypercube sampling, and a linear system of equations that determine the initial value of fractional exponents and Lagrange multipliers is illustrated to improve the stability of the simplex algorithm. Second, based on the obtained extreme value distribution, the seismic reliability of structure is estimated by the simple numerical integration. Two numerical examples, including a nonlinear single-degree-of-freedom system and a three-storey nonlinear shear frame, are adopted to validate the proposed method. Finally, the seismic reliability of a typical high-pier and long-span continuous rigid frame bridge located in southwest of China is evaluated by the proposed method and some critical conclusions are drawn. The results obtained from this study not only indicate the accuracy and efficiency of the proposed method but also can provide some direct guidelines for the seismic design of the high-pier and long-span bridges in fault zones.

Download Full-text

The extreme value distribution and dynamic reliability analysis of nonlinear structures with uncertain parameters

Structural Safety ◽

10.1016/j.strusafe.2006.02.002 ◽

2007 ◽

Vol 29 (2) ◽

pp. 77-93 ◽

Cited By ~ 148

Author(s):

Jian-Bing Chen ◽

Jie Li

Keyword(s):

Reliability Analysis ◽

Extreme Value Distribution ◽

Extreme Value ◽

Value Distribution ◽

Uncertain Parameters ◽

Nonlinear Structures ◽

Dynamic Reliability

Download Full-text

Statistics of Extreme Wind Speeds and Wave Heights by the Bivariate ACER Method

Journal of Offshore Mechanics and Arctic Engineering ◽

10.1115/1.4029370 ◽

2015 ◽

Vol 137 (2) ◽

Cited By ~ 3

Author(s):

Arvid Naess ◽

Oleh Karpa

Keyword(s):

Time Series ◽

Extreme Value Distribution ◽

Offshore Structures ◽

Accurate Estimate ◽

Value Theory ◽

Extreme Value ◽

Value Distribution ◽

Extreme Value Statistics ◽

Wind Speeds ◽

Wave Heights

In the reliability engineering and design of offshore structures, probabilistic approaches are frequently adopted. They require the estimation of extreme quantiles of oceanographic data based on the statistical information. Due to strong correlation between such random variables as, e.g., wave heights and wind speeds (WS), application of the multivariate, or bivariate in the simplest case, extreme value theory is sometimes necessary. The paper focuses on the extension of the average conditional exceedance rate (ACER) method for prediction of extreme value statistics to the case of bivariate time series. Using the ACER method, it is possible to provide an accurate estimate of the extreme value distribution of a univariate time series. This is obtained by introducing a cascade of conditioning approximations to the true extreme value distribution. When it has been ascertained that this cascade has converged, an estimate of the extreme value distribution has been obtained. In this paper, it will be shown how the univariate ACER method can be extended in a natural way to also cover the case of bivariate data. Application of the bivariate ACER method will be demonstrated for measured coupled WS and wave height data.

Download Full-text

Statistical Analysis of Ocean Environmental Conditions With PTGEVD

23rd International Conference on Offshore Mechanics and Arctic Engineering, Volume 2 ◽

10.1115/omae2004-51615 ◽

2004 ◽

Author(s):

Sheng Dong ◽

Xiaoli Hao

Keyword(s):

Environmental Conditions ◽

Oil And Gas ◽

Design Method ◽

Discrete Distribution ◽

Extreme Value Distribution ◽

Offshore Structures ◽

Extreme Value ◽

Value Distribution ◽

Design Criteria ◽

Wind Speeds

Poisson Trivariate Gumbel Extreme Value Distribution (PTGEVD), a multivariate from of the Compound Extreme Value Distribution, is presented to solve for the ocean environmental design criteria in this paper. The proposed model is combined with a discrete distribution of storm frequency and a continuous trivariate extreme value distribution of environmental conditions simultaneously occurred in storm processes. Different from traditional univariate design method, the proposed design method with PTGEVD can reflect the combined effect of multi-loads on offshore structures and result in reasonable reduction of the design criteria. Validated with the synchronically measured significant wave heights, wind speeds and current velocities of 20 typhoon processes, PTGEVD model shows that it is easy to be applied and has considerable economic potential in the exploitation of ocean oil and gas, especially for marginal field.

Download Full-text

The Dispersion Estimation Method for Assembly Stress of Silicone Rubber Foam Pad Based on Extreme Value Distribution

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.387.85 ◽

2013 ◽

Vol 387 ◽

pp. 85-89 ◽

Cited By ~ 1

Author(s):

Fei Wang ◽

Fang Mei Wan

Keyword(s):

Stress Distribution ◽

Reliability Analysis ◽

Silicone Rubber ◽

Estimation Method ◽

Extreme Value Distribution ◽

Simulation Method ◽

Extreme Value ◽

Value Distribution ◽

Suitable Material ◽

Random Simulation

The silicone rubber foam is a suitable material used for heat insulation and vibration reduction. Because of dispersion of its mechanical character, it is difficult to quantify the assemble stress in reliability analysis. Based on reduced hyper-foam Ogden Model, the interval of model parameter is obtained through fitting the test data, and the predicted assemble stress distribution is also achieved by Monte-Carlo random simulation method. Then, the regression analysis of predicted data estimated is introduced by the extreme value distribution. The predicted assembly stress distribution described in this paper achieves high confidence by the extreme value distribution. Thus, combined with strain uncertain quantification, the reliability analysis of assemble stress can be performed.

Download Full-text

Statistics of Extreme Wind Speeds and Wave Heights by the Bivariate ACER Method

Volume 2A: Structures, Safety and Reliability ◽

10.1115/omae2013-10760 ◽

2013 ◽

Cited By ~ 1

Author(s):

Arvid Naess ◽

Oleh Karpa

Keyword(s):

Time Series ◽

Extreme Value Distribution ◽

Offshore Structures ◽

Value Theory ◽

Extreme Value ◽

Value Distribution ◽

Extreme Value Statistics ◽

Wind Speeds ◽

Wave Heights ◽

Extreme Wind Speeds

In the reliability engineering and design of offshore structures probabilistic approaches are frequently adopted. They require the estimation of extreme quantiles of oceanographic data based on the statistical information. Due to strong correlation between such random variables as e.g. wave heights and wind speeds, application of the multivariate, or bivariate in the simplest case, extreme value theory is sometimes necessary. The paper focuses on the extension of the ACER method for prediction of extreme value statistics to the case of bivariate time series. Using the ACER method it is possible to provide an estimate of the exact extreme value distribution of a univariate time series. This is obtained by introducing a cascade of conditioning approximations to the exact extreme value distribution. When this cascade has converged, an estimate of the exact distribution has been obtained. In this paper it will be shown how the univariate ACER method can be extended in a natural way to also cover the case of bivariate data. Application of the bivariate ACER method will also be demonstrated at the measured coupled wind speed and wave height data.

Download Full-text