Civil Structure Damage Identification Method Based on Finite Element Model Technology

2022 ◽  
pp. 113-121
Author(s):  
Jia Li ◽  
Zhiyuan Yu ◽  
Xuezhi Zhang ◽  
Yijiang Wang
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Identification ◽  
Element Model ◽  
Structure Damage ◽  
Identification Method

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.

scholarly journals Damage Identification of High-speed Maglev Guideway Girder Based on Modal Identification

2021 ◽  
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Health Monitoring ◽  
High Speed ◽  
Damage Identification ◽  
Element Model ◽  
High Tech ◽  
Maglev Train ◽  
Identification Method ◽  
The Finite Element Model

Abstract. As a modern high-tech rail vehicle, the maglev train realizes the non-contact suspension and guidance between the train and the guideway, which greatly reduces the resistance of the system. Due to the high-speed operation characteristics of maglev trains, the structural health monitoring of guideway girders is particularly important for the safety and stability of maglev train operation. This paper takes the maglev train guideway girder as the monitoring target, and the finite element model of the maglev vehicle-guideway is established to simulate the running state of the train passing through the guideway girder. The dynamic response data of the guideway girder is obtained in the finite element model, considering healthy states and different damage states of the guideway girder. Then, a modal-based damage identification method is proposed, which obtains the guideway girder damage sensitive characteristics by decomposing the guideway girder acceleration response signal. Finally, based on the measured guideway girder acceleration data, this paper verifies the effectiveness of the damage identification method in guideway girder structure health monitoring, which provides reference and guidance for the future maintenance of the maglev guideway girder.

scholarly journals Statistical damage identification method based on dynamic response sensitivity

2018 ◽  
Vol 39 (3) ◽  
pp. 560-571 ◽  
Author(s):  
Ling Mao ◽  
Shun Weng ◽  
Shu-Jin Li ◽  
Hong-Ping Zhu ◽  
Yan-Hua Sun
Keyword(s):  
Finite Element ◽  
Dynamic Response ◽  
Finite Element Model ◽  
Damage Identification ◽  
Element Model ◽  
Measured Data ◽  
Response Sensitivity ◽  
Identification Method ◽  
The Finite Element Model ◽  
Statistical Damage

The traditional deterministic damage detection method is based on the assumption that the measured data and the finite element model are accurate. However, in real situation, there are many uncertainties in the damage identification procedure such as the errors of the finite element model and the measurement noise. Since the uncertainties inevitably exist in the finite element models and measured data, the statistic method which considers the uncertainty has wide practical application. This paper proposes a statistical damage identification method based on dynamic response sensitivity in state-space domain. Considering the noise of the finite element model and measured acceleration response, the statistical variations of the damaged finite element model are derived with perturbation method which is based on a Taylor series expansion of the response vector and verified by Monte Carlo technique. Afterward, the probability of damage existence for each structural element is estimated using the statistical characteristic of the identified structural parameters. A numerical simply supported beam under the moving load is applied to demonstrate the accuracy and efficiency of the proposed statistical method.

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 Wind-driven damage localization on a suspension bridge

2016 ◽  
Vol 11 (1) ◽  
pp. 11-21 ◽  
Author(s):  
Marco Domaneschi ◽  
Maria Pina Limongelli ◽  
Luca Martinelli
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Identification ◽  
Structural Response ◽  
Suspension Bridge ◽  
Element Model ◽  
Damage Localization ◽  
Damage Scenarios ◽  
Long Span Bridges ◽  
Long Span

The paper focuses on extending a recently proposed damage localization method, previously devised for structures subjected to a known input, to ambient vibrations induced by an unknown wind excitation. Wind induced vibrations in long-span bridges can be recorded without closing the infrastructure to traffic, providing useful data for health monitoring purposes. One major problem in damage identification of large civil structures is the scarce data recorded on damaged real structures. A detailed finite element model, able to correctly and reliably reproduce the real structure behavior under ambient excitation can be an invaluable tool, enabling the simulation of several different damage scenarios to test the performance of any monitoring system. In this work a calibrated finite element model of an existing long-span suspension bridge is used to simulate the structural response to wind actions. Several damage scenarios are simulated with different location and severity of damage to check the sensitivity of the adopted identification method. The sensitivity to the length and noise disturbances of recorded data are also investigated.

An efficient technique for extraction of nonlinear dynamic features in a model-based feature extraction scheme for machine learning–based structural health monitoring

2020 ◽  
pp. 107754632093374
Author(s):  
Mehdi Fathalizadeh Najib ◽  
Ali Salehzadeh Nobari
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Damage Identification ◽  
Element Model ◽  
Model Based ◽  
Damage Indicators ◽  
Input Model ◽  
Harmonic Components ◽  
Nonlinear Autoregressive ◽  
The Finite Element Model

Super-harmonic components in response to the harmonic excitation are sensitive indicators of damages such as breathing cracks in beams or kissing bonds in adhesive joints. In a model-based damage identification process using pattern recognition, these damage indicators can be extracted from the finite element model for all probable damage cases using stepped-sine simulation that necessitates nonlinear transient dynamic analysis with high computational costs. In this study, a procedure based on nonlinear autoregressive with exogenous input model is introduced as an alternative shortcut method for extraction of the damage indicators. As a case study, the finite element model of a beam connected to a rigid support via a flexible adhesive layer was used to investigate the efficiency of the proposed method. Kissing bond was introduced to the model as the source of nonlinearity via contact elements. The results prove that the super-harmonic components of orders up to 3, extracted from the nonlinear autoregressive with exogenous input model, agreed well with those extracted directly from the finite element model, whereas the computational time is reduced by a factor of 1/5. Consequently, the proposed method is very advantageous in the stage of damage pattern database creation in a real-world model-based damage identification process based on pattern recognition.

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