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

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

Rozsas, A.
Slobbe, A.
Huizinga, W.
Kruithof, M.
ajithkumar Pillai, K.
Kleijn, K.
Giardina, G.

2022

This paper proposes an automated approach to predict crack pattern similarities that correlate well with assessment by structural engineers. We use Siamese convolutional neural networks (SCNN) that take two crack pattern images as inputs and output scalar similarity measures. We focus on 2D masonry facades with and without openings. The image pairs are generated using a statistics-based approach and labelled by 28 structural engineering experts. When the data is randomly split into fit and test data, the SCNNs can achieve good performance on the test data (R2≈0.9). When the SCNNs are tested on ”unseen” archetypes, their test R2 values are on average 1% lower than the case where all archetypes are ”seen” during the training. These very good results indicate that SCNNs can generalise to unseen cases without compromising their performance. Although the analyses are restricted to the considered synthetic images, the results are promising and the approach is general.

Crack patterns
deep neural network
machine learning
masonry structure
regression
similarity measure
Convolution
Convolutional neural networks
Masonry construction
Masonry materials
Automated approach
Convolutional neural network
Crack patterns
Machine-learning
Masonry structures
Pattern similarity
Performance
Regression
Similarity measure
Test data
Deep neural networks

http://resolver.tudelft.nl/uuid:fafbac80-6cf5-4946-814d-0380312fa458

979445

Taylor and Francis Ltd.

1558-3058

International Journal of Architectural Heritage

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