The identification of the damage in composite steel-concrete beams is addressed by implementing simple convolutional networks. By considering several damage scenarios, collections of images are generated by numerically evaluating a set of transmissibility functions relative to the generic damaged beam an by converting them into a gray level image suitably labeled. The images so generated are used to train simple convolutional networks capable to predict only the position or the position and the intensity of a single damage. The numerical experimentation carried out highlights the effectiveness of the proposed approach which does not require the adoption of predefined damage-related features.

Simple Convolutional Neural Networks for the Damage Identification in Composite Steel-Concrete Beams

Morassi A.;
2023-01-01

Abstract

The identification of the damage in composite steel-concrete beams is addressed by implementing simple convolutional networks. By considering several damage scenarios, collections of images are generated by numerically evaluating a set of transmissibility functions relative to the generic damaged beam an by converting them into a gray level image suitably labeled. The images so generated are used to train simple convolutional networks capable to predict only the position or the position and the intensity of a single damage. The numerical experimentation carried out highlights the effectiveness of the proposed approach which does not require the adoption of predefined damage-related features.
2023
978-3-031-39116-3
978-3-031-39117-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11390/1268027
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