Development Of A Neural Network Embedding For Quantifying Crack Pattern Similarity In Masonry Structures

Árpád Rózsás, Arthur Slobbe, Wyke Huizinga, Maarten Kruithof, Giorgia Giardina

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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The degree of similarity between damage patterns often correlates with the likelihood of having similar damage causes. Therefore, deciding whether crack patterns are similar is one of the key steps in assessing the conditions of masonry structures. To our knowledge, no literature has been published regarding masonry crack pattern similarity measures that would correlate well with assessment by structural engineers. Hence, currently, similarity assessments are solely performed by experts and require considerable time and effort. Moreover, it is expensive, limited by the availability of experts, and yields only qualitative answers. In this work, we propose an automated approach that has the potential to overcome the above shortcomings and perform comparably with experts. At its core is a deep neural network embedding that can be used to calculate a numerical distance between crack patterns on comparable façades. The embedding is obtained from fitting a deep neural network to perform a classification task; i.e., to predict the crack pattern archetype label from a crack pattern image. The network is fitted to synthetic crack patterns simulated using a statistics-based approach proposed in this work. The simulation process can account for important crack pattern characteristics such as crack location, orientation, and length. The embedding transforms a crack pattern (raster image) into a 64-dimensional real-valued vector space where the closeness between two vectors is calculated as the cosine of their angle. The proposed approach is tested on 2D façades with and without openings, and with synthetic crack patterns that consist of a single crack and multiple cracks.
Original languageEnglish
Title of host publication12th International Conference on Structural Analysis of Historical Constructions
Subtitle of host publicationSAHC 2021, Online event, 29 Sep - 1 Oct, 2021
EditorsP. Roca, L. Pelà , C. Molins
Place of PublicationSpain
PublisherInternational Centre for Numerical Methods in Engineering, CIMNE
Number of pages12
ISBN (Print)978-84-123222-0-0
Publication statusPublished - 2021
Event12th International Conference on Structural Analysis of Historical Constructions - Online event
Duration: 29 Sep 20211 Oct 2021
Conference number: 12


Conference12th International Conference on Structural Analysis of Historical Constructions
Abbreviated titleSAHC
Internet address


  • Masonry Structure
  • Crack Patterns
  • Similarity Measure
  • Machine Learning
  • Deep Neural Network

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