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.

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.

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