Abstract
ABSTRACT Wood is a widely used material in various industries, and its mechanical properties are crucial to determine the final product's performance. Modulus of elasticity (MOE) is one of the most important mechanical properties of wood, which measures its stiffness and ability to resist deformation under applied loads. The objective of this research was to create robust models for predicting the moduli of elasticity (MOE) of wood species in three principal directions under varying moisture levels, using the ultrasonic pulse velocity (UPV) technique. The study employed three modeling techniques, namely multivariable linear regression (MLR), artificial neural network (ANN), and support vector regression (SVR). The research involved developing 72 models using different input variables and methods, and then training and testing them with experimental data. Our findings revealed that the SVR model utilizing the radial basis kernel function (RBF) and Levenberg-Marquardt backpropagation ANN exhibited the lowest mean squared error (MSE) and the highest correlation coefficient, with R2 values of 0.979 and 0.989, respectively. These results suggest that the developed models and the UPV technique could significantly enhance the precision of MOE predictions for wood species, presenting a valuable tool for industries that utilize wood in their products.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
