NDLI: Slope stability analysis using artificial intelligence techniques

Content Provider	Springer Nature Link
Author	Suman, Shakti Khan, S. Z. Das, S. K. Chand, S. K.
Copyright Year	2016
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.
Starting Page	727
Ending Page	748
Page Count	22
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	84
Issue Number	2
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-07-29
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Slope stability Factor of safety Artificial intelligence Multigene genetic programming Multivariate adaptive regression splines Functional network Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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