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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 25
ADVANCES IN STRUCTURAL OPTIMIZATION
Edited by: B.H.V. Topping and M. Papadrakakis
Paper VIII.3

Genetic Algorithms in a Multi-Criterion Optimal Detailing of Reinforced Concrete Members

V.K. Koumousis and S.J. Arsenis

Institute for Structural Analysis & Aseismic Research, National Technical Universityof Athens, Athens, Greece

Full Bibliographic Reference for this paper
V.K. Koumousis, S.J. Arsenis, "Genetic Algorithms in a Multi-Criterion Optimal Detailing of Reinforced Concrete Members", in B.H.V. Topping, M. Papadrakakis, (Editors), "Advances in Structural Optimization", Civil-Comp Press, Edinburgh, UK, pp 233-240, 1994. doi:10.4203/ccp.25.8.3
Abstract
A genetic algorithm is used for the optimal detailed design of reinforced concrete members of multi-storey buildings. The method synthesises the information from the cross-sectional level, to the member-level and finally to a group of members where the detailed design of the whole group is decided. For many cases the number of alternative solutions are of the order of several millions. For example, with two layers and three regions considered in every span of a continuous beam, the design space of the problem is of the order of 2(8)3n, where n is the number of spans of a continuous beam and 8 represents the alternative solutions in every region. For these design spaces enumeration methods lead to expensive solutions. A genetic algorithm is adopted for the solution of the problem. The method is applied to problems having large design spaces and near optimum solutions are found in reasonable computing time. The genetic algorithm is based on a roulette-wheel reproduction scheme, single point crossover and constant or variable mutation scheme. An elitist strategy is also used that passes the best designs of a generation to the next generation. The method decides the detailed design on the basis of a multi-criterion objective that represents a compromise between a minimum weight design, a maximum uniformity and the minimum number of bars for a group of members. By varying the weighting factors, designs with different characteristics result. The anchorage lengths are taken into account and the bars are cut at appropriate regions. The performance of the system is illustrated with a number of examples. Various parameters of the genetic algorithm are considered which render the method as an efficient optimization method for discrete structural design problems.

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