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Indexed by:期刊论文

Date of Publication:2017-11-01

Journal:APPLIED SOFT COMPUTING

Included Journals:Scopus、SCIE、EI

Volume:60

Page Number:387-396

ISSN No.:1568-4946

Key Words:Gaussian process regression; Slope stability; Covariance functions; Factor of safety; Bayesian modeling

Abstract:This paper presents a stability evaluation method for slopes based on Gaussian processes (GPs), which is a popular machine learning technique for nonlinear system modeling. Covariance function is one of the most critical parts in GPs modeling, because it determines the properties of sample functions drawn from the Gaussian process prior. Sixteen covariance functions are tested on several datasets for slope stability evaluation problems.
   Experimental: results show that GPs models can reflect the complex relationship between input and output variables. The obtained results are better or similar to the results obtained by several other existing methods, such as artificial neural networks, support vector machines, etc. The other important attractions of GPs include a simple training process and a predictive distribution of the system output. (C) 2017 Elsevier B.V. All rights reserved.