Advertisement

Landslides

, Volume 17, Issue 1, pp 231–240 | Cite as

Sinusoidal wave fit indexing of irreversible displacements for crackmeters monitoring of rockfall areas: test at Pietra di Bismantova (Northern Apennines, Italy)

  • Marco MulasEmail author
  • Martin Marnas
  • Giuseppe Ciccarese
  • Alessandro Corsini
Technical Note
  • 91 Downloads

Abstract

Temperature changes affect crackmeters monitoring on a daily and a seasonal basis. This is due to rock mass thermal dilatancy and to instrumental matters. The consequent widening closing cycles can mask small irreversible displacements that might be precursors of rock failures. Recently, Weber et al. (Cryosphere 11:567–583, 2017) have proposed a linear fit method between temperature and fracture opening in order to compute the irreversibility index as a metrics to rank irreversible displacements. However, such an approach requires temperature sensors coupled to crackmeters. In order to overcome these limits, we propose an alternative method for deriving a normalised Z-score irreversibility index. It is based on sinusoidal wave fit of cracks opening time series only; thus, it does not require temperature monitoring. The methodology has been tested using data recorded by a wireless sensor network installed at La Pietra di Bismantova rock slab composed of 14 crackmeters and thermometers monitoring potentially unstable rock masses. A comparison of results obtained using the method of Weber et al. (Cryosphere 11:567–583, 2017) and the sinusoidal approach shows that the latter is much less sensitive to the duration of the moving window used to derive the irreversibility index, making it a much more flexible tool for indexing irreversible displacements over short time periods. Moreover, as rapid high–magnitude temperature changes can also be the causal factor of irreversible displacements, their statistical relation with peaks of the Z-score irreversibility index has been investigated. Results have shown that, depending on which crack is examined, correlations between irreversibility peaks and antecedent extreme temperature variations are more or less relevant. In conclusion, we believe that the Z-score sinusoidal wave fit irreversibility index (ZSFI) can represent a useful metrics for indexing irreversible displacements in unstable blocks using crackmeters’ datasets affected by temperature cycles at the daily and seasonal scale.

Graphical Abstract

Keywords

Rockfall Crackmeters Irreversible displacements index Sinusoidal wave fit Northern Apennines 

References

  1. Allen S, Huggel C (2013) Extremely warm temperatures as a potential cause of recent high mountain rockfall. Glob Planet Chang 107:59–69.  https://doi.org/10.1016/J.GLOPLACHA.2013.04.007 CrossRefGoogle Scholar
  2. Arosio D, Corsini A, Giusti R, Zanzi L (2017) Seismic noise measurements on unstable rock blocks: the case of Bismantova rock cliff. In: Mikoš M, Arbanas Ž, Yin Y, Sassa K (eds) Advancing culture of living with landslides. Springer International Publishing, Cham, pp 325–332CrossRefGoogle Scholar
  3. Arosio D, Longoni L, Mazza F, Papini M, Zanzi L (2013) Freeze-thaw cycle and Rockfall monitoring. In: Margottini C, Canuti P, Sassa K (eds) Landslide science and practice: volume 2: early warning, instrumentation and monitoring. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 385–390.  https://doi.org/10.1007/978-3-642-31445-2_50 CrossRefGoogle Scholar
  4. Begueria S (2006) Validation and evaluation of predictive models in hazard assessment and risk management. Nat Hazards 37:315–329CrossRefGoogle Scholar
  5. Borgatti L, Tosatti G (2010) Slope instability processes affecting the Pietra Di Bismantova Geosite (Northern Apennines, Italy). Geoheritage 2:155–168.  https://doi.org/10.1007/s12371-010-0023-8 CrossRefGoogle Scholar
  6. Bottelin P, Lévy C, Baillet L, Jongmans D, Guéguen P (2013) Modal and thermal analysis of les arches unstable rock column (vercors massif, french alps). Geophys J Int 194:849–858.  https://doi.org/10.1093/gji/ggt046 CrossRefGoogle Scholar
  7. Collins BD, Stock GM (2016) Rockfall triggering by cyclic thermal stressing of exfoliation fractures. Nat Geosci 9:395–400CrossRefGoogle Scholar
  8. Conti S, Tosatti G (1994) Caratteristiche geologico-strutturali della Pietra di Bismantova e fenomeni franosi connessi. Quad Geol Appl 1:25–43Google Scholar
  9. Corsini A, Bonacini F, Deiana M, Giusti R, Russo M, Ronchetti F, Cantini C, Truffelli G, Iasio C, Generali M, Ascari L, Chiesi L, Venturi L (2016) A wireless crackmeters network for the analysis of rockfalls at the Pietra di Bismantova natural heritage site (Northern Apennines, Italy). In: Aversa S, Cascini L, Picarelli L, Scavia C (eds) Landslides and engineered slopes. Experience, theory and practice. CRC Press, London, pp 685–690Google Scholar
  10. Corsini A, Mulas M (2016) Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy). Landslides 14:1241–1252.  https://doi.org/10.1007/s10346-016-0781-8 CrossRefGoogle Scholar
  11. D’Amato J, Hantz D, Guerin A, Jaboyedoff M, Baillet L, Mariscal A (2016) Influence of meteorological factors on rockfall occurrence in~a~middle~mountain limestone cliff. Nat Hazards Earth Syst Sci 16:719–735.  https://doi.org/10.5194/nhess-16-719-2016 CrossRefGoogle Scholar
  12. Davies MCR, Hamza O, Harris C (2001) The effect of rise in mean annual temperature on the stability of rock slopes containing ice-filled discontinuities. Permafr Periglac Process 12:137–144.  https://doi.org/10.1002/ppp.378 CrossRefGoogle Scholar
  13. Deiana M, Mussi M, Ronchetti F (2017) Discharge and environmental isotope behaviours of adjacent fractured and porous aquifers. Environ Earth Sci 76:595.  https://doi.org/10.1007/s12665-017-6897-x CrossRefGoogle Scholar
  14. Delonca A, Gunzburger Y, Verdel T (2014) Statistical correlation between meteorological and rockfall databases. Nat Hazards Earth Syst Sci 14:1953–1964.  https://doi.org/10.5194/nhess-14-1953-2014 CrossRefGoogle Scholar
  15. Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111:62–72CrossRefGoogle Scholar
  16. Frayssines M, Hantz D (2006) Failure mechanisms and triggering factors in calcareous cliffs of the subalpine ranges (French Alps). Eng Geol 86:256–270.  https://doi.org/10.1016/J.ENGGEO.2006.05.009 CrossRefGoogle Scholar
  17. Gorsevski PV, Gessler PE, Foltz RB, Elliot WJ (2006) Spatial prediction of landslide hazard using logistic regression and ROC analysis. Trans GIS 10:395–415.  https://doi.org/10.1111/j.1467-9671.2006.01004.x CrossRefGoogle Scholar
  18. Gunzburger Y, Merrien-Soukatchoff V, Guglielmi Y (2005) Influence of daily surface temperature fluctuations on rock slope stability: case study of the Rochers de Valabres slope (France). Int J Rock Mech Min Sci 42:331–349.  https://doi.org/10.1016/j.ijrmms.2004.11.003 CrossRefGoogle Scholar
  19. Grøneng G, Christiansen HH, Nilsen B, Blikra LH (2011) Meteorological effects on seasonal displacements of the Åknes rockslide, western Norway. Landslides 8:1–15.  https://doi.org/10.1007/s10346-010-0224-x CrossRefGoogle Scholar
  20. IACBR (2015) Extract of the report of the 21st meeting of the International Advisory Committee for Biosphere Reserves, 2–5 February 2015. UNESCO Headquarters, Paris http://www.mabappennino.it/pdf/Esito-valutazione-IACBR-13-Marzo-2015.pdf Google Scholar
  21. Janeras M, Jara JA, Royán MJ, Vilaplana JM, Aguasca A, Fàbregas X, Gili JA, Buxó P (2017) Multi-technique approach to rockfall monitoring in the Montserrat massif (Catalonia, NE Spain). Eng Geol 219:4–20.  https://doi.org/10.1016/j.enggeo.2016.12.010 CrossRefGoogle Scholar
  22. Lévy C, Baillet L, Jongmans D, Mourot P, Hantz D (2010) Dynamic response of the Chamousset rock column (Western Alps, France). J Geophys Res Earth Surf 115:1–13.  https://doi.org/10.1029/2009JF001606 CrossRefGoogle Scholar
  23. Matsuoka N, Sakai H (1999) Rockfall activity from an alpine cliff during thawing periods. Geomorphology 28:309–328.  https://doi.org/10.1016/S0169-555X(98)00116-0 CrossRefGoogle Scholar
  24. Nordvik T, Blikra LH, Nyrnes E, Derron MH (2010) Statistical analysis of seasonal displacements at the Nordnes rockslide, northern Norway. Eng Geol 114:228–237.  https://doi.org/10.1016/j.enggeo.2010.04.019 CrossRefGoogle Scholar
  25. R Development Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna ISBN 3–900051–07-0, URL http://www.R-project.org Google Scholar
  26. Ravanel L, Deline P (2011) Climate influence on rockfalls in high-Alpine steep rockwalls: the north side of the Aiguilles de Chamonix (Mont Blanc massif) since the end of the ‘little ice age’. The Holocene 21:357–365.  https://doi.org/10.1177/0959683610374887 CrossRefGoogle Scholar
  27. Sandersen F, Bakkehøi S, Hestnes E, Lied K (1996) The influence of meteorological factors on the initiation of debris flows, rockfalls, rockslides and rockmass stability. In: Senneset K (ed) Landslides, volume 3: Proceedings of the 7th international symposium on landslides Trondheim, 3rd edn. A.A. Balkema, Norway, pp 97–114Google Scholar
  28. Swets J (1988) Measuring the accuracy of diagnostic systems. Science (80) 240:1285–1293.  https://doi.org/10.1126/science.3287615 CrossRefGoogle Scholar
  29. Weber S, Beutel J, Faillettaz J, Hasler A, Krautblatter M, Vieli A (2017) Quantifying irreversible movement in steep, fractured bedrock permafrost on Matterhorn (CH). Cryosph. 11:567–583.  https://doi.org/10.5194/tc-11-567-2017 CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Chemical and Geological SciencesUniversity of Modena and Reggio EmiliaModenaItaly
  2. 2.Ecole des Mines de NancyUniversité de LorraineNancy cedexFrance

Personalised recommendations