Purpose: The san Nicola landslide is one of the historical moving geosite landslide (sensu Calcaterra et al., 2014), affecting the UNESCO Cilento Geopark area (Campania region, southern Italy). The latest paroxysmal reactivation dates back to October 1963 after heavy rainfalls, but several precursory phenomena happened during the two years before (Guida et al., 1981). Such reactivation was the consequence of a lateral spread of sandy conglomerates over structurally-complex clayey flysh deposits, which turn out into rotational slides and earthflows. The latter destroyed portions of the old San Nicola village which was later relocated according to Central Government decisions. The occurred landslide system (sensu Valiante et al., 2021) is still evolving in a deep lateral spread and shallow slide-flows threatening the main access road and the new San Nicola village. In order to gather information useful for decision-making and countermeasure design, monitoring activities of the landslides system have been implemented since early 2021 by using conventional and innovative sensors. Methods: The former includes 5 piezometers and 3 inclinometers, whereas the latter have been coupled with New Smart Hybrid Transducers (NSHT) (Minutolo et al., 2020). NSHT is a strain transducer based on optical fiber sensing technology suitable for real-time monitoring over long distances and, as such, it has been exploited as a smart inclinometer. Furthermore, environmental factors are monitored through a series of sensors such as a weather station and electrical piezometers, which are linked together in a digital system of data ingestion having basic pre-processing functions also capable to generate alarms if threshold values of relevant parameters are exceeded. Processed data are then sent to a cloud-based data storage system and a real-time web-based dashboard. Results: After more than a year of monitoring activities, data density allowed for an in-depht characterization of the landslide system such as, for example, the aquifer response to precipitations or the recognition of buried sliding surfaces with different dephts. Conclusions: The results up to now obtained show the reliability of the exploited technologies given their capability of continuous measurements and real-time data handling.
The San Nicola landslide experimental field
Emilia Damiano;Martina de Cristofaro;Lucio Olivares;
2023
Abstract
Purpose: The san Nicola landslide is one of the historical moving geosite landslide (sensu Calcaterra et al., 2014), affecting the UNESCO Cilento Geopark area (Campania region, southern Italy). The latest paroxysmal reactivation dates back to October 1963 after heavy rainfalls, but several precursory phenomena happened during the two years before (Guida et al., 1981). Such reactivation was the consequence of a lateral spread of sandy conglomerates over structurally-complex clayey flysh deposits, which turn out into rotational slides and earthflows. The latter destroyed portions of the old San Nicola village which was later relocated according to Central Government decisions. The occurred landslide system (sensu Valiante et al., 2021) is still evolving in a deep lateral spread and shallow slide-flows threatening the main access road and the new San Nicola village. In order to gather information useful for decision-making and countermeasure design, monitoring activities of the landslides system have been implemented since early 2021 by using conventional and innovative sensors. Methods: The former includes 5 piezometers and 3 inclinometers, whereas the latter have been coupled with New Smart Hybrid Transducers (NSHT) (Minutolo et al., 2020). NSHT is a strain transducer based on optical fiber sensing technology suitable for real-time monitoring over long distances and, as such, it has been exploited as a smart inclinometer. Furthermore, environmental factors are monitored through a series of sensors such as a weather station and electrical piezometers, which are linked together in a digital system of data ingestion having basic pre-processing functions also capable to generate alarms if threshold values of relevant parameters are exceeded. Processed data are then sent to a cloud-based data storage system and a real-time web-based dashboard. Results: After more than a year of monitoring activities, data density allowed for an in-depht characterization of the landslide system such as, for example, the aquifer response to precipitations or the recognition of buried sliding surfaces with different dephts. Conclusions: The results up to now obtained show the reliability of the exploited technologies given their capability of continuous measurements and real-time data handling.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
