The Finite Element Model Updating: A Powerful Tool for Structural Health Monitoring

Mordini, Andrea; Savov, Konstantin; Wenzel, Helmut

doi:10.2749/101686607782359010

Cited by 17 publications

(7 citation statements)

References 3 publications

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“…For bridge structures, during operation, factors such as performance degradation of components and section failure may deviate from design parameters (such as section size, density, modulus of elasticity, etc.). This leads to a deviation from the actual structure when establishing a finite element model [4]- [5]. Therefore, in order to obtain an accurate finite element model, it is necessary to update the relevant parameters of the finite element model.…”

Section: Introductionmentioning

confidence: 99%

An ED-PSO model updating algorithm for structure health monitoring of beam-like structures

Wang,

Li,

Kaewniam

et al. 2023

J. meas. eng.

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Cracks and other damages generated during the service of bridges can reduce the load bearing capacity and threaten operational safety.Finite Element Model Updating (FEMU), as one of the important means of structural health diagnosis, identifies structural damage through changes in model parameters. The three key factors of FEMU are updating variables, objective functions, and optimization algorithms. The poor selection of the above three factors in existing research leads to high calculation errors in model updating, and inevitably lead to the inability of the finite element model to carry out structural health monitoring, affecting the normal operation of the structure. In order to solve the above problems, this paper combines previous research and establishes a model updating algorithm based on the combination of eigenvector difference approach and particle swarm optimization (ED-PSO). The validity and accuracy of this method are verified by finite element analysis of a simply supported beam. Compared with the existing model updating algorithms based on the combination of static and dynamic methods and particle swarm optimization (CSD-PSO), the results show that the proposed ED-PSO model updating algorithm has higher accuracy and is expected to be better applied to bridge finite element model updating research.

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Section: Introductionmentioning

confidence: 99%

An ED-PSO model updating algorithm for structure health monitoring of beam-like structures

Wang,

Li,

Kaewniam

et al. 2023

J. meas. eng.

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“…The basic principle of the FE model updating is to minimize the residual error between the predicted and measured structural response by adjusting the selected parameters of the FE model (note that the selected model parameters are called updating parameters in this paper). An accurate FE model could help understand the mechanical properties of the actual structure (Mordini et al 2007). Moreover, it is also valuable for identifying and estimating the structure's residual load capacity and safety reserve (Brownjohn 2007).…”

Section: Introductionmentioning

confidence: 99%

Hybrid nested sampling method for identifying the uncertainty of the high-dimensional updating parameters in Bayesian structural model updating

Hong

Chen

et al. 2022

Advances in Structural Engineering

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Bayesian inference is a practical and straightforward approach to quantifying the uncertainty of the model parameters in structural finite element model updating. Sampling methods are frequently used to estimate the uncertainty of the selected updating parameters in a statistically principled way. Generally, uncertainty can be described by global optimum, local optimum, expectation, variance, and marginal probability density function (PDF). However, it is rare to see model updating methods focusing on studying the high-dimensional distribution of the updating parameters due to the computational cost. This paper develops a hybrid nested sampling method to identify the global optimum and high-dimensional confidence interval simultaneously. The proposed method samples the posterior PDF by shrinking the range of the live sample set layer by layer and achieves the global optimum guided by a hybrid search strategy. Finally, the performance of the proposed method was investigated by numerical simulations and an actual shear-type structure’s model test. After analysis and comparison, the results show that the proposed method performs very well in accuracy and robustness.

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“…model can be used either to assess more accurately the behaviour of civil engineering structures [2,3,4] or to establish a strategy for damage detection [5,6].…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Finite-Element Model Updating of Civil Engineering Structures Using Nature-Inspired Computational Algorithms

Jiménez‐Alonso

Naranjo-Pérez

Pavić

et al. 2020

Structural Engineering International

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The Finite Element Model Updating: A Powerful Tool for Structural Health Monitoring

Cited by 17 publications

References 3 publications

An ED-PSO model updating algorithm for structure health monitoring of beam-like structures

An ED-PSO model updating algorithm for structure health monitoring of beam-like structures

Hybrid nested sampling method for identifying the uncertainty of the high-dimensional updating parameters in Bayesian structural model updating

Maximum Likelihood Finite-Element Model Updating of Civil Engineering Structures Using Nature-Inspired Computational Algorithms

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