Structural Concrete, Vol. 8, no. 3, September 2007
Cost optimisation of lattice-reinforced joist slabs using genetic algorithms
V.C. Castilho, Universidade Federal de Uberlândia, Brazil
M.C.V. Lima, Universidade Federal de Uberlândia, Brazil
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.