Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades

Arpad Rozsasa Department of Structural Reliability, TNO Building, Infrastructure, and Maritime, Delft, The Netherlands

https://orcid.org/0000-0003-2679-1384

Arthur Slobbea Department of Structural Reliability, TNO Building, Infrastructure, and Maritime, Delft, The Netherlands

Wyke Huizingab Department of Intelligent Imaging, TNO Defense, Safety, and Security, The Hague, The Netherlands

Maarten Kruithofb Department of Intelligent Imaging, TNO Defense, Safety, and Security, The Hague, The Netherlands

Krishna Ajithkumar Pillaia Department of Structural Reliability, TNO Building, Infrastructure, and Maritime, Delft, The Netherlands;c Department of Geoscience and Engineering, Delft University of Technology, Delft, The Netherlands

Kelvin Kleijnb Department of Intelligent Imaging, TNO Defense, Safety, and Security, The Hague, The Netherlands

Giorgia Giardinac Department of Geoscience and Engineering, Delft University of Technology, Delft, The NetherlandsCorrespondence[email protected]

https://orcid.org/0000-0002-5996-5830

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Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades

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Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades

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