More specifically, for the hazard, the role of the site effects due to stratigraphic soil conditions has been investigated, expressed by various alternatives to quantify the soil amplification factor. This possible site effect is a crucial phenomenon where the geological nature of the ground can amplify seismic waves, significantly increasing the intensity of surface shaking compared to that on solid bedrock. The available options that the user may test include:

• F_S (ITA18) – based on maps from Forte and Mori: This option calculates the site term F_S used by the most recent Italian Ground Motion Prediction Equation (GMPE) of Lanzano et al. (2019) and based on the physical properties of the first 30 meters of subsoil (a standard parameter known as V_s,30). The model assumes that the amplification is directly proportional to the ground shaking (linear behavior). The platform offers the ability to use two different national geological maps for this calculation (Forte et al., 2019, and Mori et al., 2020), making it possible to assess how different interpretations of subsoil data influence the final seismic damage.
• SS (NTC18) – based on the Forte map: This stratigraphic coefficient is calculated by using the Italian Building Code NTC18 formulation and takes into account soil non-linearity through the V_s,eq parameter, whose values were determined from the Forte et al. (2019) map.

Instead, for the vulnerability component, the possible alternative choices deal with the fragility model adopted to assess the probability of reaching/exceeding a certain damage level by a building (i.e. the propension of the building to be damaged given a certain intensity of the seismic input).  Three alternatives may be selected by the user, that reflect also different levels of detail achieved in the knowledge of the features of the building stock under examination, expressed by more or less refined data informing the taxonomy. The taxonomy collects a list of attributed useful to classify the seismic behaviour of buildings. One of the simplest, yet frequently used approaches for assessing large-scale risk, is based on the combination of three main parameters: construction age, material type, and number of stories. All these data are available from CENSUS ISTAT data. The construction age is usually adopted as a proxy to associate the material type; for the Sanremo case, this information was also available thanks to the detailed in-situ survey carried out also equipped through a thermo-camera. 

Then, the available options that the user may test include:

• National Heuristic model: This alternative relies on the heuristic-macroseismic approach proposed by Lagomarsino et al. 2021. It uses statistical analysis of damage observed in tens of thousands of buildings from major Italian earthquakes, particularly the data from the Da.D.O. platform (reference). The model is “heuristic” as it relies on assumptions from expert judgment, and “macroseismic” because it uses the framework of the European Macroseismic Scale (EMS-98) (Gruntal et al. 1998) to link damage levels to seismic intensity. By calibrating key parameters like the Vulnerability Index (V) and Ductility Index (Q), it provides a robust, data-driven vulnerability estimate that is valid on average for the entire country. The model is available for both Unreinforced Masonry (URM) and Reinforced Concrete (RC) buildings. As for the taxonomy, this model refers to the one based on ISTAT data.
• National DBV-Masonry model: This is a mechanical-analytical approach based on the principles of seismic engineering and Displacement-Based Vulnerability (DBV). The model is currently available only for URM buildings (DBV-Masonry in Lagomarsino and Cattari 2014 and Giusto et al. 2025).  The model simulates the physical behavior of masonry buildings to generate a “capacity curve” representing the structure’s in-plane seismic response while corrective factors allow also for accounting for the possible activation of out-of-plane mechanisms. The model is informed by mechanical and geometrical parameters, related to the Force Resisting Mechanism Material (FRMM), such as Unreinforced Masonry (URM) of Soft Stone (SS), Hard Stone (HS), Rubble (RU), or Regular Cut (RC) masonry; the Details and Maintenance (DM) level (High quality-HQD or Low quality-LQD), where details substantially refer to the presence or not of tie-rods and RC beams; the Floor System (FS) type (Rigid-R, Flexible-F, or Vaults-V); and the Height Level (HL) (number of level, 1, 2, 3, ….). According to the National – format the fragility curves are associated to the taxonomy based on ISTAT data, analogously to the National Heuristic model. In this case, the combination of parameters related to FRMM, DM and FS is defined upstream to the process to describe the URM building stock along the overall country (i.e. average values are adopted).
• Sector-specific DBV-Masonry model (refined): Starting from the same mechanical principles as the national model, this version is calibrated and customized using detailed data collected during CARTIS approach (Zuccaro et al., 2015) conducted in Sanremo. The city is divided into “sectors”—areas with similar construction characteristics—and for each one, a tailor-made fragility curve is defined that accounts for these prevalent local specificities in terms of FRMM, DM and FS. The combination of these parameters reflects those in Sanremo.

Once these input parameters on hazard and vulnerability are set, the platform processes the data and returns an urban-scale seismic damage scenario. The key information is provided for each census section, the smallest geographical unit for which ISTAT collects demographic and buildings data.

For each of these areas, the platform calculates the mean damage index (μ_D), a synthetic numerical index that represents the overall physical impact as a weighted average of all possible damage states (from none to collapse) as evaluated from the fragility curves for a given intensity hazard input. 

This μ_D value is then used to classify the severity of the impact into six distinct Damage Levels (DL), where DL0 corresponds to a μ_D value between 0 and 0.7, DL1 to a value between 0.7 and 1.6, DL2 to a value between 1.6 and 2.5, DL3 to a value between 2.5 and 3.4, DL4 to a value between 3.4 and 4.3, up to DL5 for a μ_D between 4.3 and 5. This classification forms the basis of the final output: a thematic map of the city that uses a color scale to visualize these damage levels, allowing for the rapid identification of the most critical areas. 

It is important to note that the options associated to the possible refinement of the fragility model are currently available for the masonry buildings. Furthermore, to ensure compliance with potentially sensitive data, results are only displayed for census tracks containing at least 15 masonry buildings—a threshold not met by any section for reinforced concrete structures.

REFERENCES

1. Cattari, S., Ottonelli, D., & Mohammadi, S. (2024). EQ-DIRECTION procedure towards an improved urban seismic resilience: Application to the pilot case study of Sanremo Municipality. Sustainability, 16(6), 2501
2. Dolce, M., Speranza, E., Giordano, F., Borzi, B., Bocchi, F., Conte, C., … & Pascale, V. (2019). Observed damage database of past Italian earthquakes: the Da. DO WebGIS. Bollettino di Geofisica Teorica ed Applicata, 60(2).
3. Forte, G., Chioccarelli, E., De Falco, M., Cito, P., Santo, A., & Iervolino, I. (2019). Seismic soil classification of Italy based on surface geology and shear-wave velocity measurements. Soil Dynamics and Earthquake Engineering, 122, 79-93
4. Giusto, S., Boem, I., Alfano, S., Gattesco, N., & Cattari, S. (2025). Derivation of seismic fragility curves through mechanical-analytical approaches: the case study of the URM school buildings in Friuli-Venezia Giulia region (Italy). Bulletin of Earthquake Engineering, 1-36.
5. Grunthal, G. (Ed.). (1998). European Macroseismic Scale 1998 (EMS-98). Cahiers du Centre Européen de Géodynamique et de Séismologie, Vol. 15, Luxembourg
6. Lagomarsino, S., & Cattari, S. (2013). Seismic vulnerability of existing buildings: Observational and mechanical approaches for application in urban areas. Seismic vulnerability of structures, 1-62.
7. Lagomarsino, S., Cattari, S., & Ottonelli, D. (2021). The heuristic vulnerability model: fragility curves for masonry buildings. Bulletin of Earthquake Engineering, 19, 3129-3163.
8. Merani, M. G. B., Sivori, D., Lagomarsino, S., Barani, S., & Cattari, S. (2026). Sensitivity of urban seismic damage predictions to input data detail: a multilevel approach in Sanremo, Italy. Bulletin of Earthquake Engineering. – SUBMITTED
9. Mori, F., Mendicelli, A., Moscatelli, M., Romagnoli, G., Peronace, E., & Naso, G. (2020). A new Vs30 map for Italy based on the seismic microzonation dataset. Engineering Geology, 275, 105745
10. Zuccaro, G., Dolce, M., De Gregorio, D., Speranza, E., & Moroni, C. (2015). La scheda CARTIS per la caratterizzazione tipologico-strutturale dei comparti urbani costituiti da edifici ordinari. Valutazione dell’esposizione in analisi di rischio sismico. Proceedings of the GNGTS.