Resonance analysis of a high-speed railway bridge using a stochastic finite element method

There is always some randomness in the material properties of a structure due to several circumstances and ignoring it increases the threat of inadequate structural safety reserves.A numerical approach is used in this study to consider the spatial variability of structural parameters.Statistical moments of the train and bridge responses were computed using the point estimation method(PEM),and the material characteristics of the bridge were set as random fields following Gaussian random distribution,which were discretized using Karhunen-Loève expansion(KLE).The following steps were carried out and the results are discussed herein.First,using the stochastic finite element method(SFEM),the mean value and standard deviation of dynamic responses of the train-bridge system(TBS)were examined.The effectiveness and accuracy of the computation were then confirmed by comparing the results to the Monte-Carlo simulation(MCS).Next,the influence of the train running speed,bridge vibration frequency,and span of the bridge on dynamic coefficient and dynamic response characteristics of resonance were discussed by using the SFEM.Finally,the lowest limit value of the vibration frequency of the simple supported bridges(SSB)with spans of 24 m,32 m,and 40 m are presented.

Determination of Random Periodic Structures in Transverse Magnetic Polarization

Consider an inverse problem that aims to identify key statistical pro-perties of the profile for the unknown random perfectly conducting grating structure by boundary measurements of the diffracted fields in transverse mag-netic polarization.The method proposed in this paper is based on a novel combination of the Monte Carlo technique,a continuation method and the Karhunen-Loève expansion for the uncertainty quantification of the random structure.Numerical results are presented to demonstrate the effectiveness of the proposed method.

Rank-Based Test for Partial Functional Linear Regression Models

This paper investigates the hypothesis test of the parametric component in partial functional linear regression models.Based on a rank score function,the authors develop a rank test using functional principal component analysis,and establish the asymptotic properties of the resulting test under null and local alternative hypotheses.A simulation study shows that the proposed test procedure has good size and power with finite sample sizes.The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids.

A Numerical Comparison Between Quasi-Monte Carlo and Sparse Grid Stochastic Collocation Methods

Quasi-Monte Carlo methods and stochastic collocation methods based on sparse grids have become popular with solving stochastic partial differential equations.These methods use deterministic points for multi-dimensional integration or interpolation without suffering from the curse of dimensionality.It is not evident which method is best,specially on random models of physical phenomena.We numerically study the error of quasi-Monte Carlo and sparse gridmethods in the context of groundwater flow in heterogeneous media.In particular,we consider the dependence of the variance error on the stochastic dimension and the number of samples/collocation points for steady flow problems in which the hydraulic conductivity is a lognormal process.The suitability of each technique is identified in terms of computational cost and error tolerance.

On a Perturbation Method for Stochastic Parabolic PDE

In this article,we address two issues related to the perturbation method introduced by Zhang and Lu(J Comput Phys 194:773-794,2004),and applied to solving linear stochastic parabolic PDE.Those issues are the construction of the perturbation series,and its convergence.