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





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


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 Arcila Zea, EAFIT University

Civil Engineer-University of Antioquia (2011). Currently, a Master's student in Earthquake-Resistant Engineering.

Carlos Alberto Riveros Jerez, University of Antioquia

Faculty of Engineering.

Javier Enrique Rivero Jerez, University of Antioquia

Faculty of Engineering, Teacher.


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How to Cite

Arcila Zea, J. ., 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. https://doi.org/10.17533/udea.redin.16382

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