Detection of Structural Damage in Medium Density Fiberboard Panels using Neural Network Method

Abstract

This research assessed the feasibility of using a neural network to detect low levels of damage in small samples of medium density fiberboard (MDF). The neural network was a three-layer back-propagation network. The undamaged stress wave frequency spectrum patterns were trained by the neural network. The trained patterns were then compared to stress waves patterns taken from MDF samples loaded to various percentages of their estimated failure load. In this experiment, if an application load is below the proportional limit, a small change in wave patterns occurs. The neural network has the unique ability to train data to recognize spectral patterns and has been used with success for the detection of structural damage.

Modeling of Compressive Strength Parallel to Grain of Heat Treated Scotch Pine (Pinus sylvestris L.) Wood by Using Artificial Neural Network

“…Artifi cial neural network has been widely used in many wood industries, such as in the wood identifi cation system (Tou et (Xu et al, 2007), in predicting fracture toughness of wood (Samarasinghe et al, 2007), in the evaluation of strength of wood timbers (Tanaka et al, 1996), in the prediction of bending strength and stiffness in western hemlock (Shawn et al, 2007), in the prediction of particleboard mechanical properties (Fernández et al, 2008), in the optimization of process parameter in a particleboard manufacturing process (Cook et al, 2000), in the detection of structural damage in medium density fi berboard panels (Long et al, 2008), in the prediction of modulus of rupture and modulus of elasticity of fl ake board (Yapıcı et al, 2009). It has also been applied to obtain the hygroscopic equilibrium points (Avramidis and Iliadis, 2005), to classify wood defects (Drake and Packianather, 1998), to determine the internal bond values of particleboard (Cook and Chiu, 1997;Fernandez et al, 2008), and in statistical process control in the manufacture of particleboard (Estaben et al, 2009b).…”

The use of an artificial neural network for modeling the moisture absorption and thickness swelling of oriented strand board

“…There also have been some studies in the field of wood-based composite materials. Artificial neural networks were used to predict the mechanical properties of particleboard (Fernández et al 2008;Cook and Chiu 1997), to optimize the process parameters in a particleboard manufacturing process (Cook et al 2000), and to detect of structural damage in medium density fiberboard panels (Long and Rice 2008). Esteban et bioresources.com .…”