Inverse-square-root-acceleration method for predicting the failure time of landslides

Abstract

Predicting the failure time of unstable slopes is one of the most pivotal issues. In this paper, the inverse square root acceleration (INSRA) method was proposed to estimate the time-of-failure (TOF) of landslides. Four collapsed slopes were presented in the three open-pit mines, two of them were probed by ground-based radar, and two of them were obtained from previous scientific papers. The inverse velocity (INV) method and INSRA method were adopted to analyze these four landslides and one slope which had great deformation but did not reach failure. Compared with the traditional INV method, the INSRA method can promote the forecasting effectiveness and has the advantage of higher accuracy.

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References

  1. 1

    Thiebes B, Bell R, Glade T, et al. Integration of a limit-equilibrium model into a landslide early warning system. Landslides, 2014, 11: 859–875

    Article  Google Scholar 

  2. 2

    Intrieri E, Gigli G, Mugnai F, et al. Design and implementation of a landslide early warning system. Eng Geol, 2012, 147–148: 124–136

    Article  Google Scholar 

  3. 3

    Greco R, Giorgio M, Capparelli G, et al. Early warning of rainfall-induced landslides based on empirical mobility function predictor. Eng Geol, 2013, 153: 68–79

    Article  Google Scholar 

  4. 4

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

    Article  Google Scholar 

  5. 5

    Newcomen W, Dick G. An update to the strain-based approach to pit wall failure prediction, and a justification for slope monitoring. J S Afr Inst Min Metall, 2016, 116: 5

    Article  Google Scholar 

  6. 6

    Deng J. Grey Forecasting and Decision Making. Wuhan: Huazhong University of Science and Technology Press, 1988. 86–128

    Google Scholar 

  7. 7

    Yang S. Engineering Application of Time Series Analysis. Wuhan: Huazhong University of Science and Technology Press, 1992

    Google Scholar 

  8. 8

    Ding J X, Yang Z F, Shang Y J, et al. A new method for spatiotemporal prediction of rainfall-induced landslide. Sci China Ser DEarth Sci, 2006, 49: 421–430

    Article  Google Scholar 

  9. 9

    Liu Z, Shao J, Xu W, et al. Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches. Landslides, 2014, 11: 889–896

    Article  Google Scholar 

  10. 10

    Yao W, Zeng Z, Lian C, et al. Training enhanced reservoir computing predictor for landslide displacement. Eng Geol, 2015, 188: 101–109

    Article  Google Scholar 

  11. 11

    Du J, Yin K, Lacasse S. Displacement prediction in colluvial landslides, Three Gorges Reservoir, China. Landslides, 2013, 10: 203–218

    Article  Google Scholar 

  12. 12

    Lian C, Zeng Z, Yao W, et al. Multiple neural networks switched prediction for landslide displacement. Eng Geol, 2015, 186: 91–99

    Article  Google Scholar 

  13. 13

    Chousianitis K, Del Gaudio V, Kalogeras I, et al. Predictive model of Arias intensity and Newmark displacement for regional scale evaluation of earthquake-induced landslide hazard in Greece. Soil Dyn Earthq Eng, 2014, 65: 11–29

    Article  Google Scholar 

  14. 14

    Du W, Wang G. A one-step Newmark displacement model for probabilistic seismic slope displacement hazard analysis. Eng Geol, 2016, 205: 12–23

    Article  Google Scholar 

  15. 15

    Hwang G S, Chen C H. A study of the Newmark sliding block displacement functions. Bull Earthq Eng, 2013, 11: 481–502

    Article  Google Scholar 

  16. 16

    Zhao Z, Zhou X P, Qian Q H. Fracture characterization and perme-ability prediction by pore scale variables extracted from X-ray CT images of porous geomaterials. Sci China Tech Sci, 2020, 63: 755–767

    Article  Google Scholar 

  17. 17

    Yiğit A. Prediction of amount of earthquake-induced slope displacement by using Newmark method. Eng Geol, 2020, 264: 105385

    Article  Google Scholar 

  18. 18

    Fukuzono T. A new method for predicting the failure time of a slope. In: Proceedings of the 4th International Conference and Field Workshop on Landslides. Tokyo, 1985. 145–150

  19. 19

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

    Article  Google Scholar 

  20. 20

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

    Article  Google Scholar 

  21. 21

    Crosta G B, Agliardi F. How to obtain alert velocity thresholds for large rockslides. Phys Chem Earth Parts A/B/C, 2002, 27: 1557–1565

    Article  Google Scholar 

  22. 22

    Dick G J, Eberhardt E, Cabrejo-Liévano A G, et al. Development of an early-warning time-of-failure analysis methodology for open-pit mine slopes utilizing ground-based slope stability radar monitoring data. Can Geotech J, 2015, 52: 515–529

    Article  Google Scholar 

  23. 23

    Rose N D, Hungr O. Forecasting potential rock slope failure in open pit mines using the inverse-velocity method. Int J Rock Mech Min Sci, 2007, 44: 308–320

    Article  Google Scholar 

  24. 24

    Carlà T, Farina P, Intrieri E, et al. On the monitoring and early-warning of brittle slope failures in hard rock masses: Examples from an open-pit mine. EngGeol, 2017, 228: 71–81

    Google Scholar 

  25. 25

    Saito M. Forecasting time of occurrence of a slope failure. In: Proceedings of the Sixth International Conference on Soil Mechanics and Foundation Engineering. Oxford, Pergamon, 1965. 537–541

    Google Scholar 

  26. 26

    Yoshida T, Yachi M. On the velocity of landslide (in Japanese). In: Proceedings of the 23rd Meeting of Japan Landslide Society, 1984. 136–139

  27. 27

    Chen M X, Jiang Q. An early warning system integrating time-of-failure analysis and alert procedure for slope failures. Eng Geol, 2020, 272: 105629

    Article  Google Scholar 

  28. 28

    Crosta G B, Agliardi F. Failure forecast for large rock slides by surface displacement measurements. Can Geotech J, 2003, 40: 176–191

    Article  Google Scholar 

  29. 29

    Antonello G, Casagli N, Farina P, et al. Ground-based SAR inter-ferometry for monitoring mass movements. Landslides, 2004, 1: 21–28

    Article  Google Scholar 

  30. 30

    Li Y S, Jiao Q S, Hu X H, et al. Detecting the slope movement after the 2018 Baige Landslides based on ground-based and space-borne radar observations. Int J Appl Earth Observ Geoinf, 2020, 84: 101949

    Article  Google Scholar 

  31. 31

    Zhang Y, Meng X M, Dijkstra T A, et al. Forecasting the magnitude of potential landslides based on InSAR techniques. Remote Sens Environ, 2020, 241: 111738

    Article  Google Scholar 

  32. 32

    Zhou X P, Liu L J, Xu C. A modified inverse-velocity method for predicting the failure time of landslides. Eng Geol, 2020, 268: 105521

    Article  Google Scholar 

  33. 33

    Xu Q, Yuan Y, Zeng Y P, et al. Some new pre-warning criteria for creep slope failure. Sci China Tech Sci, 2011, 54: 210–220

    Article  Google Scholar 

  34. 34

    Han M, Rao Y Z, Chen J L, et al. Landslide warning model of creep ion-adsorption rare earth ore slope based on displacement-time curve. Chin Rare Earths, 2019, 40: 1–9

    Google Scholar 

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Correspondence to XiaoPing Zhou.

Additional information

The work was supported by the National Natural Science Foundation of China (Grant Nos. 51839009 and 51679017).

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Zhou, X., Ye, T. Inverse-square-root-acceleration method for predicting the failure time of landslides. Sci. China Technol. Sci. (2021). https://doi.org/10.1007/s11431-020-1722-2

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Keywords

  • failure time of landslide
  • inverse square root of acceleration
  • traditional inverse velocity method