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Formulation of landslide risk scenarios using underground monitoring data and numerical models: conceptual approach, analysis, and evolution of a case study in Southern Italy

  • A. SegaliniEmail author
  • A. Carri
  • C. Grignaffini
  • G. Capparelli
Technical Note


Understanding the mechanism of a landslide and its evolution is of fundamental importance in the risk management process. This work introduces an articulated approach to the problem, applying it to a specific case in the south of Italy where a gravitational movement insists on a section of an important highway. In recent years, the site has been investigated from a geomorphological and a lithological point of view, and a comprehensive geomechanical characterization has been carried out by means of on-site and laboratory tests. The area has been instrumented with a monitoring system composed of automatic inclinometers, piezometers, a rainfall station, and time domain reflectometry (TDR) cables. These sensors have monitored the deformation processes and their correlation with groundwater fluctuation. A 2D finite differences model (FDM) of the slope has been created, calibrated, and validated through back analysis, carried out using the monitoring data available. A secondary creep phenomenon, barely influenced by the water level rise due to occasional rainfall, has been identified and modeled using the Burgers viscoelastic constitutive model. Variations in the piezometric level were introduced and their effect accounted for the numerical model refinement. Once the improvements had been completed together with the reproduction of past events, a predictive analysis was carried out in order to forecast the most probable slope behavior relative to the incoming year. At the end of this phase, the infrastructure supervisor should have information about possible deformations to be compared with the near real-time monitoring outcomes and design assumptions. This procedure allows real-time monitoring of the compatibility of slope deformations with highway safety.


Landslide Monitoring Numerical modeling Real time EWS Creep 


Funding information

This research was funded by the Italian Ministry of Education, University and Research (MIUR), PON Project No. 01_01503 “Integrated Systems for Hydrogeological Risk Monitoring, Early Warning and Mitigation Along the Main Lifelines”, CUP: B31H11000370005.


  1. Arbanas Z, Sassa K, Nagai O, Jagodnik V, Prodan M V, Jovančević S D, Peranić J, Ljutić K (2014) A landslide monitoring and early warning system using integration of GPS, TPS and conventional geotechnical monitoring methods. Landslide science for a safer. Geoenvironment. 2. Doi:
  2. Artese G, Perrelli M, Artese S, Meduri S, Brogno N (2015) POIS, a low cost tilt and position sensor: design and first tests. Sensors 15:10806–10824. CrossRefGoogle Scholar
  3. Bicocchi G, D’Ambrosio M, Rossi G, Rosi A, Tacconi-Stefanelli C, Segoni S, Nocentini M, Vannocci P, Tofani V, Casagli N, Catani F (2016) Geotechnical in situ measures to improve landslides forecasting models: a case study in Tuscany (Central Italy). In: Aversa et al. (Eds) Landslides and Engineered Slopes. Experience, Theory and Practice Proceedings of the 12th International Symposium on Landslides (Napoli, Italy, 12-19 June 2016) pp.419–424Google Scholar
  4. Carri A, Grignaffini C, Segalini A, Capparelli G, Versace P, Spolverino G (2017) Study of an active landslide on A16 Highway (Italy): modeling, monitoring and triggering alarm. M. Mikoš et al. (eds.) Advancing Culture of Living with Landslides. Doi
  5. Ciarcia S, Vitale S (2013) Sedimentology, stratigraphy and tectonics of evolving wedge-top depozone: Ariano Basin, southern Apennines, Italy. Sediment Geol 290:27–46Google Scholar
  6. Colleselli F, Colosimo P (1977) Comportamento di argille plio-pleistoceniche in una falesia del litorale adriatico. Rivista Italiana di Geotecnica 5:5–21Google Scholar
  7. Crostella A, Vezzani L (1964) La geologia dell’Appennino Foggiano. Boll Soc Geol Ital 83:121–141Google Scholar
  8. Dazzaro L, Rapisardi L (1984) Nuovi dati stratigrafici, tettonici e paleogeografici della parte settentrionale dell’Appennino Dauno. Boll Soc Geol Ital 103(01):51–58Google Scholar
  9. FLAC Version 8.0 Creep Material Models (2016)Google Scholar
  10. FLAC. FLAC® Version 8.0. URL: [Last accessed: 11 January 2018]
  11. Intrieri E, Bardi F, Fanti R, Gigli G, Fidolini F, Casagli N, Costanzo S, Raffo A, Di Massa G, Capparelli G, Versace P (2017) Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application. Natural Hazards Earth Syst Sci 17:1713–1723. CrossRefGoogle Scholar
  12. Ippolito F, Ortolani F, Russo M (1973) Struttura marginale dell’Appennino Campano: reinterpretazione di dati di antiche ricerche di idrocarburi. Mem Soc Geol Ital 12:227–250Google Scholar
  13. Krkač M, Arbanas S M, Arbanas Ž, Bernat S, Špehar K, Watanabe N, Nagai O, Sassa K, Marui H, Furuya G, Wang C, Rubinić J, Matsunami K (2014) Review of monitoring parameters of the kostanjek landslide (Zagreb, Croatia). In: Sassa K, Canuti P, Yin Y (Eds) Landslide Science for a Safer Geoenvironment. Springer, Cham. pp. 637–643Google Scholar
  14. Lin SS, Lo CM, Lin YC (2017) Investigating the deformation and failure characteristics of argillite consequent slope using discrete element method and Burgers model. Environ Earth Sci.
  15. Rocscience. All RocData Resources URL: [Last accessed: 5th January 2018]
  16. Rossi G, Catani F, Leoni L, Segoni S, Tofani V (2013) HIRESSS: a physically based slope stability simulator for HPC applications. Nat Hazards Earth Syst Sci 13:151–166CrossRefGoogle Scholar
  17. Scandone P (1967) Studi di geologia lucana: la serie calcarea-silico-marnosa e i suoi rapporti con l’Appennino calcareo. Boll Soc Nat Napoli 76(2):301–469Google Scholar
  18. Segalini A. (2001) Numerical monitoring of time dependant - slow moving – landslides in colluvium. Flac and Numerical Modelling in Geomechanics. Proceedings of the II International FLAC Symposium. Lyon, France. Balkema, NL. pp. 171–178Google Scholar
  19. Segalini A, Carini C (2013) Underground landslide displacement monitoring: a new mmes based device. In: Margottini C, Canuti P, Sassa K (Eds) Landslide Science and Practice. Springer, Berlin, Heidelberg.
  20. Segalini A, Giani GP, Ferrero AM (2009) Geomechanical studies on slow slope movements in Parma Apennine. Eng Geol 109(1–2):31–44CrossRefGoogle Scholar
  21. Singh A, Mitchell JK (1968) General stress-strain-time function for soils. J Soil Mech Found Div 94(1):21–46Google Scholar
  22. Versace P, Artese G, Autiero M, Avolio M V, Bardi F, Borgia A, Cancelliere A, Capparelli G, Capuozzo M, Caruso A, Casagli N, Cavallaro L, Cianciosi O, Conforti M, Conte E, Costanzo A, Costanzo S, De Marinis M, Di Gregorio S, Di Massa G, De Luca D L, De Santis D, Donato A, Fanti R, Fidolini F, Formetta G, Foti E, Intrieri E, La Sala G, Luci A, Maletta D, Mannara G, Moreno D, Morrone L, Mungari T, Muto F, Paoletti F, Peres D J, Raffo A, Rago V, Rigon R, Spadafora F, Spataro W, Troncone A, Trunfio G A, Vena M, Viggiani G (2014) An integrated system for landslide monitoring, early warning and risk mitigation along lifelines. PON01_01503, Cosenza 25th–28th of November 2014Google Scholar
  23. Vyalov SS (1986) Rheological fundamentals of soil mechanics. Elsevier, AmsterdamGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.DIA – Department of Engineering and ArchitectureUniversity of ParmaParmaItaly
  2. 2.DIMESUniversity of CalabriaRendeItaly

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