Slope Stability Analysis with Geometric Semantic Genetic Programming

TL;DR

This paper applies geometric semantic genetic programming to slope stability analysis, demonstrating its effectiveness in accurate prediction and safety assessment, which can inform slope safety design.

Contribution

The paper introduces a GSGP-based model for slope stability analysis, showcasing its superior accuracy and efficiency over traditional methods.

Findings

GSGP effectively classifies and predicts slope stability.

The model provides precise safety factor estimations.

Results support GSGP's use in engineering safety assessments.

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.