The geometrical description of the components of rubble masonries constitutes a key-point in the definition of their mechanical response. A variational autoencoder (VAE) is proposed as a tool for the automatic description and generation of rubble masonry geometries. The encoder and the decoder forming the VAE are implemented by defining two convolutional neural networks trained by using binary images extracted from a publicly available masonry database.

Automatic Description of Rubble Masonry Geometries by Machine Learning Based Approach / Bilotta, A., Causin, A., Solci, M., Turco, E.. - 55:(2023), pp. 51-67.

Automatic Description of Rubble Masonry Geometries by Machine Learning Based Approach

Causin, Andrea;Solci, Margherita;Turco, Emilio
2023-01-01

Abstract

The geometrical description of the components of rubble masonries constitutes a key-point in the definition of their mechanical response. A variational autoencoder (VAE) is proposed as a tool for the automatic description and generation of rubble masonry geometries. The encoder and the decoder forming the VAE are implemented by defining two convolutional neural networks trained by using binary images extracted from a publicly available masonry database.
2023
Inglese
Bretti, G. et al.
55
Mathematical Modeling in Cultural Heritage MACH2021
51
67
17
978-981-99-3678-6
Springer
Singapore
SINGAPORE
Esperti anonimi
Rubble masonry geometries Variational autoencoder VAE Machine learning approach
Internazionale
info:eu-repo/semantics/bookPart
Bilotta, Antonio; Causin, Andrea; Solci, Margherita; Turco, Emilio
2 Contributo in Volume::2.1 Contributo in volume (Capitolo o Saggio)
4
268
Automatic Description of Rubble Masonry Geometries by Machine Learning Based Approach / Bilotta, A., Causin, A., Solci, M., Turco, E.. - 55:(2023), pp. 51-67.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11388/315370
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