Structural design of confined masonry buildings using artificial neural networks

Abstract

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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.