Objective
The aim of this project is to develop new computerised grading methods for structural timber using non- destructive techniques. Currently, grading machines based on the measurement of MOE are more efficient than visual grading, but are still too conservative to achieve anything approaching the optimum yields that a perfect grading system could achieve. For example, research shows that British grown Scots pine currently returns a yield of less than 10 % for the high strength C30 grade and that the potential yield is close to 60 %. The equivalent figures for French grown Douglas fir are 9 % when visually graded to C30, with a potential yield of 78 %. With improvements m yield, timber, which is the only structural material that is a renewable resource, could be more competitive with concrete and steel for structural use.
There is currently world wide interest in improving timber grading machines and it is important that Europe does not get left behind. It is proposed to collaborate with acknowledged experts outside Europe (who have their own funding), to spread the work load, saving time and costs by taking advantage of an exchange of knowledge and expertise. This collaboration will also help to establish differences between species. The objectives are: (1) Develop nondestructive techniques (CCD cameras, micro-wave and gamma-ray emission, etc...) for the measurement of timber properties which can predict the strength of the material.
(2) Investigate new methods (neural networks, stochastic finite elements analysis, statistics, ...) for establishing the relation between these non destructive properties and the strength of the material.
(3) Use the results of steps I & 2 to propose efficient new machines and modifications to existing machines which could be implemented in the industry (sawmills, timber trade, glulam and truss manufacturing companies). The European timber industry, mainly comprising small companies, has strong interest in this project which should assist in increasing the use of timber in structures, based on improved quality of their products.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
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Topic(s)
Call for proposal
Data not availableFunding Scheme
CSC - Cost-sharing contractsCoordinator
88580 Saulay-sur-Meurthe
France