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

Authors

Keywords:

Optimization, artificial neural networks (ANN), drift, seismic design, framed structures

Abstract


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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Author Biographies

Jorge Humberto Arcila-Zea, Universidad EAFIT

Ingeniero Civil-Universidad de Antioquia (2011)

Actualmente, estudiante de Maestría en Ingeniería Sismo-Resistente

 

Carlos Alberto Riveros-Jerez, Universidad de Antioquia

Facultad de Ingeniería

Javier Enrique Rivero-Jerez, Universidad de Antioquia

Facultad de Ingeniería, Docente

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Published

2014-02-12

How to Cite

Arcila-Zea, J. H., Riveros-Jerez, C. A., & Rivero-Jerez, J. E. (2014). Optimization of beam and column sections for compliance drift of reinforced concrete buildings using Artificial Neural Networks. Revista Facultad De Ingeniería Universidad De Antioquia, (70), 34–44. Retrieved from https://revistas.udea.edu.co/index.php/ingenieria/article/view/16382