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Mercedes Solla et al. / Procedia Structural Integrity 64 (2024) 293–300 M. Solla et al. / Structural Integrity Procedia 00 (2019) 000–000

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Acknowledgements Work produced with the support of a 2022 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. The BBVA Foundation takes no responsibility for the opinions, statements, and contents of this project, which are entirely the responsibility of its authors. This work also received financial support from the OVERSIGHT project (PID2022-138526OB-I00) funded by MCIN/AEI/10.13039/501100011033, UE. M. Solla acknowledges the grant RYC2019–026604–I funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”. A. Elseicy acknowledges the Grant PREP2022-000030 for the training of predoctoral researchers funded by MCIN/AEI /10.13039/501100011033 and by FSE+. Data Availability Statement The GPR data presented in this study are openly available at https://zenodo.org/records/10962520 under a GNU General Public License (v3) and archived at DOI: https://doi.org/10.5281/zenodo.10962520. References Asadi, P., Gindy, M., Alvarez, M., 2019. A Machine Learning Based Approach for Automatic Rebar Detection and Quantification of Deterioration in Concrete Bridge Deck Ground Penetrating Radar B-scan Images. KSCE J. Civ. Eng. 23(6), 2618-2627. Diamanti, N., Annan, P., Redman, J.D., 2017. Concrete Bridge Deck Deterioration Assessment Using Ground Penetrating Radar (GPR). Journal of Environmental & Engineering Geophysics 22(2), 121-132. Elseicy, A., Alonso-Díaz, A., Solla, M., Rasol, M., Santos-Assunçao, S., 2022. Combined use of GPR and other NDTs for road pavement assessment: an overview. Remote Sensing 14, 4336. Ichi, E., Dorafshan, S., 2021. SDNET2021: Annotated NDE Dataset for Structural Defects. Datasets, doi: 10.31356/data019. Kuchipudi, S.T., Ghosh, D., Gupta, H., 2022. Automated Assessment of Reinforced Concrete Elements using Ground Penetrating Radar. Autom. Constr. 140, 104378. Li, X., Liu, H., Zhou, F., Chen, Z., Giannakis, I., Slob, E.C., 2022. Deep learning–based nondestructive evaluation of reinforcement bars using ground-penetrating radar and electromagnetic induction data. Computer-Aided Civil and Infrastructure Engineering 37(14), 1834-1853. Novo, A., Kaufmann, M., 2023. A Novel Web-Based Software for Automated Cloud Processing, AI-Assisted Analysis and 3D Visualization of GPR Data. In 2023 12th International Workshop on Advanced Ground Penetrating Radar (IWAGPR) (pp. 1-4). IEEE. Rasol, M., Pais, J.C., Pérez-Gracia, V., Solla, M., Fernandes, F.M., Fontul, S., Ayala-Cabrera, D., Schmidt, F., Assadollahi, H., 2022a. GPR monitoring for road transport infrastructures: A systematic review and machine learning insights. Construction and Building Materials 324, 126686. Rasol, M.A., Pérez-Gracia, V., Fernandes, F.M., Pais, J.C., Solla, M., Santos, C., 2022b. NDT assessment of rigid pavement damages with ground penetrating radar: laboratory and field tests. International Journal of Pavement Engineering 23(3), 900-915. Solla, M. 2024. GPR dataset: pulsed radar and SFCW data for rebar detection (experimental data) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10962520 Solla, M., Fernández, N., 2023. GPR analysis to detect subsidence: A case study on a loaded reinforced concrete pavement. Int. J. Pavement Engineering 24, 2027420. Tešić, K., Baričević, A., Serdar, M., 2021. Non-Destructive Corrosion Inspection of Reinforced Concrete Using Ground-Penetrating Radar: A Review. Materials 14, 975.