Citas bibligráficas
Molina, A., Sicha, J. (2021). Diseño estructural de viviendas de albañilería confinada mediante el uso de redes neuronales artificiales en distritos de Lima con perfil de suelo tipo S1 [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/655739
Molina, A., Sicha, J. Diseño estructural de viviendas de albañilería confinada mediante el uso de redes neuronales artificiales en distritos de Lima con perfil de suelo tipo S1 [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2021. http://hdl.handle.net/10757/655739
@misc{renati/394932,
title = "Diseño estructural de viviendas de albañilería confinada mediante el uso de redes neuronales artificiales en distritos de Lima con perfil de suelo tipo S1",
author = "Sicha Pillaca, Juan Carlos",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2021"
}
Artificial Neural Networks (ANN) have been developed in the field of structural engineering more and more over the years, this tool tries to simulate the behavior of biological neurons allowing to adapt to any environment and solve different types of problems. In the present research, it is applied to the structural design of masonry houses confined to regular structures with a rectangular geometry in plan. The application of the neuronal network in this field allows us to save time and design costs. Likewise, it only requires people with basic knowledge of computer handling or mobile applications for the operation of the neuronal network. In this way, it is easier to provide preliminary structural designs to users with limited economic resources who wish to build confined masonry housing. In the present investigation, the neural networks are applied for the structural design of confined masonry houses from 1 to 4 floors located in in Lima districts with soil profile type S1. For this, the design of 33 houses that meet the specifications of the National Building Regulations (E020, E030 and E070) was carried out, these designs were used as training models for the learning process of the neural network and in this way obtained an RNA model capable of designing confined masonry houses with an error of less than 10%.
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