Skip to main content
SpringerLink
Log in

A new prediction method for the occurrence of landslides based on the time history of tilting of the slope surface

  • Original Paper
  • Published:
Landslides Aims and scope Submit manuscript

Abstract

In recent decades, early warning systems using tilt sensors to predict the occurrence of landslides have been developed and employed in slope monitoring due to the simple installation and low cost of these systems. However, few studies were carried out to investigate the tilting behaviors of landslides, and the prediction methods for the occurrence of slope failure based on tilting measurements also demand detailed investigations. In this paper, pre-failure tilting behaviors of slopes were investigated by performing a series of model tests as well as a field test. The test results reveal a linear relationship between the reciprocal tilting rate and time during the acceleration stage of tilting before slope failure. Furthermore, an equation for this linear relation was also proposed. By approximating the reciprocal tilting rate to be 0/o, the slope failure time can be forecasted using the proposed equation, and the predicted failure time is consistent with the actual slope failure time recorded in this study. Additionally, the correlation between the tilting rate and remaining time before slope failure in logarithmic space was also studied, and most of the data is situated in a region with boundaries. Based on this region, it is possible to anticipate the remaining time before slope failure at an arbitrary tilting rate in the acceleration stage. Conclusively, this paper provides comprehensive investigations on the correlation between the pre-failure tilting behaviors and duration time before landslides, and also introduces a method to potentially predict the occurrence of slope failure based on the slope tilting measurement.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  • Angeli MG, Pasuto A, Silvano S (2000) A critical review of landslide monitoring experiences. Eng Geol 55(3):133–147

    Article  Google Scholar 

  • Carlà T, Intrieri E, Di TF, Nolesini T, Gigli G, Casagli N (2017) Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses. Landslides 14(2):517–534

    Article  Google Scholar 

  • Dikshit A, Satyam DN, Towhata I (2018) Early warning system using tilt sensors in Chibo, Kalimpong, Darjeeling Himalayas, India. Nat Hazards 94(2):727–741

    Article  Google Scholar 

  • Dixon N, Smith A, Flint JA, Khanna R, Clark B, Andjelkovic M (2018) An acoustic emission landslide early warning system for communities in low-income and middle-income countries. Landslides 15(8):1631–1644

    Article  Google Scholar 

  • Federico A, Popescu M, Murianni A (2015) Temporal prediction of landslide occurrence: a possibility or a challenge? Italian J Eng Geol Environ 1:41–60

    Google Scholar 

  • Fukuzono T (1985) A new method for predicting the failure time of a slope. Proc. IV, international conference and field workshop on landslides, Tokyo, pp 145-150

  • García A, Hördt A, Fabian M (2010) Landslide monitoring with high resolution tilt measurements at the Dollendorfer Hardt landslide, Germany. Geomorphology 120(1–2):16–25

    Article  Google Scholar 

  • Hao SW, Liu C, Lu CS, Elsworth D (2016) A relation to predict the failure of materials and potential application to volcanic eruptions and landslides. Sci Rep 6(27877):1–7

    Google Scholar 

  • Intrieri E, Gigli G (2016) Landslide forecasting and factors influencing predictability. Nat Hazards Earth Syst Sci 16:2501–2510

    Article  Google Scholar 

  • Intrieri E, Gigli G, Mugnai F, Fanti R, Casagli N (2012) Design and implementation of a landslide early warning system. Eng Geol 147(148):124–136

    Article  Google Scholar 

  • Intrieri E, Carlà T, Gigli G (2019) Forecasting the time of failure of landslides at slope-scale: a literature review. Earth Sci Rev 193:333–349

    Article  Google Scholar 

  • Ishihara Y, Kobatake S (1979). Run off model of flood forecasting. Bulletin of the Disaster Prevention Research Institute, Kyoto University, 29: 27–43

  • Keefer DK, Wilson RC, Mark RK, Brabb EE, Brown W, Ellen SD, Harp EL, Wieczorek GF, Alger CS, Zatkin RS (1987) Real-time landslide warning during heavy rainfall. Science 238:921–925

    Article  Google Scholar 

  • Koerner RM, McCabe WM, Lord AE (1981) Acoustic emission behaviour and monitoring of soils. In acoustic emission in geotechnical practice. ASTM STP 750:93–141

    Google Scholar 

  • Kuramoto K, Noro T, Osanai N, Kobayashi M, Okada K (2005). A study on rainfall indexes for giving early warning information for sediment-related disasters. Proceedings of annual research meeting in 2005, Japan Society of Erosion Control Engineering, pp 186-187 (in Japanese)

  • Manconi A, Giordan D (2016) Landslide failure forecast in near-real-time. Geomat Nat Haz Risk 7(2):639–648

    Article  Google Scholar 

  • Mufundirwa A, Fujii Y, Kodama J (2010) A new practical method for prediction of geomechanical failure-time. Int J Rock Mech Min Sci 47(7):1079–1090

    Article  Google Scholar 

  • Okada K (2001). Soil water index Sokko-Jiho. Japan Meteorological Agency 69-567-100 (in Japanese)

  • Okamoto T, Larsen JO, Matsuura S, Asano S, Takeuchi Y, Grande L (2004) Displacement properties of landslide masses at the initiation of failure in quick clay deposits and the effects of meteorological and hydrological factors. Eng Geol 72:233–251

    Article  Google Scholar 

  • Osanai N, Shimizu T, Kuramoto K, Kojima S, Noro T (2010) Japanese early-warning for debris flows and slope failure using rainfall indices with radial basis function network. Landslides 7(3):325–338

    Article  Google Scholar 

  • Petley DN (2012) Global patterns of loss of life from landslides. Geology 40:927–930

    Article  Google Scholar 

  • Petley DN, Bulmer MH, Murphy W (2002) Patterns of movement in rotational and translational landslides. Geology 30(8):719–722

    Article  Google Scholar 

  • Petley DN, Mantovani F, Bulmer MH, Zannoni A (2005) The use of surface monitoring data for the interpretation of landslide movement patterns. Geomorphology 66:133–147

    Article  Google Scholar 

  • Rouse C, Styles P, Wilson SA (1991) Microseismic emissions from flowslide-type movements in South Wales. Eng Geol 31(1):91–110

    Article  Google Scholar 

  • Saito M (1969). Forecasting time of slope failure by tertiary creep, in: Proceedings of 7th international conference on soil mechanics and foundations engineering. Montreal, Canada, Pergamon Press, Oxford, Great Britian, pp 667–683

  • Saito M (1987) On application of creep curves to forecast the time of slope failure. J Jpn Landslide Soc 24:30–38

    Article  Google Scholar 

  • Smethurst JA, Smith A, Uhlemann S, Wooff C, Chambers J, Hughes P, Lenart S, Saroglou H, Springman SM, Löfroth H, Hughes D (2017) Current and future role of instrumentation and monitoring in the performance of transport infrastructure slopes. Q J Eng Geol Hydrogeol 50:271–286

    Article  Google Scholar 

  • Smith A, Dixon N, Meldrum P, Haslam E, Chambers J (2014) Acoustic emission monitoring of a soil slope: comparisons with continuous deformation measurements. Géotechnique Lett 4:255–261

    Article  Google Scholar 

  • Towhata I, Uchimura T, Gallage CPK (2005). On early detection and warning against rainfall-induced landslide. Proc. of the first general assembly and the fourth session of Board of Representatives of the international consortium on landslides (ICL), springer, Washington, DC, pp 133–139

  • Uchimura T, Towhata I, Trinh TLA, Fukuda J, Carlos JBB, Wang L, Seko I, Uchida T, Matsuoka A, Ito Y, Onda Y, Iwagami S, Kim MS, Sakai N (2010) Simple monitoring method for precaution of landslides watching tilting and water contents on slopes surface. Landslides 7(3):351–357

    Article  Google Scholar 

  • Uchimura T, Towhata I, Wang L, Nishie S, Yamaguchi H, Seko I, Qiao J (2015) Precaution and early warning of surface failure of slopes by using tilt sensors. Soil Foundation 55(5):1086–1099

    Article  Google Scholar 

  • Voight B (1988) A method for prediction of volcanic eruptions. Nature 332:125–130

    Article  Google Scholar 

  • Voight B (1989) A relation to describe rate-dependent material failure. Science 243:200–203

    Article  Google Scholar 

  • Xie JR, Uchimura T, Chen P, Liu JP, Xie CR, Shen Q (2019) A relationship between displacement and tilting angle of the slope surface in shallow landslides. Landslides 16(6):1243–1251

    Article  Google Scholar 

  • Zhang FY, Huang XW (2018) Trend and spatiotemporal distribution of fatal landslides triggered by non-seismic effects in China. Landslides 15(8):1663–1674

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Chinese Scholarship Council (CSC,Grant No.201506370052) for PhD studies of the first author, and the Grants-in-Aid for Scientific Research of the Japan Society for the Promotion of Science (JSPS), Core-to-Core Program B (No. 16H04407).

Author information

Authors and Affiliations

  1. Department of Civil Engineering, Central South University, Changsha, 410075, Hunan province, China

    Jiren Xie & Shifan Qiao

  2. Department of Civil Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan

    Jiren Xie & Zain Maqsood

  3. Department of Civil and Environmental Engineering, Saitama University, 255 Shimo-Okubo, Sakura-ku, Saitama, 338-8570, Japan

    Taro Uchimura & Shangning Tao

  4. Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto, 611-0011, Japan

    Gonghui Wang

  5. Department of Civil Engineering, Hunan University of Technology, Zhuzhou, 412000, Hunan, China

    Quan Shen & Jiapeng Liu

  6. College of Civil Engineering and Architecture, GuangXi University, Nanning, 530004, China

    Quan Shen, Canrong Xie, Weikai Lei, Pan Chen, Hongyuan Dong & Guoxiong Mei

Authors
  1. Jiren Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Taro Uchimura
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gonghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Quan Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zain Maqsood
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Canrong Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jiapeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Weikai Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shangning Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hongyuan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Guoxiong Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Shifan Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Quan Shen.

Appendix

Appendix

  1. 1)

    The method for the calculation of tilting rates

In this study, the data series for analyzing were selected with an interval of 1° in the accelerating stage before slope failure considering the influence of noises as well as the fluctuation of the monitoring data which is close to 1o. As shown in Fig. 23, the tilting rate can be approximated using the following equation

$$ {\left(\frac{d\theta}{d t}\right)}_{ij}=\frac{d\theta}{\left({t}_{ij}-{t}_{ij-1}\right)}\left( ij=i1,i2,\cdots, in\right) $$
(A-1)

Where tij and tij − 1 represent time, and dθ means the increment of tilting angles during the periord from tij − 1 to tij, in this study, dθ = 1°. \( {\left(\frac{d\theta}{d t}\right)}_{ij} \) is the tilting rate at time tij.

Then the reciprocal tilting rate can be expressed as

$$ {\left(\frac{d t}{d\theta}\right)}_{ij}=\frac{\left({t}_{ij}-{t}_{ij-1}\right)}{d\theta}\left( ij=i1,i2,\cdots, in\right) $$
(A-2)
Fig. 23
figure 23

The method of data selection for analysis in the accelerating stage of slope failure

Full size image

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, J., Uchimura, T., Wang, G. et al. A new prediction method for the occurrence of landslides based on the time history of tilting of the slope surface. Landslides 17, 301–312 (2020). https://doi.org/10.1007/s10346-019-01283-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10346-019-01283-8

Keywords

Search

Navigation