Structural design of confined masonry buildings using artificial neural networks

Juan Carlos Sicha Pillaca, Alexander Molina Ramirez, Victor Arana Vasquez

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The aim of this article is to use artificial neural networks (ANN) to perform the structural design of confined masonry buildings. ANN is easy to operate and allows to reduce the time and cost of seismic designs. To generate the artificial neural network, training models (traditional confined masonry designs) are used to identify the input and output parameters. From this, the final architecture and activation functions are defined for each layer of the ANN. Finally, ANN training is carried out using the backpropagation algorithm to obtain the matrix of weights and thresholds that allow the network to operate and provide preliminary structural designs with a 10% margin of error, with respect to the traditional design, in the dimensions and reinforcements of the structural elements.

Idioma originalInglés
Título de la publicación alojada2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings
EditoresMonica Andrea Rico Martinez
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781728194660
DOI
EstadoPublicada - 30 set. 2020
Publicado de forma externa
Evento2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - 2020 International Conference on Innovation and Trends in Engineering, CONIITI 2020 - Bogota, Colombia
Duración: 30 set. 20202 oct. 2020

Serie de la publicación

Nombre2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - Conference Proceedings

Conferencia

Conferencia2020 Congreso Internacional de Innovacion y Tendencias en Ingenieria, CONIITI 2020 - 2020 International Conference on Innovation and Trends in Engineering, CONIITI 2020
País/TerritorioColombia
CiudadBogota
Período30/09/202/10/20

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