Searching for critical failure surface in slope stability analysis by using hybrid genetic algorithm

Li, Shouju; Shangguan, Zichang; Duan, Hongxia; Liu, Yingxi; Mao-tian, Luan

doi:10.12989/gae.2009.1.1.085

Cited by 8 publications

(1 citation statement)

References 25 publications

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“…This is regarded as among the most significant benefits of the standard soft computing methods used in today's world. In the literature on slope stability, successful implementations of these soft computing approaches may be identified (Wang et al, 2005;Choobbasti et al, 2009;Li et al, 2009;Chakraborty and Goswami, 2017;Kumar and Basudhar, 2018;Qian et al, 2019;Ahour et al, 2020;Li et al, 2020;Ray et al, 2020;Zheng et al, 2020;Che Mamat et al, 2021;Palazzolo et al, 2021); (Das et al, 2011;Erzin and Cetin, 2012;Erzin and Cetin, 2014;Abdalla et al, 2015;Ai and Zsaki, 2017;Chakraborty and Goswami, 2018;Rukhaiyar et al, 2018;Moayedi et al, 2019;Bui et al, 2020;Chen et al, 2020;He et al, 2020;Liao and Liao, 2020;Che Mamat et al, 2020;Markovic Brankovic et al, 2021;Meng et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation Circular Failure of Soil Slopes Using Classification and Predictive Gene Expression Programming Schemes

Hanandeh

2022

Front. Built Environ.

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This article presents an analysis of a dataset related to slope stability circular failure cases using the GeneXproTools program. Two models were developed: The first model was utilized to find a classification model that shows the stability status (S), and the second model was utilized to estimate the factor of safety and predict the mathematical model to serve as an indicator of slope stability. The proposed models include cohesion, unit weight, slope angle, coefficient of pore water pressure, internal friction angle, slope height, internal friction angle, and safety factor. The results showed that the values of accuracy and error were 93.1 and 6.9%, respectively, for classification model 1. The relationship between the actual values of the factor of safety and the output of model 2 was an R2 value of 0.96. The models developed were subjected to sensitivity analysis to determine the input variables’ impact on output for the two models. The selected slope locations with different material characterizations were introduced into geox5 software, and the results were compared to those of the two developed models. Furthermore, when the proposed models were compared to the available models in the literature, the results showed that the proposed models in this study performed better than the available models.

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