Finite Element Model Updating in Bridge Structures Using Kriging Model and Latin Hypercube Sampling Method

Jie Wu,Quansheng Yan,Jintu Zhong,Shiping Huang,Weifeng Wang,Chao Zou

doi:10.1155/2018/8980756

Abstract

Computational cost reduction and best model updating method seeking are the key issues during model updating for different kinds of bridges. This paper presents a combined method, Kriging model and Latin hypercube sampling method, for finite element (FE) model updating. For FE model updating, the Kriging model is serving as a surrogate model, and it is a linear unbiased minimum variance estimation to the known data in a region which have similar features. To predict the relationship between the structural parameters and responses, samples are preselected, and then Latin hypercube sampling (LHS) method is applied. To verify the proposed algorithm, a truss bridge and an arch bridge are analyzed. Compared to the predicted results obtained by using a genetic algorithm, the proposed method can reduce the computational time without losing the accuracy.

Highlights

Bridge structures are playing important roles in our lives for supporting essential social and economic functionalities
In the literature [39], finite element (FE) model updating is based on genetic algorithm (GA), and the Kriging model is applied to the structure. e procedure based on GA is given as follows, and a flowchart of the method is shown in Figure 3: (1) Establish the FE model through ANSYS [40] FE model analysis program. ere are 28 nodes, each node has two degrees of freedom (DOF), with 53 DOFs in total
A dynamic model updating procedure for bridges based on the Kriging model is presented. e Kriging model was established based on Latin hypercube sampling (LHS), and the effectiveness of the Kriging model method was investigated in two typical bridges

Summary

Introduction

Bridge structures are playing important roles in our lives for supporting essential social and economic functionalities. Compared to the direct method, parametric methods are widely used by selecting specific parameters to update the FE model to obtain the minimum error between the updated model and experimental data For complex structures, such as cable-stayed bridges, suspension bridges, and the other composite bridges, the number of nodes, elements, materials, and boundary conditions are complex. Marwala [11] proposed the response surface method for FE model updating, using genetic algorithm (GA) to optimize the selected parameters and verified the computational efficiency of the proposed method by an unsymmetrical H-shaped structure. Compared to genetic algorithm (GA), the model updating results of two cases showed that the proposed method has significant improvement to reduce the calculation time without losing the accuracy

Methodology

Experimental Verifications

Design parameter

Findings

Conclusions