Prediction of Safety Factor for Slope Designed with Various Limit Equilibrium Methods

Article Preview

Abstract:

The purpose of this study is to predict the stability of slope using adaptive neuro fuzzy inference system (ANFIS). Based on limit equilibrium theory, four different methods of analyses, i.e. Morgenstern-Price, Janbu, Bishop and Ordinary were used to calculate the overall safety factor of various slope designs. Neuro-fuzzy inference system was used to map from a given input to an output. Important parameters such as height of slope (H), unit weight of soil (γ), angle of slope (θ), coefficient of cohesion (c) and internal angle of friction (ф) were used as the input parameters while overall safety factor was the output. ANFIS model to predict the stability of the slopes was generated from the calculated data. Results showed that factors of safety predicted using ANFIS agreed well with factors of safety calculated using Limit Equilibrium Methods (LEM).

You might also be interested in these eBooks

Info:

Periodical:

Key Engineering Materials (Volumes 462-463)

Pages:

611-615

Citation:

Online since:

January 2011

Export:

Price:

[1] A. J. Choobbasti, F. Farrokhzad and A. Barari: Arab Journal Geosciences, (2009), DOI 10. 1007/s12517-009-0035-3.

Google Scholar

[2] J. Edwin and S. Kumanan: ANFIS for prediction of weld bead width in a submerged arc welding process, 66 (2007), p.335.

Google Scholar

[3] J. B. Ritter: Using an Infinite Slope Model to Delineate Areas Susceptible to Translational Sliding in the Cincinnati, OH Area (2004).

Google Scholar

[4] GEO-SLOPE International Ltd, SLOPE/W, Student Edition Workbook, (2002).

Google Scholar

[5] W.A. Lee, S. Thomas, S. Sunil and M. Glenn: Slope stability and stabilization methods, 2 (2002), p.356.

Google Scholar

[6] C. P. Kurian, V. I. George, J. Bhat & R. S Aithal, ANFIS model for the time series prediction of Interior daylight illuminance, AIML, 6 (2006), p.3.

Google Scholar

[7] C.T. Lin, C.S. George Lee: Neural Fuzzy Systems (Prentice – Hall International, Inc. 1996).

Google Scholar

[8] E. Aldrian and Y.S. Djamil: Application of multivariate ANFIS for daily rainfall prediction: influences of training data size, MAKARA, SAINS, 12 (2008), p.7.

DOI: 10.7454/mss.v12i1.320

Google Scholar

[9] GeoStudio. 2004. Reference Manual.

Google Scholar

[10] MATLAB. 2009. Reference Manual.

Google Scholar