| Summary: | The problem of wood tensile strength estimation of softwood lumber is studied in this thesis. The main contributions brought to this topic here are first, a set of knot geometry features that can be used in board strength estimation, and second a learning algorithm that selects the best set of features for the purpose of strength measurement. The estimation problem is posed as an empirical learning problem that is based on the measured properties of wood. The process of producing the required database consisted of three distinct tasks: selecting and preparing the boards, measuring a set of properties of
wood for every board, and estimating the measured strength of each board from the
measured profiles. A set of boards, providing a random sample of softwood lumber, already existed at UBC
(from previous experiments). These boards were measured and used as the preliminary
database. A second set of boards was selected randomly from the regular production of softwood lumber. These boards created the evaluation data set.
For the measurement task, all the boards were scanned using the available measurement
machines. These machines were SOG and Microwave for grain angle measurement, X-ray for local density measurement, dynamic bending machine for the Modulus of
Elasticity measurement, as well as the ultimate tensile strength tester for measuring the tensile strength of a board. The output profiles per board were saved in a data file (one data file per board per machine). The measured data files were stored in a database consisting of a structure of directories. In the strength estimation task all the measured profiles of a board were mapped to specific features (usually statistical moments) and the features were then mapped to the
strength of the board. One of the features of a board is the set of its knots. A conic model of a knot was chosen and the related mappings were developed such that the X-ray
scanning could be used in order to detect the existence, location, and shape of knots in a board. Then geometrical features were proposed such that the set of knots of a board could be transformed into a set of features suitable for strength estimation methodology of this thesis. Since specimens are costly to measure, means to reduce the number required were developed. To this end statistical learning theory was applied. This theory addresses the suitability of the learning model for the physical problem and the effectiveness of the features for the estimation problem. Based on this theory, the ASEC learning model was developed.
The learning problem for wood tensile strength estimation was divided into three
problems: defining the most suitable feature set, measuring the suitability of a learning
machine, and using the a priori knowledge about the dependence in the learning machine.
A method for measuring the suitability of a regression estimator (VC-dimension) was
developed in order to select the best model in a class of models. The ASEC learning
model was developed in order to find the best set of new features from the given feature set by using the known dependencies.
Different learning machines were tested in order to determine what model is most
suitable for tensile strength estimation of lumber. The validity of all the methods was
demonstrated by analytical proof, by simulation, or by test on the database. === Applied Science, Faculty of === Electrical and Computer Engineering, Department of === Graduate
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