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Civil-Comp Proceedings
ISSN 1759-3433 CCP: 103
PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING Edited by: Y. Tsompanakis
Paper 11
Robust Truss Optimization with Uncertain Load Directions A. Csébfalvi
Department of Structural Engineering, University of Pécs, Hungary , "Robust Truss Optimization with Uncertain Load Directions", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 11, 2013. doi:10.4203/ccp.103.11
Keywords: optimal truss design, robust truss optimization, uncertain-but-bounded parameters, uncertain load direction, unified uncertainty management.
Summary
In the real-world truss optimization problems, the optimal performance obtained using conventional deterministic methods can be dramatically degraded in the presence of sources of uncertainty. The source of uncertainty may be the variability of applied loads, spatial positions of nodes, as well as section and material properties. In this paper, we present a new theoretical model and a problem-specific metaheuristic approach when the only source of uncertainty is the variability of the applied load directions. The essence of the novel concept is independent from the theoretical description of the uncertainty which may be either probabilistic (stochastic) or possibilistic (fuzzy). In the presented unified (non-probabilistic and non-possibilistic) approach, the varying load directions are handled as uncertain-but-bounded parameters. The result of the optimization is a robust minimal-weight truss design which is invariant to the investigated load uncertainty type. The well-known ten-bar plane truss example with the most popular direction settings will be used to illustrate the validity and efficiency of the presented approach. In the presented example we replaced each nominal load direction by an angle set around the nominal value. The detailed description of the problem-specific robust metaheuristic based on the previously developed ANGEL metaheuristic that will be presented in a forthcoming paper.
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