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CRACK IDENTIFICATION USING HYBRID NEURO-GENETIC TECHNIQUE
CRACK IDENTIFICATION USING HYBRID NEURO-GENETIC TECHNIQUE
- Source :
- Journal of Sound and Vibration. 238:617-635
- Publication Year :
- 2000
- Publisher :
- Elsevier BV, 2000.
-
Abstract
- It has been established that a crack has an important effect on the dynamic behavior of a structure. This effect depends mainly on the location and depth of the crack. To identify the location and depth of a crack on a structure, a method is presented in this paper which uses hybrid neuro-genetic technique. Feed-forward multi-layer neural networks trained by back-propagation are used to learn the input (the location and depth of a crack)–output (the structural eigenfrequencies) relation of the structural system. With this trained neural network, genetic algorithm is used to identify the crack location and depth minimizing the difference from the measured frequencies.
- Subjects :
- Acoustics and Ultrasonics
Artificial neural network
Computer science
Mechanical Engineering
Numerical analysis
Structural system
Feed forward
Natural frequency
Condensed Matter Physics
Backpropagation
Finite element method
Mechanics of Materials
Genetic algorithm
ComputingMethodologies_GENERAL
Algorithm
Subjects
Details
- ISSN :
- 0022460X
- Volume :
- 238
- Database :
- OpenAIRE
- Journal :
- Journal of Sound and Vibration
- Accession number :
- edsair.doi...........6b0b2fde3d61d09fb3feae0fc48c7548
- Full Text :
- https://doi.org/10.1006/jsvi.2000.3089