Open Access

ARTICLE

A Reliability Evaluation Method for Intermittent Jointed Rock Slope Based on Evolutionary Support Vector Machine

by Shuai Zheng, An-Nan Jiang^*, Kai-Shuai Feng

College of Transportation Engineering, Dalian Maritime University, Dalian, 116026, China

* Corresponding Author: An-Nan Jiang. Email:

Computer Modeling in Engineering & Sciences 2021, 129(1), 149-166. https://doi.org/10.32604/cmes.2021.016761

Received 24 March 2021; Accepted 07 July 2021; Issue published 24 August 2021

Abstract

The randomness of rock joint development is an important factor in the uncertainty of geotechnical engineering stability. In this study, a method is proposed to evaluate the reliability of intermittent jointed rock slope. The least squares support vector machine (LSSVM) evolved by a bacterial foraging optimization algorithm (BFOA) is used to establish a response surface model to express the mapping relationship between the intermittent joint parameters and the slope safety factor. The training samples are obtained from the numerical calculation based on the joint finite element method during this process. Considering the randomness of the intermittent joint parameters in the actual project, each parameter is evaluated at different locations on the site, and its distribution characteristics are counted. According to these statistical results, a large number of parameter combinations are obtained through Monte Carlo sampling. The trained machine learning mapping model is used to obtain the slope safety factor corresponding to each group, and these results are then used to obtain the slope reliability. When the research results were applied to slope disaster treatment along the Yalu River in China’s Jilin Province, it was found that the joint length and joint inclination angle both play key roles in rock slope stability, which should receive more attention in the slope treatment. In summary, this study establishes a method for evaluating the reliability of intermittent jointed rock slope based on an evolutionary SVM model, and its feasibility is verified by engineering application.

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