Stochastic dynamic analysis for vehicle-track-bridge system based on probability density evolution method

2019 ◽  
Vol 188 ◽  
pp. 745-761 ◽  
Author(s):  
Xiang Xiao ◽  
Yu Yan ◽  
Bo Chen
Keyword(s):  
Dynamic Analysis ◽  
Probability Density ◽  
Stochastic Dynamic ◽  
Density Evolution ◽  
Probability Density Evolution Method ◽  
Stochastic Dynamic Analysis ◽  
Track Bridge ◽  
Probability Density Evolution ◽  
Bridge System

A New Reliability Method Combining Kriging and Probability Density Evolution Method

2017 ◽  
Vol 17 (10) ◽  
pp. 1750113 ◽  
Author(s):  
Zhongming Jiang ◽  
Jie Li
Keyword(s):  
Probability Density ◽  
New Method ◽  
Stochastic Dynamic ◽  
Kriging Method ◽  
Density Evolution ◽  
Stochastic Response ◽  
Probability Density Evolution Method ◽  
Numerical Precision ◽  
Stochastic Dynamic Systems ◽  
Probability Density Evolution

Stochastic dynamic analysis of structures with random parameters continues to be an open question in the field of civil engineering. As a newly developed method, the probability density evolution method (PDEM) can provide the probability density function (PDF) of the dynamic responses of highly nonlinear structures. In this paper, a new method based on PDEM and the kriging surrogate model, named the K-PDEM, is proposed to study the stochastic response of a structure. Being an exact interpolation method, the Gaussian process regression or the so-called kriging method is capable of producing highly accurate results. Unlike the traditional PDEM numerical method whose numerical precision is strongly influenced by the number of representative points, the K-PDEM employs the kriging method at each instant to generate additional time histories. Then, the PDEM, which is capable of capturing the instantaneous PDF of a dynamic response and its evolution, is employed in nonlinear stochastic dynamic systems. Because of the decoupling properties of the K-PDEM, the numerical precision of the result is improved by the enrichment of the generalized density evolution equations without increasing the computation time. The result shows that the new method is capable of calculating the stochastic response of structures with efficiency and accuracy.

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