An automatic mode pairing strategy using an enhanced modal assurance criterion based on modal strain energies

Brehm, Maik; Zabel, Volkmar; Bucher, Christian

doi:10.1016/j.jsv.2010.07.006

Cited by 69 publications

(36 citation statements)

References 18 publications

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“…In Matlab software, the eigenvalues and eigenvectors problem is solved, and based on the experimental modal information, the mode pairing between numerical and experimental modes using a modal strain energy criterion based on EMAC parameter is performed [3]. The values of the natural frequencies and the corresponding MAC (Modal Assurance Criterion) values are exported in text format.…”

Section: Computational Implementation Of An Iterative Methodsmentioning

confidence: 99%

Calibration of Dynamic Models of Railway Bridges Based on Genetic Algorithms

Ribeiro

Calçada

Delgado

2015

Computational Methods in Applied Sciences

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This chapter presents the main experimental calibration methodologies of finite element numerical models, with particular focus on methodologies based on modal parameters. In this context, the computational implementation of an iterative method based on a genetic algorithm is described. The iterative method involves the resolution of an optimization problem, which involves the minimization of an objective function by varying a set of preselected model parameters. The objective function includes residuals associated to natural frequencies and mode shapes. The proposed methodology is applied to the calibration of the dynamic models of two railway bridges, São Lourenço bridge and Alverca viaduct, both located in the northern line of the Portuguese railways in recently upgraded track sections. The calibration results demonstrate a very good agreement between numerical and experimental modal responses and a significant improvement of the numerical models before calibration. Also the stability of a significant number of parameters, considering different initial populations, proved the robustness of the genetic algorithm in the scope of the optimization of the numerical models. The updated numerical models were validated based on dynamic tests under railway traffic. The results showed an excellent agreement between numerical and experimental responses in terms of displacements and accelerations of the bridges' decks.

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Section: Computational Implementation Of An Iterative Methodsmentioning

confidence: 99%

Calibration of Dynamic Models of Railway Bridges Based on Genetic Algorithms

Ribeiro

Calçada

Delgado

2015

Computational Methods in Applied Sciences

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“…The off-diagonal elements in the matrix express the correlation between two modal vectors. The minimization of the off-diagonal terms in the MAC matrix gives a good MAC index that can guarantee the orthogonality of the measured modal vectors and increase the amount of modal information obtained from the collected data [16]. The possible range of the MACs is from zero to unity such that two modes ϕᵢ and ϕ j are the same when it is unity, while both are in no correlation when it is zero [17].…”

Section: Modal Assurance Criterion (Mac)mentioning

confidence: 99%

“…Modal strain energy is a damage index which is proposed to detect multiple damages. The MAC matrix is obtained from the mode shape matrix [16]. This study focuses on the optimal sensor placement and damage detection of the structures.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Damage Identification and Optimal Sensor Placement in Structural Health Monitoring by Genetic Algorithm Technique

Muthuraman¹,

Hashita²,

Sakthieswaran³

et al. 2016

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Civil engineering structures are constructed for strength, serviceability and durability. The structures thus constructed involve huge investment and labour work. In order to protect the structure from various damages, periodic monitoring of structures is necessary. Hence Structural Health Monitoring (SHM) plays a vital role in diagnosing the state of the structure at every moment during its life period. For this purpose, sensors are deployed in the structures for its efficient health monitoring. Sensors cannot be deployed at random locations of the structure. They have to be located at those points which reflect the damage. In this study, a 3-storey and a 4-storey building are taken and Modal Strain Energy (MSE) is used for finding the initial locations of sensors. The number of sensors obtained is then optimized using Genetic Algorithm (GA) technique. Finally damages are induced in certain locations of the structure and a damage detection technique called as "Flexibility Matrix Based Technique (FMBT)" is introduced for damage localization in the structure.

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“…Figure 9 shows the results of the sensitivity analysis using a Spearman correlation coefficients matrix [14]. The sensitivity analysis was performed using a stochastic sampling technique based on 1000 samples generated by Latin Hypercube method.…”

Section: Sensitivity Analysismentioning

confidence: 99%