An ANN-RBF method to predict shear strength parameters of slope rock mass

Author

Zhang Zhi-zeng ; Zhang Jin-hu ; Hou Dong-qi ; Cheng Xiao-peng

Author_Institution

Sch. of Civil Eng. & Archit., Zhongyuan Univ. of Technol., Zhengzhou, China

Volume

1

fYear

2011

fDate

26-28 July 2011

Firstpage

540

Lastpage

543

Abstract

The selection of shear strength parameters is significant in the analysis of slope stability. In order to improve the reliability of numerical analysis, RBF neural network model is established to predict shear strength parameters of slope rock mass and a lot of engineering data are collected to train and examine the model. The shear strength parameters of a certain slope rock mass are predicted by the model, and the result is close to the in-situ shear tests. It indicates that the model is both reasonable and convenient.

Keywords

civil engineering computing; geotechnical engineering; numerical analysis; radial basis function networks; rocks; shear strength; ANN-RBF method; RBF neural network; engineering data; insitu shear test; numerical analysis reliability; shear strength parameter prediction; slope rock mass; slope stability; Artificial neural networks; Biological neural networks; Neurons; Radial basis function networks; Rocks; Stability analysis; Training; RBF neural network; back analysis; rock mechanics; shear strength parameters; slope stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2011 Seventh International Conference on

Conference_Location

Shanghai

ISSN

2157-9555

Print_ISBN

978-1-4244-9950-2

Type

conf

DOI

10.1109/ICNC.2011.6021903

Filename

6021903