Machine Learning and Optimality in Multi Storey Reinforced Concrete Frames

Abstract

The present study investigates the potential of the implementation of machine learning techniques in optimized multi storey reinforced concrete frames. The variables that are taken into account in the objective function of the optimization problem are the following: the frame type (frame bay length optimality) and dimensioning of the cross sections. The objective function has the goal of attaining a minimum cost design based on market data, after a structural analysis of the frames. A number of optimized examples with widely encountered cases of total lengths of frames and with various loadings are presented. Modeling is based on Eurocode 2. Optimization takes place with the use of evolutionary algorithms. The optimized results are subjected to predictive modeling based on neural networks. The objective of the study is to create predictive models with the aim of minimizing the usage of scarce resources.