Neural Network for Classification Based on Non Destructive Testings
Vlatka Rajcic
ABSTRACT Proposal for classification of wood members using neural network is presented here. Using nondestructive methods (transient excitation by hammer impact, transient excitation putting the element out of state of equilibrium and ultrasound excitation) dynamic) dynamic parameters are obtained for more then 100 wood members of Slavonian Oak. Dynamic parameters resonant frequency, decrement of vibrational decay and propagation speed of ultrasound wave), moisture, density, boundary conditions and geometry of the samples were input variables while strength values and MOE were output variables for teaching neural network As a result RUNTIME option of neural network is obtained as main part of classification system which contains n-dirnensional correlation between all input and output variables. It means that just with very simple hammer percussion on wood sample or by acoustic emission, using such neural, network, we can get all strength values and MOE without any laboratory destructive testing.
Publication Source: WCTE '98 - 5th World Conference on Timber Engineering, 1998 August 17-20, Montreaux, Switzerland.
Vol. 2, Pages 318.....
Publisher: Presses polytechniques et universitaires romandes - [Homepage]
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