Evaluation of Wood Composite Sandwich Panels as a Promising Renewable Building Material

The Effect of Openings' Size and Location on Selected Dynamical Properties of Typical Wood Frame Walls

The wooden frame constructions are now popular in many developed countries of the world. Many of these locations where such buildings are constructed are exposed to seismic and other shocks which are generated by human activities. This paper discusses the effect of the size and location of openings in the wooden frame walls under dynamic loadings. Natural frequencies of such frames with and without openings have been determined. Three 14 m high walls with different widths, including 3, 6, and 12 m, have been considered. Dynamic analysis has been made using finite element method structural analysis software Dlubal RFEM 5.17. The results show that the effect of the size and location of the openings on the natural frequency is significant. Numerically speaking, the relative change of the natural frequencies of a wall without and with an opening in a specific place could be up to 30%. In addition, the change of the natural frequency for the location of the openings is more sensitive than that to the sizes. Furthermore, the appropriate sizes and locations of openings of the wooden frame walls have been suggested. The appropriate size and place were found to be small openings in the top of the walls.

A Novel Implementation of the LDEM in the Ansys LS-DYNA Finite Element Code

In this paper, a novel implementation of the Lattice Discrete Element Method (LDEM) is proposed: in particular, the LDEM is implemented in the Ansys LS-DYNA finite element code. Such an implementation is employed to evaluate the fracture behaviour of sandwich panels under bending. First, the novel hybrid model proposed is validated by simulating some three-point bending experimental tests carried out at the University of Parma, and then it is used to model the fracture behaviour of sandwich panels under four-point bending. Failure mechanisms, damage locations, and load-deflection curves are numerically determined by employing such a novel model, and the results show a good agreement with the available experimental findings.

Modeling the Bending Strength of MDF Faced, Polyurethane Foam-Cored Sandwich Panels Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

The present study evaluates and compares predictions on the performance and the approaches of the response surface methodology (RSM) and the artificial neural network (ANN) so to model the bending strength of the polyurethane foam-cored sandwich panel. The effect of the independent variables (formaldehyde to urea molar ratio (MR), sandwich panel thickness (PT) and the oxidized protein to melamine-urea-formaldehyde synthesized resin weight ratio (WR)) was examined based on the bending strength by the central composite design of the RSM and the multilayer perceptron of the ANN. The models were statistically compared based on the training and validation data sets via the determination coefficient (R2), the root mean squares error (RMSE), the absolute average deviation (AAD) and the mean absolute percentage error (MAPE). The R2 calculated for the ANN and the RSM models was 0.9969 and 0.9960, respectively. The models offered good predictions; however, the ANN model was more precise than the RSM model, thus proving that the ANN and the RSM models are valuable instruments to model and optimize the bending properties of the sandwich panel.