Landslide Dam Failure Analysis Using Imaging and Ranging Sensors | SpringerLink
Skip to main content
Springer Nature Link
Log in

Landslide Dam Failure Analysis Using Imaging and Ranging Sensors

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12955))

Included in the following conference series:

Abstract

Landslide dam failure may be triggered by heavy rainfall or earthquake and may fail due to seepage or piping because of the asymmetric compaction. Hence, they have the potential to result in serious natural hazards. Rapid assessment of this phenomenon requires the application of investigation and monitoring techniques providing information on the ongoing failure process. To this aim, a downscaled model of a natural dam landslide was reconstructed in a simulation facility (the ‘Landslide Simulator’) located in the Lecco Campus of Politecnico di Milano university, Italy. The failure of the dam was induced by artificial rainfall. A sensor network was setup to record observations during the simulation experiment, including geotechnical, geophysical, and imaging/ranging sensors. This paper focuses on the analysis of deformation measurement and other changes over time, which were observed in the recorded image sequences and 3D point clouds to analyze and predict the failure of the dam. Results showed that water seepage may play a dominant role in the dam failure process, which is anticipated by a sharp increase of strain in the dam body. Furthermore, image processing techniques may help scientists to calibrate numerical models to improve their quality and reliability.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 13727
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 17159
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Alden, W.C.: Landslide and flood at Gros Ventre, Wyoming. In: Tank, R. (ed.) Focus on Environmental Geology (1928)

    Google Scholar 

  2. Hojat, A., et al.: Quantifying seasonal 3D effects for a permanent electrical resistivity tomography monitoring system along the embankment of an irrigation canal. Near Surf. Geophys. 18, 427–443 (2020). https://doi.org/10.1002/nsg.12110

    Article  Google Scholar 

  3. Besl, P.J., McKay, N.D.: Method for registration of 3-d shapes. In: Sensor Fusion IV: Control Paradigms and Data Structures, pp. 586–560. International Society for Optics and Photonics. https://doi.org/10.1109/34.121791 (1992)

  4. Costa, J.E., Schuster, R.L.: The formation and failure of natural dams. USGS Open-File, Report 87-392, https://doi.org/10.3133/ofr87392 (1987)

  5. DiFrancesco, Paul-Mark., Bonneau, D., Hutchinson, D.J.: The implications of M3C2 projection diameter on 3D semi-automated Rockfall extraction from sequential terrestrial laser scanning point clouds. Remote Sens. 12(11), 1885 (2020). https://doi.org/10.3390/rs12111885

    Article  Google Scholar 

  6. Eberl, C.: MATLAB Central File Exchange. Digital Image Correlation and Tracking. https://www.mathworks.com/matlabcentral/fileexchange/12413-digital-image-correlation-and-tracking (2021)

  7. Ermini, L., Casagli, N.: Prediction of the behaviour of landslide dams using a geomorphological dimensionless index. Earth Surf. Proc. Land. 28, 31–47 (2003). https://doi.org/10.1002/esp.424

    Article  Google Scholar 

  8. Fedele, R., Scaioni, M., Barazzetti, L., Rosati, G., Biolzi, L., Condoleo, P.: Delamination tests on CFRP-reinforced masonry pillars: optical monitoring and mechanical modelling. Cement Concr. Compos. 45, 243–254 (2014). https://doi.org/10.1016/j.cemconcomp.2013.10.006

    Article  Google Scholar 

  9. Feng, T., et al.: Measurement of surface changes in a scaled-down landslide model using high-speed stereo image sequences. Photogrammetr. Eng. Remote Sens. 82(7), 547–557 (2016). https://doi.org/10.14358/PERS.82.7.547

    Article  Google Scholar 

  10. Fey, C., Wichmann, V.: Long-range terrestrial laser scanning for geomorphological change detection in alpine terrain – handling uncertainties. Earth Surf. Proc. Land. 42, 789–802 (2017). https://doi.org/10.1002/esp.4022

    Article  Google Scholar 

  11. Giordan, D., Manconi, A., Remondino, F., Nex, F.: Use of unmanned aerial vehicles in monitoring application and management of natural hazards. Geomat. Nat. Haz. Risk 8, 1–4 (2017). https://doi.org/10.1080/19475705.2017.1315619

    Article  Google Scholar 

  12. Glazyrin, G.Y., Reyzvikh, V.N.: Computation of the flow hydrograph for the breach of landslide lakes. Soviet Hydrol. 5, 492–496 (1968)

    Google Scholar 

  13. Gonzalez-Aguilera, D., Gomez-Lahoz, J., Sanchez, J.: A new approach for structural monitoring of large dams with a three-dimensional laser scanner. Sensors 8, 5866–5883 (2008). https://doi.org/10.3390/s8095866

    Article  Google Scholar 

  14. Hänsel, P., Schindewolf, M., Eltner, A., Kaiser, A., Schmidt, J.: Feasibility of high-resolution soil erosion measurements by means of rainfall simulations and SfM photogrammetry. Hydrology 3(4), 38 (2016). https://doi.org/10.3390/hydrology3040038

    Article  Google Scholar 

  15. Huang, Q., Luzi, G., Monserrat, O., Crosetto, M.: Ground-based synthetic aperture radar interferometry for deformation monitoring: a case study at Geheyan Dam, China. J. Appl. Remote Sens. 11(3), 1 (2017). https://doi.org/10.1117/1.JRS.11.036030

    Article  Google Scholar 

  16. Hungr, O., Leroueil, S., Picarelli, L.: The Varnes classification of landslide types, an update. J. Appl. Remote Sens. 11, 167–194 (2014). https://doi.org/10.1117/1.JRS.11.036030

    Article  Google Scholar 

  17. Lague, D., Brodu, N., Leroux, J.: Accurate 3D comparison of complex topography with terrestrial laser scanner: application to the Rangitikei canyon (N-Z). ISPRS J. Photogramm. Remote. Sens. 82, 10–26 (2013). https://doi.org/10.1016/j.isprsjprs.2013.04.009

    Article  Google Scholar 

  18. Lee, K.L., Duncan, J.M.: Landslide of April 25, 1974 on the Mantaro River, Peru. National Academy of Sciences, Washington, DC (1975)

    Google Scholar 

  19. Lindenbergh, R., Pietrzyk, P.: Change detection and deformation analysis using static and mobile laser scanning. Appl. Geomatics 7(2), 65–74 (2015). https://doi.org/10.1007/s12518-014-0151-y

    Article  Google Scholar 

  20. Osmanoğlu, B., Sunar, F., Wdowinski, S., Cabral-Cano, E.: Time series analysis of InSAR data: methods and trends. ISPRS J. Photogramm. Remote Sens. 115, 90–102 (2016). https://doi.org/10.1016/j.isprsjprs.2015.10.003

    Article  Google Scholar 

  21. Awal, R., Nakagawa, H., Baba, Y., Sharma, R.H., Ito, N.: Study on landslide dam failure by sliding. Annuals of Disas. Prev. Res. Inst, Kyoto Univ., No. 50 B (2007)

    Google Scholar 

  22. Sattar, A., Konagai, K.: Recent Landslide Damming Events and Their Hazard Mitigation Strategies. In: Moustafa, A. (ed.) Advances in Geotechnical Earthquake Engineering – Soil Liquefaction and Seismic Safety of Dams and Monuments. InTech (2012). https://doi.org/10.5772/28044

    Chapter  Google Scholar 

  23. Scaioni, M., Crippa, J., Longoni, L., Papini, M., Zanzi, L.: Image-based reconstruction and analysis of dynamic scenes in a landslide simulation facility. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5/W1, 63–70 (2017). https://doi.org/10.5194/isprs-annals-IV-5-W1-63-2017

    Article  Google Scholar 

  24. Scaioni, M., et al.: Some applications of 2-D and 3-D photogrammetry during laboratory experiments for hydrogeological risk assessment. Geomat. Nat. Haz. Risk 6(5–7), 473–496 (2015). https://doi.org/10.1080/19475705.2014.885090

    Article  Google Scholar 

  25. Scaioni, M., Longoni, L., Zanzi, L., Ivanov, V., Papini, M.: Teaching geomatics for geohazard mitigation and management in the COVID-19 time. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-3/W1-2020, 131–138 (2020). https://doi.org/10.5194/isprs-archives-XLIV-3-W1-2020-131-2020

    Article  Google Scholar 

  26. Scaioni, M., et al.: Analysis of spatial sensor network observations during landslide simulation experiments. Eur. J. Environ. Civ. Eng. 17(9), 802–825 (2013). https://doi.org/10.1080/19648189.2013.822427

    Article  Google Scholar 

  27. Scaioni, M., Marsella, M., Crosetto, M., Tornatore, V., Wang, J.: Geodetic and remote-sensing sensors for dam deformation monitoring. Sensors 18(11), 3682 (2018). https://doi.org/10.3390/s18113682

    Article  Google Scholar 

  28. Schuster, R.L.: Interaction of dams and landslides: case studies and mitigation. US Geol. Surv. (2006). https://doi.org/10.3133/PP1723

    Article  Google Scholar 

  29. Schuster, R.L.: Risk-reduction measures for landslide dams. Ital. J. Eng. Geol. Environ. (2006). https://doi.org/10.4408/IJEGE.2006-01.S-01

    Article  Google Scholar 

  30. Snow, D.: Landslide of Cerro Condor-Sencca, Department of Ayacucho, Peru. In: Kiersch, G.A. (ed.) Engineering Geology Case Histories Number 5, pp. 1–6. Geological Society of America, New York (1964). https://doi.org/10.1130/Eng-Case-5.1

    Chapter  Google Scholar 

  31. Ivanov, I.V., et al.: Investigation on the role of water for the stability of shallow landslides—insights from experimental tests. Water 12, 1203 (2020). https://doi.org/10.3390/w12041203

    Article  Google Scholar 

  32. Vosselman, G., Maas, H.G.: Airborne and Terrestrial Laser Scanning. Taylor and Francis Group, Boca Raton, FL, USA (2010)

    Google Scholar 

  33. Fu-gang, X., Yang, Xing-guo, Zhou, Jia-wen, Hao, Ming-hui: Experimental research on the dam-break mechanisms of the Jiadanwan landslide dam triggered by the Wenchuan earthquake in China. Sci. World J. 2013, 1–13 (2013). https://doi.org/10.1155/2013/272363

    Article  Google Scholar 

  34. Doa, X.K., Kimb, M., Nguyenc, T., Jungd, K.: Analysis of landslide dam failure caused by overtopping. Procedia Eng. 154, 990–994 (2016). https://doi.org/10.1016/j.proeng.2016.07.587

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge companies that provided free trial-demo version of software packages VIC-2D® and GOM Correlate® to allow students to accomplish their experiments. They would like also to acknowledge Eberl [6] for the open-source Matlab® code for 2D DIC and the authors of CloudCompare open-source software. Eventually, acknowledgements go to the Lecco Campus of Politecnico di Milano and to Prof. Monica Papini for the availability of the ‘Landslide Simulator’.

Author information

Authors and Affiliations

  1. Politecnico di Milano, Lecco Campus, via G. Previati 1/c, 23900, Lecco, Italy

    Keivan Tavakoli, Ehsan Zadehali, Arsalan Malekian & Sara Darsi

  2. Department of Civil and Environmental Engineering, Politecnico di Milano, piazza L. da Vinci 32, 20133, Milano, Italy

    Laura Longoni

  3. Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, via Ponzio 31, 20133, Milano, Italy

    Marco Scaioni

Authors
  1. Keivan Tavakoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ehsan Zadehali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arsalan Malekian
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sara Darsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laura Longoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Marco Scaioni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Scaioni .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tavakoli, K., Zadehali, E., Malekian, A., Darsi, S., Longoni, L., Scaioni, M. (2021). Landslide Dam Failure Analysis Using Imaging and Ranging Sensors. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-87007-2_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87006-5

  • Online ISBN: 978-3-030-87007-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation