Substructural Damage Detection With Incomplete Information of the Structure

2012 ◽  
Vol 79 (4) ◽  
Author(s):  
J. Li ◽  
S. S. Law
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Degrees Of Freedom ◽  
Damage Identification ◽  
Model Updating ◽  
Three Dimensional ◽  
Measurement Data ◽  
Impulse Response Function ◽  
Element Model ◽  
Reconstruction Technique

This paper proposes a substructural damage identification approach without the information of responses and forces at the interface degrees-of-freedom. It is based on the response reconstruction technique using the unit impulse response function in the wavelet domain. The finite element model of the target substructure and acceleration measurement data from the damaged substructure are required in the identification. A dynamic response sensitivity-based method is used for the substructural finite element model updating, and local damage is identified as a change in the elemental stiffness factors. The adaptive Tikhonov regularization technique is adopted to improve the identification results with the measurement noise effect. Numerical studies on a three-dimensional box-section girder are conducted to validate the proposed method of substructural damage identification. The simulated damage can be identified effectively even with 10% noise in the measurements and a 5% coefficient of variation in the elastic modulus of material of the 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.

scholarly journals Finite element model updating using Lagrange interpolation

2019 ◽  
Vol 23 (1) ◽  
pp. 228-232
Author(s):  
F. Asma
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Degrees Of Freedom ◽  
Model Updating ◽  
Element Model ◽  
Truss Structures ◽  
Finite Element Model Updating ◽  
Initial Model ◽  
Information Matrices ◽  
Matrix Interpolation

Abstract In this paper, an iterative finite element model updating method in structural dynamics is proposed. This uses information matrices and element connectivity matrices to reconstruct the corrected model by reproducing the frequency response at measured degrees of freedom. Indicators have been proposed to quantify the mismodelling errors based on a development in Lagrange matrix interpolation. When applied on simulated truss structures, the model gives satisfactory results by detecting and quantifying the defaults of the initial model.

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