PSI - Issue 84

Gianluca Quinci et al. / Procedia Structural Integrity 84 (2026) 199–206

206

Quinci, G., Paolacci, F., Fragiadakis, M., & Bursi, O. S. (2025). A machine learning framework for seismic risk assessment of industrial equipment. Reliability Engineering & System Safety, 254, 110606. https://doi.org/10.1016/j.ress.2024.110606 Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. Shinozuka, M., et al. (2000). Statistical analysis of fragility curves. Journal of Engineering Mechanics, 126(12), 1224–1231. Skokandić, D., Vlašić, A., Marić, M. K., Srbić, M., & Ivanković, A. M. (2022). Seismic assessment and retrofitting of existin g road bridges: State of the art review. Materials, 15, 2523. https://doi.org/10.3390/ma15072523 Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. Somvanshi, M., et al. (2016). A review of machine learning techniques using decision tree and support vector machine. 2016 International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE. Xie, Y., Ebad Sichani, M., Padgett, J. E., & DesRoches, R. (2020). The promise of implementing machine learning in earthquake engineering: A state-of-the-art review. Earthquake Spectra, 36(4), 1769–1801. https://doi.org/10.1177/8755293020919419 Zhao, R., et al. (2021). State-of-the-art and annual progress of bridge engineering in 2020. Advances in Building Engineering, 29, 2–29. https://doi.org/10.1186