PSI - Issue 78

Marco Martino Rosso et al. / Procedia Structural Integrity 78 (2026) 1382–1388

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Acknowledgements Marco Martino Rosso, Giuseppe Carlo Marano, and Giuseppe Quaranta acknowledge the support received through the project “Artificial Intelligence for SUstainable seismic risk reduction of STructures (AI - SUST)” (project code: 2022LEFKHS) funded by European Union -- NextGeneration EU through the PRIN 2022 program of the Italian Ministry of University and Research (MUR) (D. D. n. 104, 02-02-2022). This work reflects only the authors' views and opinions whereas the MUR cannot be considered responsible for them. Cristoforo Demartino acknowledge the support received through the project the «Artificial Intelligence for ENVIronmental impact minimization of SEismic Retrofitting of Structures (AI-ENVISERS)» project – funded by European Union – Next Generation EU within the PRIN 2022 PNRR program (D.D.1409 del 14/09/2022 Ministero dell’Università e della Ricerca). This manuscript reflects only the authors’ views and opinions and the Ministry cannot be considered responsible for them. Zucconi, M., Sorrentino, L., & Ferlito, R. (2017). Principal component analysis for a seismic usability model of unreinforced masonry buildings. Soil Dynamics and Earthquake Engineering, 96, 64-75. Bertelli, S., Rossetto, T., & Ioannou, I. (2018). Derivation of empirical fragility functions from the 2009 l'aquila earthquake. In Prooceedings 16th European conference on earthquake engineering (Vol. 16). European Association of Earthquake Engineering. Tocchi, G., Misra, S., Padgett, J. E., Polese, M., & Di Ludovico, M. (2023). The use of machine-learning methods for post-earthquake building usability assessment: A predictive model for seismic-risk impact analyses. International journal of disaster risk reduction, 97, 104033. Aloisio, A., Rosso, M. M., Di Battista, L., & Quaranta, G. (2024). Machine-learning-aided regional post-seismic usability prediction of buildings: 2016 – 2017 Central Italy earthquakes. Journal of Building Engineering, 91, 109526. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273-297. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32. Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007). Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, 8(1), 25. Fiorentino, G., Forte, A., Pagano, E., Sabetta, F., Baggio, C., Lavorato, D., Nuti, C., & Santini, S. (2018). Damage patterns in the town of Amatrice after August 24th 2016 Central Italy earthquakes. Bulletin of Earthquake Engineering, 16(3), 1399-1423. Luzi, L., Pacor, F., Puglia, R., Lanzano, G., Felicetta, C., D ’ Amico, M., Michelini, A., Faenza, L., Lauciani, V., Iervolino, I., & Chioccarelli, E. (2017). The central Italy seismic sequence between August and December 2016: Analysis of strong ‐ motion observations. Seismological Research Letters, 88(5), 1219-1231. References