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Toward state-of-the-art techniques in predicting and controlling slope stability in open-pit mines based on limit equilibrium analysis, radial basis function neural network, and brainstorm optimization

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Abstract

This study aims to propose state-of-the-art techniques in predicting and controlling slope stability in open-pit mines based on limit equilibrium analysis, artificial neural networks, and optimization algorithms. Accordingly, the simplified Bishop method was used to analyze the slope stability of an open-pit coal mine through the limit equilibrium analysis method. Various rock mass properties and the geometrical parameters of the slopes were considered, such as bench height, slope angle, unit weight, cohesion, and friction angle. Finally, 495 cases were analyzed to compute the factor of safety (FOS). Subsequently, the radial basis function neural network (RBFNN) model was applied to predict FOS. In order to optimize the RBFNN model, the brainstorm optimization (BSO) algorithm was applied to train the RBFNN model, named as BSO-RBFNN model. The genetic algorithm (GA)-RBFNN, RBFNN (without optimization), and multiple layers perceptron (MLP) neural network were also developed to predict FOS and compared with the proposed BSO-RBFNN model as part of the study. The results revealed that the optimization of the BSO algorithm and RBFNN model provided a state-of-the-art technique (i.e., BSO-RBFNN) for predicting and controlling slope stability with high accuracy (i.e., mean absolute error (MAE) = 0.047, root-mean-squared error (RMSE) = 0.057, determination coefficient (R2) = 0.929, variance accounted for (VAF) = 92.948), and reliability (i.e., absolute error of 5.89% for 80% of cases in practice). Comparisons also indicated that the proposed BSO-RBFNN model is the most dominant model for predicting slope stability in this study (i.e., MAEGA-RBFNN = 0.048, RMSEGA-RBFNN = 0.060, R2GA-RBFNN = 0.927, VAFGA-RBFNN = 92.534; MAERBFNN = 0.064, RMSERBFNN = 0.081, R2RBFNN = 0.925, VAFRBFNN = 89.189; MAEMLP = 0.065, RMSEMLP = 0.081, R2MLP = 0.873, VAFMLP = 85.724). Furthermore, the slope angle and bench height should be taken into account to control slope stability in practical engineering based on the proposed BSO-RBFNN model.

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Acknowledgements

This research was supported by the Rearing Fund of HuangHuai University (XKPY-202002), the Key Research Projects of Universities in Henan Province (NO.22B560010). Also, the authors would like to thank the Surface Mining Department of the Hanoi University of Mining and Geology (HUMG), Vietnam; the Center for Mining, Electro-Mechanical Research of HUMG for their kind supports in the expertise areas.

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Authors and Affiliations

  1. Institute of Architecture Engineering, Huanghuai University, Zhumadian, 463000, Henan, China

    Li Shang

  2. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Bac Tu Liem Distr., Hanoi, 100000, Vietnam

    Hoang Nguyen, Xuan-Nam Bui & Le Thi Minh Hanh

  3. Innovations for Sustainable and Responsible Mining (ISRM) Group, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang Nguyen & Xuan-Nam Bui

  4. Department of Geodesy, National University of Civil Engineering, Hanoi, 100000, Vietnam

    Thai Ha Vu

  5. Department of Civil Engineering, Transilvania University of Brasov, 5, Turnului Str, 500152, Brasov, Romania

    Romulus Costache

  6. Institute of Civil Engineering, Warsaw University of Life Sciences (SGGW), Nowoursynowska 166, 02-787, Warszawa, Poland

    Le Thi Minh Hanh

  7. Danube Delta National Institute for Research and Development, 165 Babadag Street, 820112, Tulcea, Romania

    Romulus Costache

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Shang, L., Nguyen, H., Bui, XN. et al. Toward state-of-the-art techniques in predicting and controlling slope stability in open-pit mines based on limit equilibrium analysis, radial basis function neural network, and brainstorm optimization. Acta Geotech. 17, 1295–1314 (2022). https://doi.org/10.1007/s11440-021-01373-9

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