Response Surface Method for Updating Dynamic Finite Element Models

Volume 2 ◽  
2004 ◽  
Author(s):  
Kun-Nan Chen ◽  
Cheng-Tien Chang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Response Surface ◽  
Damage Identification ◽  
Model Updating ◽  
Response Prediction ◽  
Element Model ◽  
General Purpose ◽  
Frame Structure ◽  
Fe Model

A finite element model of a structure can be updated as certain criteria based on experimental data are satisfied. The updated FE model is considered a better model for future studies in dynamic response prediction, structural modification, and damage identification. A finite element model updating technique incorporating the concept of response surface approximation (RSA) requires no sensitivity calculations and is much easier to implement with a general-purpose finite element code. The proposed updating method was incorporated with MSC. Nastran to solve the updating problem for an H-shaped frame structure. The updated results show that the predicted and experimental modes are correlated well with high MAC values and with a maximum frequency difference of 1.5%. Moreover, the updated parameters provide a physical insight to the modeling of bolted and welded joints of the H-frame structure.

Structural damage identification via physics-guided machine learning: a methodology integrating pattern recognition with finite element model updating

2020 ◽  
pp. 147592172092748 ◽  
Author(s):  
Zhiming Zhang ◽  
Chao Sun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
Damage Identification ◽  
Model Updating ◽  
Element Model ◽  
Data Driven ◽  
Finite Element Model Updating

Structural health monitoring methods are broadly classified into two categories: data-driven methods via statistical pattern recognition and physics-based methods through finite elementmodel updating. Data-driven structural health monitoring faces the challenge of data insufficiency that renders the learned model limited in identifying damage scenarios that are not contained in the training data. Model-based methods are susceptible to modeling error due to model idealizations and simplifications that make the finite element model updating results deviate from the truth. This study attempts to combine the merits of data-driven and physics-based structural health monitoring methods via physics-guided machine learning, expecting that the damage identification performance can be improved. Physics-guided machine learning uses observed feature data with correct labels as well as the physical model output of unlabeled instances. In this study, physics-guided machine learning is realized with a physics-guided neural network. The original modal-property based features are extended with the damage identification result of finite element model updating. A physics-based loss function is designed to evaluate the discrepancy between the neural network model output and that of finite element model updating. With the guidance from the scientific knowledge contained in finite element model updating, the learned neural network model has the potential to improve the generality and scientific consistency of the damage detection results. The proposed methodology is validated by a numerical case study on a steel pedestrian bridge model and an experimental study on a three-story building model.

Finite Element Model Updating Approach to Damage Identification in Beams Using Particle Swarm Optimization

2008 ◽  
Author(s):  
Mohamed M. Saada ◽  
Mustafa H. Arafa ◽  
Ashraf O. Nassef
Keyword(s):  
Finite Element ◽  
Particle Swarm Optimization ◽  
Finite Element Model ◽  
Natural Frequencies ◽  
Damage Identification ◽  
Model Updating ◽  
Particle Swarm ◽  
Element Model ◽  
Pso Algorithm ◽  
Swarm Optimization

The use of vibration-based techniques in damage identification has recently received considerable attention in many engineering disciplines. While various damage indicators have been proposed in the literature, those relying only on changes in the natural frequencies are quite appealing since these quantities can conveniently be acquired. Nevertheless, the use of natural frequencies in damage identification is faced with many obstacles, including insensitivity and non-uniqueness issues. The aim of this paper is to develop a viable damage identification scheme based only on changes in the natural frequencies and to attempt to overcome the challenges typically encountered. The proposed methodology relies on building a Finite Element Model (FEM) of the structure under investigation. A modified Particle Swarm Optimization (PSO) algorithm is proposed to facilitate updating the FEM in accordance with experimentally-determined natural frequencies in order to predict the damage location and extent. The method is tested on beam structures and was shown to be an effective tool for damage identification.

A Meta-Modeling Procedure for Updating the Finite Element Model of an Arch Bridge Model

2013 ◽  
Vol 540 ◽  
pp. 79-86
Author(s):  
De Jun Wang ◽  
Yang Liu
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Dynamic Analysis ◽  
Large Scale ◽  
Model Updating ◽  
Element Model ◽  
Fe Model ◽  
Large Scale Structures ◽  
Bridge Model ◽  
Scale Structures

Finite element (FE) model updating of structures using vibration test data has received considerable attentions in recent years due to its crucial role in fields ranging from establishing a reality-consistent structural model for dynamic analysis and control, to providing baseline model for damage identification in structural health monitoring. Model updating is to correct the analytical finite element model using test data to produce a refined one that better predict the dynamic behavior of structure. However, for real complex structures, conventional updating methods is difficult to be utilized to update the FE model of structures due to the heavy computational burden for the dynamic analysis. Meta-model is an effective surrogate model for dynamic analysis of large-scale structures. An updating method based on the combination between meta-model and component mode synthesis (CMS) is proposed to improve the efficiency of model updating of large-scale structures. The effectiveness of the proposed method is then validated by updating a scaled suspender arch bridge model using the simulated data.

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