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A Monte Carlo technique in safety assessment of slope under seismic condition

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Abstract

In geotechnical engineering, stabilization of slopes is one of the significant issues that needs to be considered especially in seismic situation. Evaluation and precise prediction of factor of safety (FOS) of slopes can be useful for designing/analyzing very important structures such as dams and highways. Hence, in the present study, an attempt has been done to evaluate/predict FOS of many homogenous slopes in different conditions using Monte Carlo (MC) simulation technique. For achieving this aim, the most important parameters on the FOS were investigated, and finally, slope height (H), slope angle (α), cohesion (C), angle of internal friction (\(\varnothing\)) and peak ground acceleration (PGA) were selected as model inputs to estimate FOS values. In the first step of analysis, a multiple linear regression (MLR) equation was developed and then it was used for evaluation and prediction by MC technique. Generally, MC model simulated FOS of less than 1.18, lower and higher than measured and predicted FOS values, respectively. However, the results of MC simulation for the FOS values of more than 1.33, is higher than those measured and predicted FOS values. As a result, the mean of FOS values simulated by MC was very close to the mean of actual FOS values. Moreover, results of sensitivity analysis demonstrated that the (\(\varnothing\)), among other parameters, is the most effective one on FOS. The obtained results indicated that MC is a reliable approach for evaluating and estimating FOS of slopes with high degree of performance.

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References

  1. De Blasio FV (2010) Introduction to the physics of landslides. Springer, Berlin

    Google Scholar 

  2. 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 

  3. Davis RO, Desai CS, Smith NR (1993) Stability of motions of translational landslides. J Geotech Engg ACSE 119:420–432

    Article  Google Scholar 

  4. Wang HB, Xu WY, Xu RC (2005) Slope stability evaluation using back propagation neural networks. Eng Geol 80:302–315

    Article  Google Scholar 

  5. Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Géotechnique 5(1):7–17

    Article  Google Scholar 

  6. Spencer E (1967) A method of analysis of the stability of embankments assuming parallel inter-slice forces. Geotechnique 17:11–26

    Article  Google Scholar 

  7. Chau KT (1995) Landslides modeled as bifurcations of creeping slopes with non-linear friction law. Int J Solids Struct 32:3451–3464

    Article  MATH  Google Scholar 

  8. Yang CX, Tham LG, Feng XT, Wang YJ, Lee PKK (2004) Two stepped evolutionary algorithm and its application to stability analysis of slopes. J Comput Civil Eng 18(2):145–153

    Article  Google Scholar 

  9. 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

  10. Samui P, Kothari DP (2011) Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Scientia Iranica 18(1):53–58

    Article  MATH  Google Scholar 

  11. Erzin Y, Cetin T (2013) The predıctıon of the crıtıcal factor of safety of homogeneous fınıte slopes usıng neural networks and multıple regressıons. Comput Geosci 51:305–313

    Article  Google Scholar 

  12. Baker R (2006) A relation between safety factors with respect to strength and height of slopes. Comput Geotech 33(4):275–277

    Article  Google Scholar 

  13. Lee S, Pradhan B (2006) Probabilistic landslide risk mapping at Penang Island, Malaysia. J Earth Syst Sci 115(6):661–672

    Article  Google Scholar 

  14. Legorreta PG, Bursik M (2009) Logisnet: a tool for multimethod, multiple soil layers slope stability analysis. Comput Geosci 35:1007–1016

    Article  Google Scholar 

  15. Irigaray C, El Hamdouni R, Jiménez-Perálvarez JD, Fernández P, Chacón J (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 

  16. Verma AK, Singh TN, Monjezi M (2010) Intelligent prediction of heating value of coal. Iran J Earth Sci 2:32–38

    Google Scholar 

  17. Ramakrishnan D, Singh TN, Verma AK, Gulati A, Tiwari KC (2013) Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India. Nat Hazards 65(1):315–330

    Article  Google Scholar 

  18. Verma AK, Maheshwar S (2014) Comparative study of intelligent prediction models for pressure wave velocity. J Geosci Geomat 2(3):130–138

    Google Scholar 

  19. Verma AK, Singh TN, Chauhan NK, Sarkar K (2016) A hybrid FEM-ANN approach for slope instability prediction. J Inst Eng (India) Ser A 97(3):171–180

    Article  Google Scholar 

  20. Sakellariou M, Ferentinou M (2005) A study of slope stability prediction using neural networks. Geotech Geol Eng 24(3):419–445

    Article  Google Scholar 

  21. 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. EngGeol 85:347–366

    Google Scholar 

  22. 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 

  23. Oh HJ, Pradhan B (2011) Application of a neuro-fuzzy model to landslide susceptiblity mapping for shallow landslides in tropical hilly area. Comput Geosci 37:1264–1276

    Article  Google Scholar 

  24. 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(1):201–210

    Article  Google Scholar 

  25. 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. doi:10.1007/s00366-015-0400-7

    Google Scholar 

  26. Sari M, Ghasemi E, Ataei M (2013) Stochastic modeling approach for the evaluation of backbreak due to blasting operations in open pitmines. Rock Mech Rock Eng. doi:10.1007/s00603-013-0438-z

    Google Scholar 

  27. Choobbasti AJ, Farrokhzad F, Barari A (2009) Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arab J Geosci 2(4):311–319

    Article  Google Scholar 

  28. Kramer SL (1996) Geotechnical earthquake engineering. Prentice-Hall, New Jersey

    Google Scholar 

  29. Hasanipanah M, Jahed Armaghani D, Monjezi M, Shams S (2016) Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system. Environ. Earth Sci 75:808

    Article  Google Scholar 

  30. Hasanipanah M, Naderi R, Kashir J, Noorani SA, Zeynali Aaq Qaleh A (2016) Prediction of blast-produced ground vibration using particle swarm optimization. Eng Comput. doi:10.1007/s00366-016-0462-1

    Google Scholar 

  31. Hasanipanah M, Jahed Armaghani D, Bakhshandeh Amnieh H, Abd Majid MZ, Tahir MMD (2016) Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Comput Appl. doi:10.1007/s00521-016-2434-1

    Google Scholar 

  32. Jahed Armaghani D, Mahdiyar A, Hasanipanah M, Shirani Faradonbeh R, Khandelwal M, Bakhshandeh Amnieh H (2016) Risk assessment and prediction of flyrock distance by combined multiple regression analysis and monte carlo simulation of quarry blasting. Rock Mech Rock Eng. doi:10.1007/s00603-016-1015-z

    Google Scholar 

  33. Hasanipanah M, Shahnazar A, Arab H, Bagheri Golzar S, Amiri M (2016) Developing a new hybrid-AI model to predict blast-induced backbreak. Eng Comput. doi:10.1007/s00366-016-0477-7.

    Google Scholar 

  34. SPSS Inc (2007) SPSS for Windows (Version 16.0). SPSS Inc, Chicago

    Google Scholar 

  35. US EPA Technical Panel (1997) Guiding principles for Monte Carlo analysis. Us Epa, pp 1–35

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

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

    Article  Google Scholar 

  38. Bianchini F, Hewage K (2012) Probabilistic social cost-benefit analysis for green roofs: a lifecycle approach. Build Environ 58:152–162. doi:10.1016/j.buildenv.2012.07.005

    Article  Google Scholar 

  39. 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. doi:10.1016/j.ijrmms.2012.03.011

    Article  Google Scholar 

  40. Abramson LW (2002) Slope stability and stabilization methods. Wiley, Singapore

    Google Scholar 

  41. Li S, Zhao HB, Ru Z (2013) Slope reliability analysis by updated support vector machine and Monte Carlo simulation. Nat Hazards 65:707–722

    Article  Google Scholar 

  42. Tobutt DC (1982) Monte Carlo simulation methods for slope stability. Comput Geosci 8:199–208

    Article  Google Scholar 

  43. Morin AM, 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 

  44. 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, p 34

  45. 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, p 181

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

    Article  Google Scholar 

  47. Malkawi AIH, Hassan WF, Abdulla FA (2000) Uncertainty and reliability analysis applied to slope stability. Struct Saf 22:161–187

    Article  Google Scholar 

  48. 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 

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

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. Department of Structure and Material, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia

    Amir Mahdiyar & Arham Abdullah

  2. Department of Mining Engineering, University of Kashan, Kashan, Iran

    Mahdi Hasanipanah

  3. Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, 15914, Iran

    Danial Jahed Armaghani

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

    Behrouz Gordan

  5. Young Researchers and Elite Club, Qom Branch, Islamic Azad University, Qom, Iran

    Hossein Arab

  6. UTM Construction Research Centre, Institute for Smart Infrastructure and Innovative Construction (ISIIC), Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Muhd Zaimi Abd Majid

Authors
  1. Amir Mahdiyar
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  2. Mahdi Hasanipanah
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  3. Danial Jahed Armaghani
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  4. Behrouz Gordan
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  5. Arham Abdullah
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  6. Hossein Arab
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  7. Muhd Zaimi Abd Majid
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Correspondence to Danial Jahed Armaghani.

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Mahdiyar, A., Hasanipanah, M., Armaghani, D.J. et al. A Monte Carlo technique in safety assessment of slope under seismic condition. Engineering with Computers 33, 807–817 (2017). https://doi.org/10.1007/s00366-016-0499-1

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  • DOI: https://doi.org/10.1007/s00366-016-0499-1

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