Abstract
Reliability analysis of slope is an important aspect of geotechnical engineering practice. However, it is very difficult to assess the estimation of reliability analysis of slope because it include the uncertainty parameters i.e. soil parameters cohesion of soil (c), angle of shearing resistance (φ) and unit weight of soil (γ). In former days, to execute the reliability analysis of slope, several analytical methods were used but at the present days various computer technique (model) are available and among which Gaussian Process Regression (GPR) and Genetic Programming (GP) are part of them. This paper’s primary objective is to survey the fitness of GPR and GP with the help of First Oder Second Moment Method (FOSM) to find out the Reliability analysis of slope. The GP is the non-parametric kernel probabilistic method. It is adequately assigned by its mean function m(x) and covariance function k(x, x′). GP is a standard technique for acquiring computer automatically to resolve a problem from an eminent-level argument of what need to be performed. This study shows that the proposed GPR based FOSM is suitable alternative for reliability analysis of slope.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods. https://doi.org/10.1016/S0167-7012(00)00201-3
Bishop AW (1955) The use of the slip circle in the stability analysis of slopes. Géotechnique. https://doi.org/10.1680/geot.1955.5.1.7
Chowdhury RN, Xu DW (1994) Rational polynomial technique in slope-reliability analysis. J Geotech Eng 119:1910–1928. https://doi.org/10.1061/(ASCE)0733-9410(1993)119:12(1910)
Cornell CA (1969) A probability-based structural code. ACI J. https://doi.org/10.14359/7446
Gavin K, Xue J (2009) Use of a genetic algorithm to perform reliability analysis of unsaturated soil slopes. Géotechnique 59:545–549. https://doi.org/10.1680/geot.8.T.004
Giasi CI, Masi P, Cherubini C (2003) Probabilistic and fuzzy reliability analysis of a sample slope near Aliano. Eng Geol 67:391–402. https://doi.org/10.1016/S0013-7952(02)00222-3
Holland JH (1975) Adaptation in Natural and Artificial Systems. Univ Michigan Press, Ann Arbor. https://doi.org/10.1137/1018105
Huang F-K, Wang GS (2007) ANN-based reliability analysis for deep excavation. In: Eurocorn2007—international conference on computer as a tool, pp 2039–2046. https://doi.org/10.1109/eurcon.2007.4400328
Hurtado JE, Alvarez DA (2001) Neural-network-based reliability analysis: a comparative study. Comput Methods Appl Mech Eng 191:113–132. https://doi.org/10.1016/S0045-7825(01)00248-1
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685. https://doi.org/10.1109/21.256541
Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc Ser B (Stat Methodol) 63:425–464. https://doi.org/10.1111/1467-9868.00294
Koza JR (1992) The Genetic Programming paradigm: genetically breeding populations of computer programs to solve problems. MIT Press, Cambridge. https://doi.org/10.1109/tai.1990.130444
Mathur S, Samui P (2013) Application of relevance vector machine for structural reliability analysis. In: Proceedings of international conference on advances in civil engineering AETACE, pp 961–967
Papadrakakis M, Papadopoulos V, Lagaros ND (1996) Structural reliability analyis of elastic-plastic structures using neural networks and Monte Carlo simulation. Comput Methods Appl Mech Eng 136:145–163. https://doi.org/10.1016/0045-7825(96)01011-0
Park D, Rilett LR (1999) Forecasting freeway link travel times with a multilayer feedforward neural network. Comput Civ Infrastruct Eng 14:357–367. https://doi.org/10.1111/0885-9507.00154
Rasmussen CE, Williams CKI (2004) Gaussian processes for machine learning. MIT Press, Cambridge, MA
Rasmussen CE, Uk CA, Nickisch H, De HM (2010) Gaussian processes for machine learning (GPML) toolbox. J Mach Learn Res 11:3011–3015. https://doi.org/10.1142/S0129065704001899
Sengupta A, Upadhyay A (2009) Locating the critical failure surface in a slope stability analysis by genetic algorithm. Appl Soft Comput J 9:387–392. https://doi.org/10.1016/j.asoc.2008.04.015
Shi JQ, Wang B, Murray-Smith R, Titterington DM (2007) Gaussian process functional regression modeling for batch data. Biometrics 63:714–723. https://doi.org/10.1111/j.1541-0420.2007.00758.x
Takagi T, Sugeno M (1985) Fuzzy Identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern SMC 15:116–132. https://doi.org/10.1109/tsmc.1985.6313399
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Samui, P., Kumar, R., Yadav, U. et al. Reliability Analysis of Slope Safety Factor by Using GPR and GP. Geotech Geol Eng 37, 2245–2254 (2019). https://doi.org/10.1007/s10706-018-0697-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10706-018-0697-2