Finite Element Model Updating Using Bayesian Approach

TL;DR

This paper compares Bayesian and Maximum-likelihood methods for finite element model updating, demonstrating Bayesian's superior accuracy in predicting modal properties with similar computational effort.

Contribution

It introduces a Bayesian approach using Markov Chain Monte Carlo for finite element model updating and compares it with the traditional Maximum-likelihood method.

Findings

Bayesian method yields more accurate modal property predictions.

Both methods require similar computational resources.

Bayesian approach is effective for finite element model updating.

Abstract

This paper compares the Maximum-likelihood method and Bayesian method for finite element model updating. The Maximum-likelihood method was implemented using genetic algorithm while the Bayesian method was implemented using the Markov Chain Monte Carlo. These methods were tested on a simple beam and an unsymmetrical H-shaped structure. The results show that the Bayesian method gave updated finite element models that predicted more accurate modal properties than the updated finite element models obtained through the use of the Maximum-likelihood method. Furthermore, both these methods were found to require the same levels of computational loads.