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...

Full description

Saved in:
Bibliographic Details
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
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0120-6230
2422-2844
DOI:10.17533/udea.redin.16382