Slope stability analysis using artificial intelligence techniques

Shakti Suman (), S. Z. Khan (), S. K. Das () and S. K. Chand ()
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Shakti Suman: National Institute of Technology Rourkela
S. Z. Khan: BPUT
S. K. Das: National Institute of Technology Rourkela
S. K. Chand: IGIT

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 84, issue 2, No 1, 727-748

Abstract: Abstract Natural and man-made soil slope failures are complex phenomena and cause serious hazards in many countries of the world. As a result, public and private property is damaged worth millions of dollars. It is necessary to understand the processes causing failure of slopes and prediction of its vulnerability for proper mitigation of slope failure hazards. Various attempts have been made to predict the stability of slope using both conventional methods such as limit equilibrium method, finite element method, finite difference method and statistical methods. Artificial intelligence (AI) methods like artificial neural networks, genetic programming and genetic algorithms, and support vector machines are found to have better efficiency compared to statistical methods. The present study is an attempt to use recently developed AI methods such as functional networks (FNs), multivariate adaptive regression splines (MARS), and multigene genetic programming (MGGP) to predict factor of safety of slope using slope stability data available in the literature. Prediction model equations are also provided, which can be used to predict the factor of safety of a slope. The performances of these AI techniques have been evaluated in terms of different statistical parameters such as average absolute error, maximum absolute error, root mean square error, correlation coefficient, and Nash–Sutcliff coefficient of efficiency. Of the available models, MARS model had better prediction performance in comparison with MGGP and FN models in terms of the above statistical criteria.

Keywords: Slope stability; Factor of safety; Artificial intelligence; Multigene genetic programming; Multivariate adaptive regression splines; Functional network (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-016-2454-2

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