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- W2020178757 abstract "Genetic algorithms (GA), a search method inspired by Darwin's theory of evolution, offer an optimisation tool that has been used very successfully to solve a variety of engineering problems. The search process it implements starts with a set of one or more chromosomes (initial population) and, by applying selection and reproduction operators, iteratively ‘evolves’ the population into better ones, until a stopping criterion is reached. This article investigates lattice-reinforced joist slab cost optimisation problems using a GA with continuous variables. The problem considered concerns one-way slabs, continuous over two spans, in which only the in situ concrete characteristics and joist spacing are varied. The design variables are: concrete layer thickness, concrete layer strength, reinforcement, distance between joists and degree of redistribution of the continuous slabs’ negative moments. The search for a solution includes an investigation into the use of discrete variables for data representation. To obtain results that allow for a comparative empirical analysis, these problems are also evaluated by a conventional optimisation method. The results indicate that the GA method is a viable optimisation tool for solving lattice-reinforced joist slab cost minimisation problems." @default.
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- W2020178757 date "2007-09-01" @default.
- W2020178757 modified "2023-09-26" @default.
- W2020178757 title "Cost optimisation of lattice-reinforced joist slabs using genetic algorithms" @default.
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- W2020178757 doi "https://doi.org/10.1680/stco.2007.8.3.111" @default.
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