Slope Stability Analysis with Geometric Semantic Genetic Programming

Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringency and computing efficiency. In this paper, GSGP is adopted for the classificatio...

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Bibliographic Details
Published inarXiv.org
Main Authors Xu, Juncai, Shen, Zhenzhong, Ren, Qingwen, Xie, Xin, Yang, Zhengyu
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 05.12.2017
Subjects
Artificial neural networks
Computing time
Genetic algorithms
Neural networks
Programming
Regression analysis
Safety factors
Semantics
Slope stability
Stability analysis
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Summary:Genetic programming has been widely used in the engineering field. Compared with the conventional genetic programming and artificial neural network, geometric semantic genetic programming (GSGP) is superior in astringency and computing efficiency. In this paper, GSGP is adopted for the classification and regression analysis of a sample dataset. Furthermore, a model for slope stability analysis is established on the basis of geometric semantics. According to the results of the study based on GSGP, the method can analyze slope stability objectively and is highly precise in predicting slope stability and safety factors. Hence, the predicted results can be used as a reference for slope safety design.
ISSN:2331-8422