Abstract

This paper deals with the problem of optimum design of reinforced concrete one-way ribbed using genetic algorithms. ACI-Coefficient method is used for the structural analysis and design of slabs. The cost function represents the cost of concrete, steel, and formwork for the slab. The design variables are taken as slab thickness, spacing between ribs, lower width of ribbed, upper width of ribbed, depth of rib, depth of beam, shear reinforcement spacing of ribs, bar diameter and spacing of top slab, depth of the neutral axis from bottom fiber, beam width, and areas of flexural reinforcement at moment critical section along ribs and beams. The constraints include the constraints of the joist construction constraints stated by ACI-code, constraints on the top slab thickness to satisfy fire resistance, constraints on the areas of steel reinforcement to satisfy the flexural and the minimum area requirements, the constraints on the total slab thickness to satisfy deflection, and flexural behavior. The parameters were taken in this study included: the span length(4-12)m, the compressive of concrete(25-60) MPa, the strength of steel (345-600)MPa, the live load (2-7)KN/m2, unit cost ratio(cost of concrete/cost of steel)(0.1-0.35), aspect ratio (1-2), the formwork cost(10,000-22,000)I.D. The results show that, when discussed the span length, the ratio of (total slab depth/span length) should be (1/18-1/4) using G.A, to get the optimum slab design. It is also concluded that the optimum ratio of (depth of rib/lower rib width) is to be ranged within (1.243-2.917) using G.A. The optimum (rib spacing/span length) ratio is found to be ranged from (1/40- 1/5) using G.A, the results also showed that the range of the optimum (slab thickness/spacing of rib) ratio is found to be ranged as (1/4-1/2) using G.A, in order to find the optimum solution.A computer program is written using MATLAB for the implementation of structural analysis and the design of one-way ribbed slabs by the ACI coefficient method. The optimization process is performed using the built-in genetic algorithm toolbox of MATLAB.

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