Finite Element Model Updating Using Response Surface Method

TL;DR

This paper introduces a response surface method using neural networks for finite element model updating, significantly reducing computational time while maintaining accuracy compared to traditional optimization techniques.

Contribution

The paper presents a novel approach combining neural network-based response surface approximation with genetic algorithms for efficient finite element model updating.

Findings

Achieved accurate natural frequency and mode shape updates.

Reduced computational time by over 2.5 times compared to full finite element methods.

Performed well on an unsymmetrical H-shaped structure.

Abstract

This paper proposes the response surface method for finite element model updating. The response surface method is implemented by approximating the finite element model surface response equation by a multi-layer perceptron. The updated parameters of the finite element model were calculated using genetic algorithm by optimizing the surface response equation. The proposed method was compared to the existing methods that use simulated annealing or genetic algorithm together with a full finite element model for finite element model updating. The proposed method was tested on an unsymmetri-cal H-shaped structure. It was observed that the proposed method gave the updated natural frequen-cies and mode shapes that were of the same order of accuracy as those given by simulated annealing and genetic algorithm. Furthermore, it was observed that the response surface method achieved these results at a computational speed that was more than 2.5 times as fast as the genetic algorithm and a full finite element model and 24 times faster than the simulated annealing.