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Uncertainty analysis of the stability parameters of rock walls

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of this paper is to investigate the effects of natural uncertainties and effective parameters on the stability of plate-type rock walls. For this, the effective factors and geo-mechanical properties in the study area were obtained using field experiments. Stability analysis of rock walls was investigated for 40 scenarios in dry and saturated states. These parameters were then evaluated using Easyfit software and Markov chain analysis and Monte Carlo simulation by Rock Plane software. Comparison of the results of numerical and uncertainty methods shows that the rock walls with 60-80 degree slope are stable; and In saturated state they require stability due to the reduction of shear strength. Fixation of the rock walls was also investigated, indicating an optimum angle of 30° for the installation of the rock screw. The results show that the Monte Carlo simulation provides a simpler interpretation and the uncertainty methods are more accurate and reliable than the numerical methods.
Rocznik
Strony
703--721
Opis fizyczny
Bibliogr. 27 poz., il., tab.
Twórcy
autor
  • Department of Civil Engineering, Arak Branch, Islamic Azad University, Arak, Iran
  • Department of Geology, Faculty of Earth Sciences, Arak University, of Technology, Iran
Bibliografia
  • 1. Kalkani, E. C, “Stability analysis of rock slopes”, Water Power, 1976.
  • 2. M. J. Kanda, T. R. Stacey, “The influence of various factors on the results of stability analysis of rock slopes and on the evaluation of risk”, Journal of the Southern African Institute of Mining and Metallurgy 116: 1075-1081, 2016.
  • 3. M. Valipour, M. E Banihabib, S. M. R. Behbahani, “Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir”, Journal of hydrology 476: 433-441, 2013.
  • 4. N. Gupta, “Artificial neural network”, Network and Complex Systems 3: 24-28, 2013.
  • 5. P. Kayastha, M. R. Dhital, F. D. Smedt, “Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: a case study from the Tinau watershed, west Nepal”, Computers & Geosciences 52: 398-408, 2013.
  • 6. B. Stimpson, “Modelling materials for engineering rock mechanics”, In International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 7: 77-121, 1970.
  • 7. M. Yousefirad, “Hydrostratigraphy of Haftad Gholle Karst, Markazi province, Iran, optimized by Fuzzy Logic”, Geofísica Internacional 51: 365-376, 2012.
  • 8. I. Yilmaz, “Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine”, Environmental Earth Sciences 61: 821-836, 2010.
  • 9. M. Yousefirad, S. Mokhtar, “The classification of hydrostratigraphic units of North Mahallat based on fuzzy logic”, Journal of Food, Agriculture & Environment, 10: 823-827, 2012.
  • 10. S. Chauhan, M. Sharma, M. K. Arora, N. K. Gupta, “Landslide susceptibility zonation through ratings derived from artificial neural network”, International Journal of Applied Earth Observation and Geoinformation 12: 340-350, 2010.
  • 11. S. K. Das, R. K. Biswal, N. Sivakugan, B. Das, “Classification of slopes and prediction of factor of safety using differential evolution neural networks”, Environmental Earth Sciences 64: 201-210, 2011.
  • 12. Q. Lü, C.L Chan, B. K. Low, “Probabilistic evaluation of ground-support interaction for deep rock excavation using artificial neural network and uniform design”, Tunnelling and Underground Space Technology 32: 1-18, 2012.
  • 13. L. L. Zhang, Z. B. Zuo, G. L. Ye, D. S. Jeng, J. H. Wang, “Probabilistic parameter estimation and predictive uncertainty based on field measurements for unsaturated soil slope”, Computers and Geotechnics 48: 72-81, 2013.
  • 14. D. Q. Li, T. Xiao, Z. J. Cao, C. B. Zhou, L. M. Zhang, “Enhancement of random finite element method in reliability analysis and risk assessment of soil slopes using Subset Simulation”, Landslides 13: 293-303, 2016.
  • 15. A. Kaur, Amritpal, R. K. Sharma, “Slope stability analysis techniques: A review”, International Journal of Engineering Applied Sciences and Technology 1: 52-57, 2016.
  • 16. H. El-Ramly, N. R. Morgenstern, D. M. Cruden, “Lodalen slide: a probabilistic assessment”, Canadian Geotechnical Journal 43: 956-968, 2006.
  • 17. N. Babanouri, H. Dehghani, “Investigating a potential reservoir landslide and suggesting its treatment using limit-equilibrium and numerical methods”, Journal of Mountain Science 14: 432-441, 2017.
  • 18. G. G. Iovine, R. Greco, S. L. Gariano, A. D. Pellegrino, O. G. Terranova, “Shallow-ladslide susceptibility in the Costa Viola mountain ridge (southern Calabria, Italy) with considerations on the role of causal factors”, Natural hazards 73: 111-136, 2014.
  • 19. H. Tang, R. Yong,M. E. Eldin, “Stability analysis of stratified rock slopes with spatially variable strength parameters: the case of Qianjiangping landslide”, Bulletin of engineering geology and the environment 76: 839-853, 2017.
  • 20. X. L. Yang, L. Li, J. H. Yin, “Stability analysis of rock slopes with a modified Hoek-Brown failure criterion”, International Journal for Numerical and Analytical Methods in Geomechanics 28: 181-190, 2004.
  • 21. J. Xiaoyu, Q. Jianping, W. Chenghua, and Z. Yu, “Computer simulation of landslides by the contact element method”, Computers & geosciences 32: 434-441, 2006.
  • 22. W. Li, C. Zhang, “A single-chain-based multidimensional Markov chain model for subsurface characterization”, Environmental and Ecological Statistics 15: 157-174, 2008.
  • 23. S. H. Jiang, D. Q. Li, Z. J. Cao, C. B. Zhou, K. K. Phoon, “Efficient system reliability analysis of slope stability in spatially variable soils using Monte Carlo simulation”, Journal of Geotechnical and Geoenvironmental Engineering 141: 04014096, 2014.
  • 24. S. Mokhtar, M. Yousefirad, “The Stability Analysis of Rok Walls by Artificial Markov Chains in the Roodbar in Lorestan, Iran”, Journal of Engineering and Applied Sciences, 12: 2361-2366, 2017.
  • 25. E. Hoek, J. D. Bray, “Rock slope engineering”, CRC Press, 1981.
  • 26. A. Mahdiyar, M. Hasanipanah, D. Jahed Armaghani, B. Gordan, A. Abdullah, H. Arab, “A Monte Carlo technique in safety assessment of slope under seismic condition”, Engineering with Computers 33: 807-817, 2017.
  • 27. W. Ching, H. Ximin, K. N. Michael, S. Tak-Kuen, “Markov chains Models, algorithms and applications”, Springer Science+ Business Media, 2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-995de53d-9d8b-40db-8a9c-03c7e4ac3894
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