Application of Rough Set Method Based on Modified Efficiency Coefficient in Slope Stability Analysis
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ORIGINAL PAPER
Application of Rough Set Method Based on Modified Efficiency Coefficient in Slope Stability Analysis Chen Qiao
. Chang-hong Li . Yong-gang Xiao
Received: 17 June 2020 / Accepted: 8 August 2020 / Published online: 14 August 2020 Ó Springer Nature Switzerland AG 2020
Abstract In order to evaluate the slope stability objectively and prevent geological disasters, the modified efficiency coefficient method was used to evaluate the slope stability. The main factors influencing slope stability were selected as evaluation indicators, including slope angle, slope height, internal friction angle, cohesion, bulk density and pore pressure ratio. The rough set theory was used to simplify the attributes of the slope stability evaluation indicators and determine the weight coefficient of the important indicators extracted. The weight coefficient was applied to the modified efficiency coefficient method to calculate the total efficiency coefficient value of the sample and evaluate the slope stability grade. The comprehensive evaluation model based on rough set and modified efficiency coefficient method is used to predict the stability of a slope project. The results show that the prediction results of this method are consistent with the actual state of the project, and the accuracy of the evaluation using the comprehensive evaluation model is 90.9%, which verifies the validity and reliability of this method. The combination of rough set theory and modified efficiency coefficient method can effectively improve the accuracy of slope stability evaluation and provide a new idea for slope stability evaluation. C. Qiao (&) C. Li Y. Xiao School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China e-mail: [email protected]
Keywords Slope stability Rough set Weight coefficient Modified efficiency coefficient method Comprehensive evaluation model
1 Introduction Zhao et al. (2011) obtained the data of slope safety state through long-term monitoring, calculated the multi-level indicator catastrophe series of slope stability evaluation system by using catastrophe theory, predicted the slope stability state, and applied it to engineering examples to verify the accuracy of the method. Based on grey system theory and the slope rock mass quality classification method, Liu and Cao (2015) proposed an modified SMR method for the quality evaluation of the slope rock mass. Gelisli et al. (2015) selected 100 slope models for calculation, and compared the safety factors calculated by the neural network method with those calculated by the conventional slope parameter stability calculation method, which verified rapidity and convenience of the artificial neural network method in the slope stability evaluation. Li and Liu (2013) used rough set theory to reduce the prediction indicator, and used the catastrophe series method to predict the slope stability, and verified the feasibility of the method applied to engineering examples
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