A technological system capable of automatically producing damage scenarios at an urban scale, as soon as an earthquake occurs, can help the decision‐makers in planning the first post‐disaster response, i.e., to prioritize the field activities for checking damage, making a building safe, and supporting rescue and recovery. This system can be even more useful when it works on densely populated areas, as well as on historic urban centers. In the paper, we propose a processing chain on a GIS platform to generate post‐earthquake damage scenarios, which are based: (1) on the near real‐time processing of the ground motion, that is recorded in different sites by MEMS accel-erometric sensor network in order to take into account the local effects, and (2) the current structural characteristics of the built heritage, that can be managed through an information system from the local public administration authority. In the framework of the EU‐funded H2020‐ARCH pro-ject, the components of the system have been developed for the historic area of Camerino (Italy). Currently, some experimental fragility curves in the scientific literature, which are based on the damage observations after Italian earthquakes, are implemented in the platform. These curves allow relating the acceleration peaks obtained by the recordings of the ground motion with the probability to reach a certain damage level, depending on the structural typology. An operational test of the system was performed with reference to an ML3.3 earthquake that occurred 13 km south of Camerino. Acceleration peaks between 1.3 and 4.5 cm/s2 were recorded by the network, and probabilities lower than 35% for negligible damage (and then about 10% for moderate damage) were calculated for the historical buildings given this low‐energy earthquake.
CITATION STYLE
Costanzo, A., Falcone, S., D’alessandro, A., Vitale, G., Giovinazzi, S., Morici, M., … Buongiorno, M. F. (2021). A technological system for post‐earthquake damage scenarios based on the monitoring by means of an urban seismic network. Sensors, 21(23). https://doi.org/10.3390/s21237887
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