Skip to main content
Springer Nature Link
Log in

A novel probabilistic simulation approach for forecasting the safety factor of slopes: a case study

  • Original Article
  • Published:
Engineering with Computers Aims and scope Submit manuscript

Abstract

Stabilization of slopes is considered as the aim of the several geotechnical applications such as embankment, tunnel, highway, building and railway and dam. Therefore, evaluation and precise prediction of the factor of safety (FoS) of slopes can be useful in designing these important structures. This research is carried out to evaluate the ability of Monte Carlo (MC) technique for the forecasting the FoS of many homogenous slopes in the static condition. Moreover, the sensitivity of the FoS on the effective parameters was identified. To do this, the most important factors on FoS, such as angle of internal friction \((\emptyset )\), slope angle \((\alpha )\) and cohesion \((C)\) were investigated and used as the inputs to forecast the FoS. Then, a regression analysis was performed, and the results were used for the FoS prediction using MC. The obtained results of MC simulation were very close with the actual FoS values. The mean of the simulated FoS by MC was achieved as 1.32, while, according to actual FoSs, it was 1.27. These results showed that MC is an acceptable technique to estimate the FoS of slopes with high level of accuracy. Moreover, based on the results of correlation and regression sensitivity analyses, it was concluded that angle of internal friction, was the most influential one on the results of FoS in both types of sensitivity analyses.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

Abbreviations

\(\emptyset\) :

Angle of internal friction

AI:

Artificial intelligent

\({\text{C}}\) :

Cohesion

CPD:

Continuous probability distributions

FoS:

Factor of safety

FD:

Finite difference

FM:

Finite element

LA:

Limit analysis

LEM:

Limit equilibrium method

MC:

Monte Carlo

MR:

Multiple regression

\({\text{PGA}}\) :

Peak ground acceleration

RMSE:

Root mean squared error

\(\alpha\) :

Slope angle

\(H\) :

Slope height

\(\gamma\) :

Unit weight

VAF:

Variance account for

References

  1. Taheri A, Tani K (2010) Assessment of the stability of rock slopes by the slope stability rating classification system. Rock Mech Rock Eng 43:321–333. https://doi.org/10.1007/s00603-009-0050-4

    Article  Google Scholar 

  2. Sharma LK, Umrao RK, Singh R, Ahmad M, Singh TN (2017) Stability investigation of hill cut soil slopes along national highway 222 at Malshej Ghat, Maharashtra, India. J Geol Soc India 89(2):165–174

    Article  Google Scholar 

  3. Monjezi M, Singh TN (2000) Slope instability in an open cast mine. Coal Int 8:145–147

    Google Scholar 

  4. Wang H, Xu W, Xu R (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80:302–315

    Article  Google Scholar 

  5. Canal A, Akin M (2016) Assessment of rock slope stability by probabilistic-based Slope Stability Probability Classification method along highway cut slopes in Adilcevaz-Bitlis (Turkey). J Mt Sci 13:1893–1909

    Article  Google Scholar 

  6. Umrao RK, Singh R, Sharma LK, Singh TN (2017) Soil slope instability along a strategic road corridor in Meghalaya, northeastern India. Arab J Geosci. https://doi.org/10.1007/s12517-017-3043-8

    Google Scholar 

  7. de Blasio FV (2011) Introduction to the physics of landslides. Springer Netherlands, Dordrecht

    Book  Google Scholar 

  8. Harabinová S (2017) Assessment of slope stability on the road. Procedia Eng 190:390–397. https://doi.org/10.1016/j.proeng.2017.05.354

    Article  Google Scholar 

  9. Baker R (2006) A relation between safety factors with respect to strength and height of slopes. Comput Geotech 33:275–277

    Article  Google Scholar 

  10. Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672

    Article  Google Scholar 

  11. Irigaray C, El Hamdouni R, Jiménez-Perálvarez JD et al (2012) Spatial stability of slope cuts in rock massifs using GIS technology and probabilistic analysis. Bull Eng Geol Environ 71:569–578

    Article  Google Scholar 

  12. Singh TN, Singh R, Singh B, Sharma LK, Singh R, Ansari MK (2016) Investigations and stability analyses of Malin village landslide of Pune district, Maharashtra, India. Nat Hazards 81(3):2019–2030

    Article  Google Scholar 

  13. Davis RO, Desai CS, Smith NR (1993) Stability of motions of translational landslides. J Geotech Eng 119:420–432. https://doi.org/10.1061/(ASCE)0733-9410(1993)119:3(420)

    Article  Google Scholar 

  14. Helmstetter A, Sornette D, Grasso J-R, Andersen JV, Gluzman S, Pisarenko V (2004) Slider block friction model for landslides: application to Vaiont and La Clapie´ re landslides. J Geophys Res 109:B02409

    Article  Google Scholar 

  15. Yang CX, Tham LG, Feng X-T et al (2004) Two-stepped evolutionary algorithm and its application to stability analysis of slopes. J Comput Civ Eng 18:145–153

    Article  Google Scholar 

  16. Shangguan Z, Li S, Luan M (2009) Intelligent forecasting method for slope stability estimation by using probabilistic neural networks. Electron J Geotech Eng Bundle 13

  17. Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci Iran 18:53–58

    Article  MATH  Google Scholar 

  18. Sharma LK, Sirdesai NN, Sharma KM, Singh TN (2017) Experimental study to examine the independent roles of lime and cement on the stabilization of a mountain soil: a comparative study. Appl Clay Sci 152:183–195

    Article  Google Scholar 

  19. Sharma LK, Umrao RK, Singh R, Ahmad M, Singh TN (2017) Geotechnical characterization of road cut hill slope forming unconsolidated geo-materials: a case study. Geotech Geol Eng 35(1):503–515

    Article  Google Scholar 

  20. Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366

    Article  Google Scholar 

  21. Yilmaz I (2009) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. Bull Eng Geol Environ 68:297–306

    Article  Google Scholar 

  22. Das SK, Biswal RK, Sivakugan N, Das B (2011) Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environ Earth Sci 64:201–210

    Article  Google Scholar 

  23. Sharma LK, Singh R, Umrao RK, Sharma KM, Singh TN (2017) Evaluating the modulus of elasticity of soil using soft computing system. Eng Comput 33(3):497–507

    Article  Google Scholar 

  24. Sharma LK, Singh TN (2018) Regression based models for the prediction of unconfined compressive strength of artificially structured soil. Eng Comput 34:175–186

    Article  Google Scholar 

  25. Erzin Y, Cetin T (2013) The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Comput Geosci 51:305–313

    Article  Google Scholar 

  26. Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2015) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput 32:85–97. https://doi.org/10.1007/s00366-015-0400-7

    Article  Google Scholar 

  27. Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643

    Article  Google Scholar 

  28. Hasanipanah M, Monjezi M, Shahnazar A, Jahed Armaghanid D, Farazmand A (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement 75:289–297

    Article  Google Scholar 

  29. Hasanipanah M, Noorian-Bidgoli M, Jahed Armaghani D, Khamesi H (2016) Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling. Eng Comput 32(4):705–715. https://doi.org/10.1007/s00366-016-0447-0

    Article  Google Scholar 

  30. Taheri K, Hasanipanah M, Bagheri Golzar S, Abd Majid MZ (2017) A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Eng Comput 33(3):689–700. https://doi.org/10.1007/s00366-016-0497-3

    Article  Google Scholar 

  31. Hasanipanah M, Shirani Faradonbeh R, Bakhshandeh Amnieh H, Jahed Armaghani D, Monjezi M (2017) Forecasting blastinduced ground vibration developing a CART model. Eng Comput 33(2):307–316. https://doi.org/10.1007/s00366-016-0475-9

    Article  Google Scholar 

  32. Nikafshan Rad H, Hasanipanah M, Rezaei M, Lotfi Eghlim A (2017) Developing a least squares support vector machine for estimating the blast-induced flyrock. Eng Comput. https://doi.org/10.1007/s00366-017-0568-0

    Google Scholar 

  33. Hasanipanah M, Shahnazar A, Bakhshandeh Amnieh H, Jahed Armaghani D (2017) Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Eng Comput 33(1):23–31. https://doi.org/10.1007/s00366-016-0453-2

    Article  Google Scholar 

  34. Hasanipanah M et al (2018) Prediction of an environmental issue of mine blasting: an imperialistic competitive algorithm-based fuzzy system. Int J Environ Sci Technol 15(3):551–560. https://doi.org/10.1007/s13762-017-1395-y

    Article  Google Scholar 

  35. Jahed Armaghani D, Hasanipanah M, Bakhshandeh Amnieh H, Tonnizam Mohamad E (2018) Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Comput Appl 29(9):457–465. https://doi.org/10.1007/s00521-016-2577-0

    Article  Google Scholar 

  36. Ghasemi E, Sari M, Ataei M (2012) Development of an empirical model for predicting the effects of controllable blasting parameters on flyrock distance in surface mines. Int J Rock Mech Min Sci 52:163–170. https://doi.org/10.1016/j.ijrmms.2012.03.011

    Article  Google Scholar 

  37. Mahdiyar A et al (2017) A Monte Carlo technique in safety assessment of slope under seismic condition. Eng Comput 33(4):807–817. https://doi.org/10.1007/s00366-016-0499-1

    Article  Google Scholar 

  38. Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2:311–319

    Article  Google Scholar 

  39. Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H et al (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl. https://doi.org/10.1007/s00521-016-2434-1

    Google Scholar 

  40. Jahed Armaghani D et al. (2016) Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Comput Appl 1–11. https://doi.org/10.1007/s00521-016-2598-8

  41. Solver F (2010) Premium solver platform. User Guide, Frontline Systems

    Google Scholar 

  42. Dunn WL, Shultis JK (2009) Monte Carlo methods for design and analysis of radiation detectors. Radiat Phys Chem 78:852–858. https://doi.org/10.1016/j.radphyschem.2009.04.030

    Article  Google Scholar 

  43. Mahdiyar A et al (2016) Probabilistic private cost-benefit analysis for green roof installation: a Monte Carlo simulation approach. Urban For Urban Green 20:317–327. https://doi.org/10.1016/j.ufug.2016.10.001

    Article  Google Scholar 

  44. Zhu H, Zhang LM, Xiao T, Li XY (2017) Enhancement of slope stability by vegetation considering uncertainties in root distribution. Comput Geotech 85:84–89. https://doi.org/10.1016/j.compgeo.2016.12.027

    Article  Google Scholar 

  45. Li S, Zhao H-B, Ru Z (2013) Slope reliability analysis by updated support vector machine and Monte Carlo simulation. Nat Hazards 65:707–722. https://doi.org/10.1007/s11069-012-0396-x

    Article  Google Scholar 

  46. Calamak M, Yanmaz AM (2014) Probabilistic assessment of slope stability for earth-fill dams having random soil parameters. In: 11th National Conference on Hydraulics in Civil Engineering & 5th International Symposium on Hydraulic Structures: Hydraulic Structures and Society-Engineering Challenges and Extremes. Engineers Australia

  47. Danka J (2011) Probability of failure calculation of dikes based on Monte Carlo simulation. Geotechnical Engineering: New Horizons. In: Proceedings of the 21st European Young Geotechnical Engineers. Conference, Rotterdam

  48. El-Ramly H, Morgenstern NR, Cruden DM (2002) Probabilistic slope stability analysis for practice. Can Geotech J 39:665–683

    Article  Google Scholar 

  49. Husein Malkawi AI, Hassan WF, Abdulla FA (2000) Uncertainty and reliability analysis applied to slope stability. Struct Saf 22:161–187. https://doi.org/10.1016/S0167-4730(00)00006-0

    Article  Google Scholar 

  50. Ma J, Wang J (2014) Probabilistic stability analyses of the slope reinforcement system based on response surface-Monte Carlo simulation. Electron J Geotech Eng 19:6569–6583

    Google Scholar 

  51. Morin MA, Ficarazzo F (2006) Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz–Ram model. Comput Geosci 32:352–359

    Article  Google Scholar 

  52. Liu MM (2014) Probabilistic prediction of green roof energy performance under parameter uncertainty. Energy 77:667–674. https://doi.org/10.1016/j.energy.2014.09.043

    Article  Google Scholar 

  53. Song J, Wang Z (2016) Evaluating the impact of built environment characteristics on urban boundary layer dynamics using an advanced stochastic approach. Atmos Chem Phys 16:6285–6301. https://doi.org/10.5194/acp-16-6285-2016

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Department, Sharif University of Technology, Tehran, Iran

    S. Farid F. Mojtahedi

  2. Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Malaysia

    Sanaz Tabatabaee

  3. Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mahyar Ghoroqi

  4. Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Mehran Soltani Tehrani

  5. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Behrouz Gordan

  6. Department of Civil Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Milad Ghoroqi

Authors
  1. S. Farid F. Mojtahedi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sanaz Tabatabaee
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mahyar Ghoroqi
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Mehran Soltani Tehrani
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Behrouz Gordan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Milad Ghoroqi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Mahyar Ghoroqi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mojtahedi, S.F.F., Tabatabaee, S., Ghoroqi, M. et al. A novel probabilistic simulation approach for forecasting the safety factor of slopes: a case study. Engineering with Computers 35, 637–646 (2019). https://doi.org/10.1007/s00366-018-0623-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00366-018-0623-5

Keywords