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Stochastic engineering framework for timber structural elements
Author(s) -
Kandler Georg,
Lukacevic Markus,
Wolff Sebastian,
Füssl Josef
Publication year - 2018
Publication title -
beton‐ und stahlbetonbau
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.486
H-Index - 25
eISSN - 1437-1006
pISSN - 0005-9900
DOI - 10.1002/best.201800055
Subject(s) - stiffness , robustness (evolution) , probabilistic logic , workflow , finite element method , computer science , stochastic process , structural engineering , sensitivity (control systems) , software , probabilistic analysis of algorithms , statistical model , mechanical engineering , engineering , mathematics , machine learning , biochemistry , chemistry , statistics , database , artificial intelligence , electronic engineering , gene , programming language
Due to the natural growth process of trees, the resulting wooden boards show a high amount of random fluctuation in their mechanical properties. In this work, a framework able to consider the impact of random stiffness fluctuations of wooden boards on the performance of glued laminated timber (GLT) is presented. A 2D finite element model is employed to depict the interactions between individual laminations and is integrated into a commercial robustness analysis and optimisation software package. The mechanical properties of each board are obtained during the grading process and are condensed into so‐called stiffness and strength profiles. Based thereon, a probabilistic material model is developed for the random generation of an arbitrary number of stiffness and strength profiles. On basis of the probabilistic material model and the mechanical FE model, the sensitivity to changes in the design can be explored. Subsequently, the optimal design parameters of the user‐defined structure are identified. An exemplary application scenario shows the workflow for determining the optimum hole dimensions and location in a GLT beam. The material model indicates large scatter in the mechanical properties, leading to conservative results. Nonetheless, the example shows the strengths of the employed workflow from sensitivity analysis to best designs.