GNGTS 2024 - Atti del 42° Convegno Nazionale

Session 2.2 GNGTS 2024 (a) (b) (c) (d) (e) (f) (g) (h) (i) (l) Fig. 1 – Damage data extracted from (Petrovski et al. 1984): a) unconfined stone masonry buildings (URM-St), b) unconfined brick masonry buildings (URM-Br), and (c) confined masonry (CFM). From (d) to (l): damage data extracted from (Pavićević Božidar S 2004) for 7 municipalities. Given that both reports (Petrovski et al. 1984) and (Pavićević Božidar S 2004) are derived from surveys related to the same SE, we conducted data manipulation to extract pertinent information for establishing anchor points in fragility functions. Specifically, we initially computed, based on Fig. 1 (a) - (c), the percentage of URM-St, URM-Br, and CFM buildings within each DS. This information has been compiled and is presented in Tab. 3. Damage State URM-St URM-Br CFM DS0 32% 48% 21% DS1 57% 29% 13% DS2 81% 13% 6% DS3 92% 6% 3% DS4 94% 4% 2% DS5 96% 3% 2% Tab. 3 – Distribution of building classes across damage states: derived from Fig. 1 (a) - (c). Then, by applying the percentages of Tab. 3 to the data in Fig. 1 (d)-(l), we determine the number of buildings corresponding to each class falling in each DS. For the sake of conciseness, we do not (a) (b) (c) (a) (b) (c) (a) (b) (c)