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Efficient slope reliability analysis under soil spatial variability using maximum entropy distribution with fractional moments

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Journal of Rock Mechanics and Geotechnical Engineering 2024年第4期16卷 1140-1152页

作者： Chengxin Feng Marcos A.Valdebenito Marcin Chwała Kang Liao Matteo Broggi Michael Beer Institute for Risk and Reliability Leibniz University HannoverCallinstr.34Hannover30167Germany Chair for Reliability Engineering TU Dortmund UniversityLeonhard-Euler-Str.5Dortmund44227Germany Faculty of Civil Engineering Wrocƚaw University of Science and TechnologyWrocƚawPoland Faculty of Geosciences and Environmental Engineering Southwest Jiaotong UniversityChengdu610031China University of Liverpool Institute for Risk and UncertaintyPeach StreetLiverpoolL69 7ZFUnited Kingdom International Joint Research Center for Resilient Infrastructure & International Joint Research Center for Engineering Reliability and Stochastic Mechanics Tongji UniversityShanghaiChina

Spatial variability of soil properties imposes a challenge for practical analysis and design in geotechnical *** latter is particularly true for slope stability assessment,where the effects of uncertainty are synthesized in the so-called probability of *** probability quantifies the reliability of a slope and its numerical calculation is usually quite involved from a numerical *** view of this issue,this paper proposes an approach for failure probability assessment based on Latinized partially stratified sampling and maximum entropy distribution with fractional *** spatial variability of geotechnical properties is represented by means of random fields and the Karhunen-Loève ***,failure probabilities are estimated employing maximum entropy distribution with fractional *** application of the proposed approach is examined with two examples:a case study of an undrained slope and a case study of a slope with cross-correlated random fields of strength parameters under a drained *** results show that the proposed approach has excellent accuracy and high efficiency,and it can be applied straightforwardly to similar geotechnical engineering problems.

关键词： Slope Random field Reliability analysis maximum entropy distribution Latinized partial stratified sampling

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Application of maximum entropy distribution to the Statistical Properties of Wave Groups

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China Ocean Engineering 2007年第3期21卷 461-470页

作者：于定勇李晶刘华兴 College of Engineering Ocean University of China

The new distributions of the statistics of wave groups based on the maximum entropy principle are presented. The maximum entropy distributions appear to be superior to conventional distributions when applied to a limited amount of information. Its applications to the wave group properties show the effectiveness of the maximum entropy distribution. FFF filtering method is employed to obtain the wave envelope fast and efficiently. Comparisons of both the maximum entropy distribution and the distribution of Longuet-Higgins （1984） with the laboratory wind-wave data show that the former gives a better fit.

关键词： maximum entropy distribution high run length wave group length probability density function wave envelope

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Estimation of Design Sea Ice Thickness with maximum entropy distribution by Particle Swarm Optimization Method

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Journal of Ocean University of China 2016年第3期15卷 423-428页

作者： TAO Shanshan DONG Sheng WANG Zhifeng JIANG Wensheng College of Engineering Ocean University of China College of Environmental Science and Engineering Ocean University of China

The maximum entropy distribution, which consists of various recognized theoretical distributions, is a better curve to estimate the design thickness of sea ice. Method of moment and empirical curve fitting method are common-used parameter estimation methods for maximum entropy distribution. In this study, we propose to use the particle swarm optimization method as a new parameter estimation method for the maximum entropy distribution, which has the advantage to avoid deviation introduced by simplifications made in other methods. We conducted a case study to fit the hindcasted thickness of the sea ice in the Liaodong Bay of Bohai Sea using these three parameter-estimation methods for the maximum entropy distribution. All methods implemented in this study pass the K-S tests at 0.05 significant level. In terms of the average sum of deviation squares, the empirical curve fitting method provides the best fit for the original data, while the method of moment provides the worst. Among all three methods, the particle swarm optimization method predicts the largest thickness of the sea ice for a same return period. As a result, we recommend using the particle swarm optimization method for the maximum entropy distribution for offshore structures mainly influenced by the sea ice in winter, but using the empirical curve fitting method to reduce the cost in the design of temporary and economic buildings.

关键词： sea ice thickness maximum entropy distribution particle swarm optimization return period offshore structural de-sign

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