A hybrid artificial intelligence model for design of reinforced concrete columns

In the optimum design of structures, the optimization process is an iterative one and it may last a long time. If the structural plan is updated, the optimization process is needed to be redone since dimensions and internal forces change. Also, the local market prices may show differences and the proposed design may not be the optimum anymore. To skip the optimization process, intelligence methods can be used to predict the optimum values. In the study, a model is proposed for cost optimum results of reinforced concrete columns. A hybrid method is presented that uses harmony search as a metaheuristic method in the optimum design and multi-layer perceptions as a type of artificial neural networks in machine learning to generate a model. The prediction results were evaluated for several error metrics, and the model is feasible in proposing optimum solutions.