PSI - Issue 78

Merani Margherita Gabriella Bruna et al. / Procedia Structural Integrity 78 (2026) 785–792

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primarily depends on the structural type. This hybrid approach allows us to extract the most relevant FA value for each building class, matching it to either a measured or a calculated period. Adopting this second approach is crucial because it directly models the physical interplay between the ground and the structure. By matching site amplification to the building's fundamental period, the method accounts for soil-structure resonance, a phenomenon that can lead to a dramatic increase in seismic demand. This effect is most pronounced for stiff structures like unreinforced masonry buildings, where using a generic, frequency-independent factor can result in a significant underestimation of risk. This refined hazard characterization provides a more accurate input for the subsequent damage assessment. Figure 4 highlights the critical impact of the chosen hazard characterization by contrasting different amplification methodologies. The first three panels (4a-c) show period-dependent amplification factors (FA) from detailed microzonation (MS) studies, while Figure 4d shows the simplified, period-independent factor (FS) from the ITA18 attenuation law. The comparison reveals profound differences. The MS-based factors show significant spatial variability, identifying a distinct "hotspot" of high amplification (up to 2.4) in the northwest, especially for short-period structures (Figure 4a). The simplified FS factor, however, produces a uniform field that completely fails to capture this localized hazard. Furthermore, the figure illustrates that the hotspot is prominent for short periods (0.1-0.5s), diminishes for medium periods, and nearly disappears for longer periods. This demonstrates the critical importance of structural monitoring. By identifying a building's fundamental period, we can select the correct, period-specific amplification factor from the MS results. This leads to a much more accurate assessment of seismic demand and avoids the significant over- or under-estimation of risk that occurs when using a generic or mismatched factor.

The practical utility of integrating monitoring data into damage scenarios is twofold. On strategic structures, it provides quantitative data (e.g., drift) to support operationality assessments against predefined thresholds (Mori et al., 2015). For more common building typologies, data from a few instrumented structures can be used to update the fragility curves for an entire subclass, thereby improving damage estimates for all similar, unmonitored buildings. To demonstrate this, the impact of the different hazard characterizations is subsequently propagated to the final damage assessment. For this scenario, damage was estimated using fragility curves based on the Displacement Based Vulnerability DBV-Masonry mechanical approach (Lagomarsino and Cattari, 2013). Figure 5 contrasts the resulting damage scenario for the masonry building stock within the surveyed census sections. The top row displays the mean damage grade ( ), an index that summarizes the overall expected level of structural damage on a scale from 0 (no damage) to 5 (collapse). The map based on the refined FA factor (Figure 5a) reveals a significant spatial variability in the expected damage, pinpointing localized areas with relatively elevated values. In contrast, the scenario using the generic FS factor (Figure 5b) fails to capture these local differences, predicting a more uniform and generally lower level of damage across the area. This pattern is mirrored in the bottom row, which shows the estimated number of masonry unusable buildings. The FA-based assessment (Figure 5c) pinpoints a high concentration of unusable buildings in the same critical zones. Conversely, the FS-based assessment (Figure 5d) Figure 4: Comparison of amplification factors derived from different approaches: (a) FA from SM for the 0.1-0.5 s period range; (b) FA from SM for the 0.4-0.8 s period range; (c) FA from SM for the 0.7-1.1 s period range; (d) simplified FS factor based on the ITA18 attenuation law