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In the present study, the authors analyzed the exertion of various deep learning models in order to increase the productivity of classifying ground penetrating radar (GPR) images for SHM purposes, especially focusing on road tunnel linings evaluations. Specifically, the authors presented a comparative study employing two convolutional models, i.e. the ResNet-50 and the EfficientNet-B0, and a recent transformer model, i.e. the Vision Transformer (ViT). Precisely, the authors evaluated the effects of training the models with or without pre-processed data through the bi-dimensional Fourier transform. Despite the theoretical advantages envisaged by adopting this kind of pre-processing technique on GPR images, the best classification performances have been still manifested by the classifiers trained without the Fourier pre-processing.<\/jats:p>","DOI":"10.3233\/ica-230709","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T17:34:44Z","timestamp":1683912884000},"page":"213-232","source":"Crossref","is-referenced-by-count":23,"title":["Comparative deep learning studies for indirect tunnel monitoring with and without Fourier pre-processing"],"prefix":"10.1177","volume":"31","author":[{"given":"Marco Martino","family":"Rosso","sequence":"first","affiliation":[{"name":"DISEG, Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Turin, Italy"}]},{"given":"Angelo","family":"Aloisio","sequence":"additional","affiliation":[{"name":"DICEAA, Civil Environmental and Architectural Engineering Department, University of L\u2019Aquila, L\u2019Aquila, Italy"}]},{"given":"Vincenzo","family":"Randazzo","sequence":"additional","affiliation":[{"name":"DISEG, Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Turin, Italy"}]},{"given":"Leonardo","family":"Tanzi","sequence":"additional","affiliation":[{"name":"DISEG, Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Turin, Italy"}]},{"given":"Giansalvo","family":"Cirrincione","sequence":"additional","affiliation":[{"name":"Laboratoire des Technologies Innovantes (LTI), University of Picardie Jules Verne, Amiens, France"}]},{"given":"Giuseppe Carlo","family":"Marano","sequence":"additional","affiliation":[{"name":"DISEG, Department of Structural, Geotechnical and Building Engineering, Politecnico di Torino, Turin, Italy"}]}],"member":"179","reference":[{"key":"10.3233\/ICA-230709_ref1","doi-asserted-by":"crossref","first-page":"127554","DOI":"10.1016\/j.conbuildmat.2022.127554","article-title":"Durability degradation of tunnel-lining-shotcrete exposed to nitric acid: Neutralization and nitrate ion migration","volume":"336","author":"Wang","year":"2022","journal-title":"Construction and Building Materials"},{"key":"10.3233\/ICA-230709_ref2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15732479.2022.2127795","article-title":"Structural behavior degradation of corroded metro tunnel lining segment","author":"Zhang","year":"2022","journal-title":"Structure and Infrastructure Engineering"},{"key":"10.3233\/ICA-230709_ref3","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1201\/9781003307822-15","article-title":"Structural Health Monitoring of Existing Building Structures for Creating Green Smart Cities Using Deep Learning","author":"Kapoor","year":"2022","journal-title":"Recurrent Neural Networks"},{"key":"10.3233\/ICA-230709_ref4","doi-asserted-by":"crossref","unstructured":"Tyagi AK, Abraham A. 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