Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades-Reference-Cited by-同舟云学术

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

Published:2022-10-13 Issue:1 Volume:17 Page:147-169
ISSN:1558-3058
Container-title:International Journal of Architectural Heritage
language:en
Short-container-title:International Journal of Architectural Heritage

Author:

Rozsas Arpad¹^ORCID,Slobbe Arthur¹,Huizinga Wyke²,Kruithof Maarten²,Ajithkumar Pillai Krishna¹³,Kleijn Kelvin²,Giardina Giorgia³^ORCID

Affiliation:

1. Department of Structural Reliability, TNO Building, Infrastructure, and Maritime, Delft, The Netherlands

2. Department of Intelligent Imaging, TNO Defense, Safety, and Security, The Hague, The Netherlands

3. Department of Geoscience and Engineering, Delft University of Technology, Delft, The Netherlands

Publisher

Informa UK Limited

Subject

Visual Arts and Performing Arts,Architecture,Conservation

Link

https://www.tandfonline.com/doi/pdf/10.1080/15583058.2022.2134062

Reference43 articles.

1. Ajithkumar Pillai, K. 2022. Integrating machine learning and computational physics to assess crack pattern similarity in masonry buildings. M.Sc. thesis, Delft University of Technology, Civil Engineering and Geosciences

2. Crack Survey in Unreinforced Concrete or Masonry Abutments in Short- and Medium-Span Bridges

3. Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

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