This work addresses the problem of grading timber into strength classes. All materials, used in the construction, have to be graded and marked with a CE mark. For the classification into a certain strength class, the material has to adhere to minimal mechanical requirements of the class. Timber is an anisotropic, non-homogeneous material. The timber strength within a certain species depends, among other factors, on the part of the log that the sample has been cut off, the environment of its growth, the type of soil, the timber age, the damage to the timber, the presence of knots etc. The only accurate method for determining timber strength is destructive testing. Presently, the only method considered relevant in Slovenia is the visual evaluation of the timber, which is a very conservative method and results in lower prices of good quality timber. The goal of the presented work was to develop a system for timber grading on the basis of results of non-destructive methods, particularly by using the machine learning methods. The analysis sample consists of more than 5000 timber samples, which have been tested with non-destructive, as well as destructive methods. Results of the performed tests were analysed by using the machine learning algorithms contained in the WEKA Toolkit and the statistical methods of the IBM's SPSS 20 program. In order to analyze the collected data, standard statistical tests were performed for the analysis and artificial neural networks, as well as regression trees, were also used. The presented results indicate that the number of the samples was sufficient and that the formalism of the artificial neural networks is the appropriate tool for determining the timber grading parameters on the basis of results of non destructive testing methods.
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