Bibliographic citations
Sanchez, M., Vidal, P. (2021). Optimización de dimensiones de elementos estructurales mediante el uso de redes neuronales para la reducción de sobrecostos en edificios multifamiliares de 6 pisos ubicado en el distrito de Miraflores [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/655741
Sanchez, M., Vidal, P. Optimización de dimensiones de elementos estructurales mediante el uso de redes neuronales para la reducción de sobrecostos en edificios multifamiliares de 6 pisos ubicado en el distrito de Miraflores [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/655741
@misc{renati/394893,
title = "Optimización de dimensiones de elementos estructurales mediante el uso de redes neuronales para la reducción de sobrecostos en edificios multifamiliares de 6 pisos ubicado en el distrito de Miraflores",
author = "Vidal Feliz, Pool Rusbel",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
Cost overruns in the construction of reinforced concrete buildings represent losses of 28% of the investment (Flyvbjerg, 2002), this is due to the fact that the sections of the structural elements are oversized and generate higher costs in concrete and steel. Therefore, the present research was carried out using a methodology capable of optimizing the dimensions of structural elements (columns, beams and slabs) in regular 6-story multifamily buildings. The methodology used was based on the use of feedforward neural networks, in which the preliminary data of a building were established as input variables and the dimensions of each structural element as output variables. For this purpose, 30 6-story buildings were prepared as a database and in each one of them the drift verifications were performed according to the Peruvian Technical Standard E 0.30 and the resistance of each structural element. From the database, 22 were used for training and 8 for the internal validation of the neural network. The structure of the neural network was established after running 10 different neural networks and the network with the most homogeneous correlation coefficient close to 1 was selected; in this research it was 0.98. Finally, a comparison was made between the volume of concrete used in a building dimensioned with conventional methods with the use of ETABS software and those obtained with the use of the methodology employing artificial neural networks, according to this, the difference of concrete between both cases was calculated. With the results obtained, it was proved that the methodology applied in this research provides an effective saving close to 10%.
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