Prediction of Slope Stability Using Artificial Neural Network (Case Study: Noabad, Mazandaran, Iran)
Location
Arlington, Virginia
Date
14 Aug 2008, 4:30pm - 6:00pm
Abstract
The surface of the earth is very rarely flat and so there are slopes nearly everywhere. The loads on slope are due to the self-weight of the soil and to external loads, which may come from foundation at the top and seismic loads. Geotechnical engineers have to pay particular attention to geology, ground water and shear strength of soils in assessing slop stability. Neural networks are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are non-linear. With respect to the above advantages, in this paper, artificial neural networks are developed to predict slope stability in a specified location. Then the results are compared with older analysis methods to check the ANN model’s validity.
Department(s)
Civil, Architectural and Environmental Engineering
Meeting Name
6th Conference of the International Conference on Case Histories in Geotechnical Engineering
Publisher
Missouri University of Science and Technology
Document Version
Final Version
Rights
© 2008 Missouri University of Science and Technology, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Document Type
Article - Conference proceedings
File Type
text
Language
English
Recommended Citation
Farrokhzad, Farzad; JanAliZadeh, Asskar; and Barari, Amin, "Prediction of Slope Stability Using Artificial Neural Network (Case Study: Noabad, Mazandaran, Iran)" (2008). International Conference on Case Histories in Geotechnical Engineering. 41.
https://scholarsmine.mst.edu/icchge/6icchge/session02/41
Prediction of Slope Stability Using Artificial Neural Network (Case Study: Noabad, Mazandaran, Iran)
Arlington, Virginia
The surface of the earth is very rarely flat and so there are slopes nearly everywhere. The loads on slope are due to the self-weight of the soil and to external loads, which may come from foundation at the top and seismic loads. Geotechnical engineers have to pay particular attention to geology, ground water and shear strength of soils in assessing slop stability. Neural networks are very sophisticated modeling techniques, capable of modeling extremely complex functions. In particular, neural networks are non-linear. With respect to the above advantages, in this paper, artificial neural networks are developed to predict slope stability in a specified location. Then the results are compared with older analysis methods to check the ANN model’s validity.
