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Engineering with Computers

, Volume 35, Issue 2, pp 637–646 | Cite as

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

  • S. Farid F. Mojtahedi
  • Sanaz Tabatabaee
  • Mahyar GhoroqiEmail author
  • Mehran Soltani Tehrani
  • Behrouz Gordan
  • Milad Ghoroqi
Original Article
  • 233 Downloads

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.

Keywords

Factor of safety Monte Carlo simulation Regression analysis Sensitivity analysis 

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

Notes

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • S. Farid F. Mojtahedi
    • 1
  • Sanaz Tabatabaee
    • 2
  • Mahyar Ghoroqi
    • 3
    Email author
  • Mehran Soltani Tehrani
    • 4
  • Behrouz Gordan
    • 5
  • Milad Ghoroqi
    • 6
  1. 1.Civil Engineering DepartmentSharif University of TechnologyTehranIran
  2. 2.Faculty of Civil EngineeringUniversiti Teknologi Malaysia (UTM)SkudaiMalaysia
  3. 3.Young Researchers and Elites Club, Science and Research BranchIslamic Azad UniversityTehranIran
  4. 4.Department of Civil Engineering, Najafabad BranchIslamic Azad UniversityNajafabadIran
  5. 5.Department of Geotechnics and Transportation, Faculty of Civil EngineeringUniversiti Teknologi MalaysiaUTM SkudaiMalaysia
  6. 6.Department of Civil Engineering, Central Tehran BranchIslamic Azad UniversityTehranIran

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