PSI - Issue 77

Douaa Benhaddouche et al. / Procedia Structural Integrity 77 (2026) 152–160 Author name / Structural Integrity Procedia 00 (2026) 000 – 000

160

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5487.0000517 Rafiei, M.H., Adeli, H., 2018. A novel unsupervised deep learning model for global and local health condition assessment of structures. Engineering Structures 156, 598 – 607. https://doi.org/10.1016/j.engstruct.2017.10.070 Rastin, Z., Amiri, G.G., Darvishan, E., 2021. Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder. Shock and Vibration 11. Sakoe, H., Chiba, S., 1978. Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26 43 – 49. Soleimani-Babakamali, M.H., Soleimani-Babakamali, R., Sarlo, R., Farghally, M.F., Lourentzou, I., 2023. On the effectiveness of dimensionality reduction for unsupervised structural health monitoring anomaly detection. Mechanical Systems and Signal Processing 187, 109910. https://doi.org/10.1016/j.ymssp.2022.109910 Teng, Z., Teng, S., Zhang, J., Chen, G., Cui, F., 2020. Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network. Applied Sciences 10, 4720. https://doi.org/10.3390/app10144720 Wang, Z., 2023. A Novel Unsupervised Deep Learning Method with a Convolutional Neural Network for Structural Damage Detection, in: Dilworth, B.J., Marinone, T., Mains, M. (Eds.), Topics in Modal Analysis & Parameter Identification, Volume 8, Conference Proceedings of the Society for Experimental Mechanics Series. Springer International Publishing, Cham, pp. 105 – 112. https://doi.org/10.1007/978-3-031-05445 7_12 Wang, Z., Cha, Y.-J., 2021. Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage. Structural Health Monitoring 20, 406 – 425. https://doi.org/10.1177/1475921720934051 Wu, Y., Dai, H.-N., Tang, H., 2022. Graph Neural Networks for Anomaly Detection in Industrial Internet of Things. IEEE Internet Things J. 9, 9214 – 9231. https://doi.org/10.1109/JIOT.2021.3094295 Yu, Y., Wang, C., Gu, X., Li, J., 2019. A novel deep learning-based method for damage identification of smart building structures. Structural Health Monitoring 18, 143 – 163. https://doi.org/10.1177/1475921718804132