Scipediacontent: Scipediacontent moved page Draft Content 942401603 to Rozsas et al 2021a

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Scipediacontent moved page Draft Content 942401603 to Rozsas et al 2021a

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Created page with "== Abstract == The degree of similarity between damage patterns often correlates with the likelihood of having similar damage causes. Therefore, deciding whether crack patte..."

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== Abstract ==

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.

== Full document ==
<pdf>Media:Draft_Content_942401603p1087.pdf</pdf>
== References ==

[1] Silva, W. and Schwerz de Lucena, D. Concrete cracks detection based on deep learning image classification. In: The Eighteenth International Conference of Experimental Mechanics, Vol. II (2018), pp. 5387.

[2] Chaiyasarn, K., Khan, W., Sharma, M., Brackenbury, D., and DeJong, M. Crack detection in masonry structures using convolutional neural networks and support vector machines. In: Proceedings of the 35th ISARC, Berlin, Germany (2018), pp.118-125.

[3] Dung, C. V. and Anh, L. D. Autonomous concrete crack detection using deep fully convolutional neural network. Automation in Construction (2019) 99:52-58.

[4] Napolitano, R. and Glisic, B. Methodology for diagnosing crack patterns in masonry structures using photogrammetry and distinct element modeling. Engineering Structures (2019) 181:519-528.

[5] de Vent, I. Prototype of a diagnostic decision support tool for structural damage in masonry. PhD thesis, Delft University of Technology (2011).

[6] Wang, S.-C. Artificial Neural Network. In: Interdisciplinary Computing in Java Programming, Springer US, Boston,MA, (2003), pp. 81-100.

[7] Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., and Alsaadi, F. E. A survey of deep neural network architectures and their applications. Neurocomputing, (2017) 234:11-26.

[8] Arel, I., Rose, D. C., and Karnowski, T. P. Deep machine learning - a new frontier in artificial intelligence research. In: IEEE Computational Intelligence Magazine, (2010) 5(4):13–18.

[9] van der Maaten, L. and Hinton, G. Visualizing data using t-SNE. Journal of Machine Learning Research, (2008) 9:2579–2605.

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Scipediacontent: Scipediacontent moved page Draft Content 942401603 to Rozsas et al 2021a

Scipediacontent: Created page with "== Abstract == The degree of similarity between damage patterns often correlates with the likelihood of having similar damage causes. Therefore, deciding whether crack patte..."