Abstract
In this paper, a design optimization for the reinforced concrete plane frame structure has been done in order to minimize the cost of the concrete and steel for beams and columns by adopting the Artificial Neural Network (ANN) computational model trough the NeuroShell-2 software program. The design procedure conforms to the ACI-318-08 Code. The variables used for design optimization are the width, depth and the area of reinforced steel, including longitudinal reinforcement and shear reinforcement. A three-bay two-story RC frame is modeled with selecting different span length and different load cases. Acceptable design results are obtained from more than 50 examples which are subjected to all the constraints of the ACI Code, using different cross-section sizes and these results are used to train the NeuroShell-2 program. The results obtained demonstrate the efficiency of the ANN procedure for the multi story RC frame design.
Highlights
Following the requirements of any code of practice in order to design beams and columns and with obtaining many acceptable cross sections, most engineers are in hesitation with selecting suitable cross sections that lead to minimize cost without further calculations
4) Maximum reinforcing steel area constraint In order to have reasonable assurance that concrete beams fail in a ductile manner under flexure, the ACI Code limits the amount of tension steel to not more than 75 percent of the amount in the balanced strain that is: ρ = 0.75ρb
6) Crack width constraint According to the ACI Code, the maximum crack width of beam can be controlled by cross section dimension; number of bars placing in it and the exposure condition, the following constraint can be imposed: 0.0006 145 fy where: d ′ = concrete cover. nmin is the minimum number of bars in the single layer of reinforcement at top or bottom of beam
Summary
Following the requirements of any code of practice in order to design beams and columns and with obtaining many acceptable cross sections, most engineers are in hesitation with selecting suitable cross sections that lead to minimize cost without further calculations. Artificial Neural Networks (ANN) are generally presented as systems of interconnected “neurons” which can compute values from inputs. These researchers basically set the structural parameters such as the material property, the boundary condition and the size of a structure as the input of the ANN model to predict the ability for the structure to resist the load [12]. In most of these works, the neural networks have been trained by using back propagation algorithm. These values are modified automatically according to the learning algorithm during the process of learning
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