Uncertain Dynamic Characteristic Analysis for Structures with Spatially Dependent Random System Parameters

Abstract

This work presents a robust non-deterministic free vibration analysis for engineering structures with random field parameters in the frame of stochastic finite element method. For this, considering the randomness and spatial correlation of structural physical parameters, a parameter setting model based on random field theory is proposed to represent the random uncertainty of parameters, and the stochastic dynamic characteristics of different structural systems are then analyzed by incorporating the presented parameter setting model with finite element method. First, Gauss random field theory is used to describe the uncertainty of structural material parameters, the random parameters are then characterized as the standard deviation and correlation length of the random field, and the random field parameters are then discretized with the Karhunen–Loeve expansion method. Moreover, based on the discretized random parameters and finite element method, structural dynamic characteristics analysis is addressed, and the probability distribution density function of the random natural frequency is estimated based on multi-dimensional kernel density estimation method. Finally, the random field parameters of the structures are quantified by using the maximum likelihood estimation method to verify the effectiveness of the proposed method and the applicability of the constructed model. The results indicate that (1) for the perspective of maximum likelihood estimation, the parameter setting at the maximum value point is highly similar to the input parameters; (2) the random field considering more parameters reflects a more realistic structure.

Stochastic Finite Element Analysis of Plate Structures Considering Spatial Parameter Random Fields

“…On the random field problem of structures, numerous scholars have conducted a variety of investigations. Du et al [6] analyzed the structural dynamic analysis considering discrete random parameters and the finite element method (FEM), and used a multi-dimensional kernel density estimation approach to estimate the probability density function (PDF) of random natural frequency, and this strategy can solve the robust nondeterministic free vibration problem. For predicting the structural horizontal strength of composite laminates, Nastos et al [7] developed a deep learning algorithm based on convolutional neural networks and trained it using the datasets from probabilistic failure analysis generated by SFEM.…”