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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 60
Edited by: B.H.V. Topping and B. Kumar
Paper IV.7

Optimum Geometry and Spacing Design of Roof Trusses based on BS5950 using Genetic Algorithm

S.H. Weldali and M.P. Saka

Civil Engineering Department, University of Bahrain, State of Bahrain

Full Bibliographic Reference for this paper
S.H. Weldali, M.P. Saka, "Optimum Geometry and Spacing Design of Roof Trusses based on BS5950 using Genetic Algorithm", in B.H.V. Topping, B. Kumar, (Editors), "Optimization and Control in Civil and Structural Engineering", Civil-Comp Press, Edinburgh, UK, pp 119-130, 1999. doi:10.4203/ccp.60.4.7
This paper describes a genetic algorithm based optimum design method for roof trusses subjected to multiple loading cases. The algorithm obtains a roof truss which has the minimum weight by selecting appropriate steel sections from the standard section tables while satisfying the design limitations given in BS5950. The design constraints which covers the serviceability and strength limitations are implemented directly from the code. The algorithm has three versions. In the first, the optimum angle section designations are found for the truss members while the geometry and spacing between the trusses are kept fixed. In the second the height of appex is treated as design variable in addition to member sizes. The third one considers the spacing between the trusses also as an additional design variable. In all these versions, the genetic algorithm has successfully obtained the optimum solution.

The design algorithm developed is applied to find the optimum solution of four different design problems, in each seven different topologies are considered for the roof truss. In the first, the geometry and spacing are kept fixed and optimum equal leg angle designations are obtained for each roof truss topology. In the second the spacing is also made variable while the geometry is kept fixed. In the third, the height of appex is treated as variable allowing the geometry to change while the spacing is kept fixed. In the fourth, the height of appex and the spacing are taken as variables in addition to member designations. It is shown that the latter yields to the lightest structure for the roof. It is noticed that genetic algorithm performs efficiently in the design problems where the height and spacing are fixed. However, when these parameters are treated as design variables, it has the tendency of settling down with the local optimum.

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