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Infrastructures 2017, 2(2), 6; doi:10.3390/infrastructures2020006

Machine Learning and Optimality in Multi Storey Reinforced Concrete Frames

Department of Production Engineering and Management, Technical University of Crete, 731 00 Chania, Greece
Author to whom correspondence should be addressed.
Academic Editor: Jónatas Valença
Received: 25 January 2017 / Revised: 7 April 2017 / Accepted: 22 April 2017 / Published: 3 May 2017
(This article belongs to the Special Issue Concrete Structures: Present and Future Trends)
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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. View Full-Text
Keywords: machine learning; RC frame optimization; multi storey RC frames; evolutionary algorithms machine learning; RC frame optimization; multi storey RC frames; evolutionary algorithms

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Bekas, G.K.; Stavroulakis, G.E. Machine Learning and Optimality in Multi Storey Reinforced Concrete Frames. Infrastructures 2017, 2, 6.

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