Damage detection with genetic algorithms taking into account a crack contact model

Buezas, Fernando Salvador; Rosales, Marta B.; Filipich, C.P.

doi:10.1016/j.engfracmech.2010.11.008

Cited by 56 publications

(30 citation statements)

References 17 publications

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“…This is true for those at the levels = 2 and 3 (hence their graphs are not given). However, the at = 4 is in well agreement with the noiseless data (see Figure 6(b)); thus, it can be employed as reference data in (19), that is, =4 . At higher levels the deviates from the noiseless data, since not only high-frequency components are removed but also some low-frequency parts that affect shape of the signal are removed.…”

Section: Applicationsupporting

confidence: 56%

“…Motivated by this conclusion and the approach of Buezas et al [19], Gökdag [20] introduced a model-updatebased damage detection approach for beam type structures subject to vehicular load. In this respect, time-dependent displacements from several points on a cracked beam are 2 Advances in Acoustics and Vibration obtained, and an objective function is defined by subtracting these from the ones calculated by the mathematical model of the structure.…”

Section: Introductionmentioning

confidence: 99%

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A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

Gökdağ

2013

Advances in Acoustics and Vibration

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In this work a crack identification method is proposed for bridge type structures carrying moving vehicle. The bridge is modeled as an Euler-Bernoulli beam, and open cracks exist on several points of the beam. Half-car model is adopted for the vehicle. Coupled equations of the beam-vehicle system are solved using Newmark-Beta method, and the dynamic responses of the beam are obtained. Using these and the reference displacements, an objective function is derived. Crack locations and depths are determined by solving the optimization problem. To this end, a robust evolutionary algorithm, that is, the particle swarm optimization (PSO), is employed. To enhance the performance of the method, the measured displacements are denoised using multiresolution property of the discrete wavelet transform (DWT). It is observed that by the proposed method it is possible to determine small cracks with depth ratio 0.1 in spite of 5% noise interference.

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Section: Applicationsupporting

confidence: 56%

Section: Introductionmentioning

confidence: 99%

A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

Gökdağ

2013

Advances in Acoustics and Vibration

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“…This model was used for analysis of different kinds of damaged structures. Buezas [2] applied genetic algorithms to detect crack on damaged structural elements up-to level three detection. They optimized the objective function which was derived from the dynamics of the studied body.…”

Section: Genetic Algorithm For Crack Detectionmentioning

confidence: 99%

“…Saravanan [1] employed fuzzy interference technique in order to determine the condition of an inaccessible gear in an operating machine. Buezas [2] used genetic algorithm optimization method in order to deal with crack detection in structural elements. Rosales [3] used neural network technique for the detection of cracks in beam like structure.…”

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confidence: 99%

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Thatoi¹

2013

IJET

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Abstract-Detection of cracks using vibration analysis employs different algorithms out of which artificial intelligence (AI) techniques is drawing a lot of attention of the engineers and scientists. This paper presents a comprehensive review of various AI techniques for determining the location, size and depth of cracks in different structures. The techniques mainly consist of fuzzy interference technique, neural network (NN), genetic algorithm (GA), hybrid models and other optimization algorithms.

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“…They showed that the vibration signals obtained from two sensors can effectively diagnose the extent of the crack development. Genetic algorithms appear as a desirable approach to optimize an objective function that depends on crack position and size [7][8][9][10]. Mehrjoo et al [11] used the GAs to search the solution of an inverse problem where the cracks' location and size are calculated.…”

Section: Introductionmentioning

confidence: 99%

Crack Detection of Fan Blade Based on Natural Frequencies

Gao

et al. 2018

International Journal of Rotating Machinery

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A simple method was developed to detect damage based on a discrete mathematical model for fan blades using changes in natural frequencies combined with a fluid-structure analysis. In addition, a numerical approach was developed for the fluid-structure analysis. The results of numerical simulation provided the natural frequency data for each mode under different locations and sizes of a single crack in a blade. A fault database was built using Matlab. The damage of a blade was detected using the changes in natural frequencies. This study will assist in investigating the effect of a crack on a structure from different perspectives; the simulation results show the effectiveness of this approach.

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Damage detection with genetic algorithms taking into account a crack contact model

Cited by 56 publications

References 17 publications

A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

A Crack Identification Method for Bridge Type Structures under Vehicular Load Using Wavelet Transform and Particle Swarm Optimization

Application of Artificial Intelligence Techniques for Detection of Cracks-A Review

Crack Detection of Fan Blade Based on Natural Frequencies

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