Slope Stability Analysis with Geometric Semantic Genetic Programming
Neural and Evolutionary Computing
2017-12-06 v2
Abstract
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.
Keywords
Cite
@article{arxiv.1708.09116,
title = {Slope Stability Analysis with Geometric Semantic Genetic Programming},
author = {Juncai Xu and Zhenzhong Shen and Qingwen Ren and Xin Xie and Zhengyu Yang},
journal= {arXiv preprint arXiv:1708.09116},
year = {2017}
}