Abstract
Landslide dam failure may be triggered by heavy rainfall or earthquake and may fail due to seepage or pi** 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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alden, W.C.: Landslide and flood at Gros Ventre, Wyoming. In: Tank, R. (ed.) Focus on Environmental Geology (1928)
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
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)
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)
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
Eberl, C.: MATLAB Central File Exchange. Digital Image Correlation and Tracking. https://www.mathworks.com/matlabcentral/fileexchange/12413-digital-image-correlation-and-tracking (2021)
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
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
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
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
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
Glazyrin, G.Y., Reyzvikh, V.N.: Computation of the flow hydrograph for the breach of landslide lakes. Soviet Hydrol. 5, 492–496 (1968)
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
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
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
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
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
Lee, K.L., Duncan, J.M.: Landslide of April 25, 1974 on the Mantaro River, Peru. National Academy of Sciences, Washington, DC (1975)
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
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
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)
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
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
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
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
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
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
Schuster, R.L.: Interaction of dams and landslides: case studies and mitigation. US Geol. Surv. (2006). https://doi.org/10.3133/PP1723
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
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
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
Vosselman, G., Maas, H.G.: Airborne and Terrestrial Laser Scanning. Taylor and Francis Group, Boca Raton, FL, USA (2010)
Fu-gang, X., Yang, **ng-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
Doa, X.K., Kimb, M., Nguyenc, T., Jungd, K.: Analysis of landslide dam failure caused by overtop**. Procedia Eng. 154, 990–994 (2016). https://doi.org/10.1016/j.proeng.2016.07.587
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
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)