PSI - Issue 78

Angelo Aloisio et al. / Procedia Structural Integrity 78 (2026) 1–8

7

References

[1] Angelo Aloisio, Yuri De Santis, Francesco Irti, Dag Pasquale Pasca, Leonardo Scimia, and Massimo Fragiacomo. Machine learning predictions of code-based seismic vulnerability for reinforced concrete and masonry buildings: Insights from a 300-building database. Engineering Structures , 301:117295, 2024. [2] Angelo Aloisio, Marco Martino Rosso, Andrea Matteo De Leo, Massimo Fragiacomo, and Maria Basi. Damage classification after the 2009 l’aquila earthquake using multinomial logistic regression and neural networks. International Journal of Disaster Risk Reduction , 96:103959, 2023. [3] Angelo Aloisio, Marco Martino Rosso, Luca Di Battista, and Giuseppe Quaranta. Machine-learning-aided regional post-seismic usability prediction of buildings: 2016–2017 central italy earthquakes. Journal of Building Engineering , 91:109526, 2024. [4] Alex H Barbat, Martha L Carren˜o, Lluis G Pujades, Nieves Lantada, Omar D Cardona, and Mabel C Marulanda. Seismic vulnerability and risk evaluation methods for urban areas. a review with application to a pilot area. Structure and Infrastructure Engineering , 6(1-2):17–38, 2010. [5] S. Bertelli, T. Rossetto, and I. Ioannou. Derivation of empirical fragility functions from the 2009 aquila earthquake. In: 16Th European Conference on Earthquake Engineering, Thessaloniki , pages 1–12, 2018. cited By 6. [6] Giuseppe Brando, Gianfranco De Matteis, and Enrico Spacone. Predictive model for the seismic vulnerability assessment of small historic centres: application to the inner abruzzi region in italy. Engineering Structures , 153:81–96, 2017. [7] Giuseppe Brando, Davide Rapone, Enrico Spacone, and Maria Giovanna Masciotta. Mudis: A low computational e ff ort multi-unit discretization procedure for modelling masonry walls with periodic arrangement. In Structures , volume 43, pages 1380–1406. Elsevier, 2022. [8] Leo Breiman, J Friedman, R Olshen, and C Stone. Classification and regression trees–crc press. Boca Raton, Florida , 1984. [9] S.E. Chang, C. Pasion, K. Tatebe, and R. Ahmad. Linking lifeline infrastructure performance and community disaster resilience: Models and multi-stakeholder processes. Linking Lifeline Infrastructure Performance and Community Disaster Resilience: Models and Multi-Stakeholder Processes , 2008. cited By 22. [10] Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , pages 785–794, 2016. [11] A.W. Coburn, R.J.S. Spence, and A. Pomonis. Factors determining human casualty levels in earthquakes: Mortality prediction in building collapse. Proceedings of the 10th World Conference on Earthquake Engineering , pages 5989–5994, 1992. cited By 152. [12] G. De Martino, M. Di Ludovico, A. Prota, C. Moroni, G. Manfredi, and M. Dolce. Estimation of repair costs for rc and masonry residential buildings based on damage data collected by post-earthquake visual inspection. Bulletin of Earthquake Engineering , 15(4):1681–1706, 2017. cited By 54. [13] Ministero delle Infrastrutture e dei Trasporti. Norme tecniche per le costruzioni. Decreto Ministeriale , 2008. [14] M. Di Ludovico, G. De Martino, A. Prota, G. Manfredi, and M. Dolce. Relationships between empirical damage and direct / indirect costs for the assessment of seismic loss scenarios. Bulletin of Earthquake Engineering , 20(1):229–254, 2022. cited By 11. [15] Ministero delle Infrastrutture e dei Trasporti. Ntc2008–norme tecniche per le costruzioni. Technical report, DM 14 / 01 / 2008 [In Italian], 2008. [16] Antonio Formisano. Theoretical and numerical seismic analysis of masonry building aggregates: case studies in san pio delle camere (l’aquila, italy). Journal of Earthquake Engineering , 21(2):227–245, 2017. [17] Antonio Formisano, Gilda Florio, Ra ff aele Landolfo, and Federico M Mazzolani. Numerical calibration of an easy method for seismic behaviour assessment on large scale of masonry building aggregates. Advances in Engineering Software , 80:116–138, 2015. [20] David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant. Applied logistic regression , volume 398. John Wiley & Sons, 2013. [21] Alban Kita, Nicola Cavalagli, Maria Giovanna Masciotta, Paulo B Lourenc¸o, and Filippo Ubertini. Rapid post-earthquake damage localization and quantification in masonry structures through multidimensional non-linear seismic ida. Engineering Structures , 219:110841, 2020. [22] Pouria Kourehpaz and Carlos Molina Hutt. Machine learning for enhanced regional seismic risk assessments. Journal of Structural Engineering , 148(9):04022126, 2022. [23] Si-Qi Li and Paolo Gardoni. Empirical seismic vulnerability models for building clusters considering hybrid intensity measures. Journal of Building Engineering , 68:106130, 2023. [24] John Neter, William Wasserman, and Michael H Kutner. Applied linear regression models . Richard D. Irwin, 1983. [25] Fabian Pedregosa, Gae¨l Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research , 12:2825–2830, 2011. [26] Daniele Pellegrini, Maria Girardi, Paulo B Lourenc¸o, Maria Giovanna Masciotta, Nuno Mendes, Cristina Padovani, and Luis F Ramos. Modal analysis of historical masonry structures: Linear perturbation and software benchmarking. Construction and Building Materials , 189:1232– 1250, 2018. [27] R. Spence, E. So, and C. Scawthorn. A global earthquake building damage and casualty database. Human Casualties in Earthquakes: Progress in Modelling and Mitigation , 2011. cited By 43. [28] Z. Stojadinovic´, M. Kovacˇevic´, D. Marinkovic´, and B. Stojadinovic´. Rapid earthquake loss assessment based on machine learning and representative sampling. Earthquake Spectra , 38(1):152–177, 2022. [29] Gabriella Tocchi, Sushreyo Misra, Jamie E Padgett, Maria Polese, and Marco Di Ludovico. 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 Reduc tion , 97:104033, 2023. [30] Vladimir Vapnik and Vlamimir Vapnik. Statistical learning theory wiley. NewYork , 1(624):2, 1998. [31] Marco Vettore, Marco Dona`, Pietro Carpanese, Veronica Follador, Francesca da Porto, and Maria Rosa Valluzzi. A multilevel procedure at urban [18] Frank E. Harrell. Regression Modeling Strategies . Springer-Verlag, Berlin, Heidelberg, 2006. [19] Simon Haykin. Neural networks: a comprehensive foundation . Prentice Hall PTR, 1998.