Experimental verifications of a structural damage identification technique using reduced order finite-element model

2010 ◽  
Author(s):  
Rui Li ◽  
Li Zhou ◽  
Jann N. Yang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Structural Damage ◽  
Damage Identification ◽  
Element Model ◽  
Reduced Order ◽  
Structural Damage Identification ◽  
Identification Technique

scholarly journals Sensitivity Analysis Using a Reduced Finite Element Model for Structural Damage Identification

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5514
Author(s):  
Qiuwei Yang ◽  
Xi Peng
Keyword(s):  
Sensitivity Analysis ◽  
Finite Element ◽  
Finite Element Model ◽  
Structural Damage ◽  
Damage Identification ◽  
Generalized Inverse ◽  
Linear Equations ◽  
Element Model ◽  
Eigenvalues And Eigenvectors ◽  
Structural Damage Identification

Sensitivity analysis is widely used in engineering fields, such as structural damage identification, model correction, and vibration control. In general, the existing sensitivity calculation formulas are derived from th,,,e complete finite element model, which requires a large amount of calculation for large-scale structures. In view of this, a fast sensitivity analysis algorithm based on the reduced finite element model is proposed in this paper. The basic idea of the proposed sensitivity analysis algorithm is to use a model reduction technique to avoid the complex calculation required in solving eigenvalues and eigenvectors by the complete model. Compared with the existing sensitivity calculation formulas, the proposed approach may increase efficiency, with a small loss of accuracy of sensitivity analysis. Using the fast sensitivity analysis, the linear equations for structural damage identification can be established to solve the desired elemental damage parameters. Moreover, a feedback-generalized inverse algorithm is proposed in this work in order to improve the calculation accuracy of damage identification. The core principle of this feedback operation is to reduce the number of unknowns, step by step, according to the generalized inverse solution. Numerical and experimental examples show that the fast sensitivity analysis based on the reduced model can obtain almost the same results as those obtained by the complete model for low eigenvalues and eigenvectors. The feedback-generalized inverse algorithm can effectively overcome the ill-posed problem of the linear equations and obtain accurate results of damage identification under data noise interference. The proposed method may be a very promising tool for sensitivity analysis and damage identification based on the reduced finite element model.

scholarly journals Structural Damage Identification Based on Transmissibility in Time Domain

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 393
Author(s):  
Yunfeng Zou ◽  
Xuandong Lu ◽  
Jinsong Yang ◽  
Tiantian Wang ◽  
Xuhui He
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Time Domain ◽  
Structural Damage ◽  
Damage Identification ◽  
Element Model ◽  
Time Frequency ◽  
Identification Method ◽  
Structural Damage Identification ◽  
The Time Domain

Structural damage identification technology is of great significance to improve the reliability and safety of civil structures and has attracted much attention in the study of structural health monitoring. In this paper, a novel structural damage identification method based on transmissibility in the time domain is proposed. The method takes the discrepancy of transmissibility of structure response in the time domain before and after damage as the basis of finite element model updating. The damage is located and quantified through iteration by minimizing the difference between the measurements at gauge locations and the reconstruction response extrapolated by the finite element model. Taking advantage of the response reconstruction method based on empirical mode decomposition, damage information can be obtained in the absence of prior knowledge on excitation. Moreover, this method directly collects time-domain data for identification without modal identification and frequent time–frequency conversion, which can greatly improve efficiency on the premise of ensuring accuracy. A numerical example is used to demonstrate the overall damage identification method, and the study of measurement noise shows that the method has strong robustness. Finally, the present work investigates the method through a simply supported overhanging beam. The experiments collect the vibration strain signals of the beam via resistance strain gauges. The comparison between identification results and theoretical values shows the effectiveness and accuracy of the method.

Structural damage identification using frequency and modal changes

2011 ◽  
Vol 59 (1) ◽  
pp. 27-32 ◽  
Author(s):  
K. Dems ◽  
J. Turant
Keyword(s):  
Finite Element ◽  
Vibration Frequency ◽  
Structural Damage ◽  
Damage Identification ◽  
Structural Response ◽  
Element Model ◽  
Identification Procedure ◽  
Proper Distance ◽  
Structural Damage Identification ◽  
Local Defects

Structural damage identification using frequency and modal changesThe problem of identification of a structural damage is considered. The identification of location and/or dimensions of a damaged area or local defects and inclusions is performed using the measurements of vibration frequency and eigenvalues of real structure and the corresponding finite element model. The proper distance norms between the measured and calculated structural response are introduced and minimized during the identification procedure.

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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