Optimization of beam and column sections for compliance drift of reinforced concrete buildings using Artificial Neural Networks

This article presents the application of Artificial Neural Networks (ANN) to estimate optimal sections of beams and reinforced concrete columns for symmetric framed buildings with 1-6 floors taking into consideration the minimum requirements of the NSR-10 related with drift and seismic design. It is...

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Published in:Revista Facultad de Ingeniería 2014-03 (70), p.34-44
Main Authors: Arcila Zea, Jorge, Riveros Jerez, Carlos Alberto, Rivero Jerez, Javier Enrique
Format: Article
Language:eng ; spa
Subjects:
Artificial neural networks
artificial neural networks (ANN)
Beams (structural)
Buildings
Drift
framed structures
Learning theory
Neural networks
Optimization
Reinforced concrete
seismic design
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Riveros Jerez, Carlos Alberto
Rivero Jerez, Javier Enrique
description This article presents the application of Artificial Neural Networks (ANN) to estimate optimal sections of beams and reinforced concrete columns for symmetric framed buildings with 1-6 floors taking into consideration the minimum requirements of the NSR-10 related with drift and seismic design. It is also studied the sensitivity of drift to the values of dimensions of beamsand columns providing a better understanding of this relationship in order to obtain optimal designs more quickly, easily and reliably as compared to current used procedures.
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subjects Artificial neural networks
artificial neural networks (ANN)
Beams (structural)
Buildings
Drift
framed structures
Learning theory
Neural networks
Optimization
Reinforced concrete
seismic design
title Optimization of beam and column sections for compliance drift of reinforced concrete buildings using Artificial Neural Networks
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