Structural design of confined masonry buildings using artificial neural networks

• Main Authors: Sicha Pillaca, Juan Carlos, Molina Ramirez, Alexander, Vasquez, Victor Arana
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Subjects: artificial intelligence; artificial neural networks; confined masonry; structural design
• Online Access: http://hdl.handle.net/10757/656414

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. === 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.