Slope Stability Analysis with Geometric Semantic Genetic Programming
This work addresses slope safety design for engineering applications, but it is incremental as it applies an existing method (GSGP) to a specific domain problem.
The paper tackled slope stability analysis by applying geometric semantic genetic programming (GSGP) to a sample dataset for classification and regression, resulting in a highly precise model for predicting slope stability and safety factors, with the predicted results serving as a reference for slope safety design.
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.