PSI - Issue 78

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

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scale to assess the vulnerability and the exposure of residential masonry buildings: The case study of pordenone, northeast italy. Heritage , 3(4):1433–1468, 2020. [32] W. Wen, C. Zhang, and C. Zhai. Rapid seismic response prediction of rc frames based on deep learning and limited building information. Engi neering Structures , 267, 2022. cited By 4. [33] Ji-Gang Xu, De-Cheng Feng, Sujith Mangalathu, and Jong-Su Jeon. Data-driven rapid damage evaluation for life-cycle seismic assessment of regional reinforced concrete bridges. Earthquake Engineering & Structural Dynamics , 51(11):2730–2751, 2022. [34] Yu XuanRui. Developing an artificial neural network model to predict the durability of the rc beam by machine learning approaches. Case Studies in Construction Materials , 17:e01382, 2022. [35] G. Zuccaro and F. Cacace. Revisione dell’inventario a scala nazionale delle classi tipologiche di vulnerabilita` ed aggiornamento delle mappe nazionali di rischio sismico. Revisione dell’inventario a Scala Nazionale Delle Classi Tipologiche Di vulnerabilita` Ed Aggiornamento Delle Mappe Nazionali Di Rischio Sismico. Atti Del XIII Convegno ANIDIS , 2009. cited By 9. [36] M. Zucconi, F. Romano, and B. Ferracuti. Typological fragility curves for rc buildings: influence of damage index and building sample selection. Engineering Structures , 266, 2022. cited By 7.