PSI - Issue 64

Ricardo Perera et al. / Procedia Structural Integrity 64 (2024) 1369–1375 Author name / Structural Integrity Procedia 00 (2019) 000–000

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function, computed from the difference between the reconstructed outputs and their original input EMI spectrums can be used as damage sensitive feature. Acknowledgements This research was funded by the Spanish Ministry of Science and Innovation (MCIN/AEI), grants number PID2020‐119015GB‐C21 and PID2020‐119015GB‐C22. References Ahmed, A.S., El-Behaidy, W.H., Youssif, A.A.A., 2021. Medical image denoising system based on stacked convolutional autoencoder for enhancing 2-dimensional gel electrophoresis noise reduction. Biomedical Signal Processing and Control 69, 102842. https://doi.org/10.1016/j.bspc.2021.102842 Al-Saadi, N.T.K., Mohammed, A., Al-Mahaidi, R., Sanjayan, J., 2019. A state-of-the-art review: Near-surface mounted FRP composites for reinforced concrete structures. Construction and Building Materials 209, 748-769. https://doi.org/10.1016/j.conbuildmat.2019.03.121 Choi, S.H., Choi, H.J., Min, C.H., Chung, Y.H., Ahn, J.J., 2021. Development of de-noised image reconstruction technique using Convolutional AutoEncoder for fast monitoring of fuel assemblies. Nuclear Engineering and Technology 53(3), 888-893. https://doi.org/10.1016/j.net.2020.08.020. Dasan, E., Panneerselvam, I., 2021. A novel dimensionality reduction approach for ECG signal via convolutional denoising autoencoder with LSTM. Biomedical Signal Processing and Control 63, 102225. https://doi.org/10.1016/j.bspc.2020.102225. Dong, J., Wang, Q., Guan, Z., 2013. Structural behaviour of RC beams with external flexural and flexural–shear strengthening by FRP sheets. Composites Part B Engineering 44, 604-612. https://doi.org/10.1016/j.compositesb.2012.02.018. Fettah, A., Menassel, R., Gattal, A., Gattal, A., 2024. Convolutional Autoencoder-Based medical image compression using a novel annotated medical X-ray imaging dataset. Biomedical Signal Processing and Control 94, 106238. https://doi.org/10.1016/j.bspc.2024.106238 Lee, K., Jeong, S., Sim, S.H., Shin, D.H., 2021. Field experiment on a PSC-I bridge for convolutional autoencoder-based damage detection. Structural Health Monitoring 20 (4), 1627–1643. Ortiz, J., Dolati, S.S.K., Malla, P., Nanni, A., Mehrabi, A., 2023. FRP-Reinforced/Strengthened Concrete: State-of-the-Art Review on Durability and Mechanical Effects. Materials 16(5), 1990. https://doi.org/10.3390/ma16051990 Siddika, A., Al Mamun, M.A., Alyousef, R., Amran, Y.M., 2019. Strengthening of reinforced concrete beams by using fiber-reinforced polymer composites: a review. Journal of Building Engineering 25, 100798. Yessoufou, F., Zhu, J., 2023. Deep autoencoder model for direct monitoring of bridges subjected to a moving vehicle load under varying temperature conditions. Structures 52, 752-767. https://doi.org/10.1016/j.istruc.2023.03.171 Yuan, Z., Zhu, S. Chang, C., Yuan, X., Zhang, Q., Zhai, W., 2021. An unsupervised method based on convolutional variational auto-encoder and anomaly detection algorithms for light rail squat localization. Construction and Building Materials 313, 125563. https://doi.org/10.1016/j.conbuildmat.2021.125563 Fachinger, J., den Exter, M., Grambow, B., Holgerson, S., Landesmann, C., Titov, M., Podruhzina, T., 2004. Behavior of spent HTR fuel elements in aquatic phases of repository host rock formations, 2nd International Topical Meeting on High Temperature Reactor Technology. Beijing, China, paper #B08. Fachinger, J., 2006. Behavior of HTR Fuel Elements in Aquatic Phases of Repository Host Rock Formations. Nuclear Engineering & Design 236, 54.