Assessment of slope stability using classification and regression algorithms subjected to internal and external factors

• Autorzy: Singh Sudhir Kumar , Chakravarty Debashish

Identyfikatory

DOI

10.24425/ams.2023.144319

• Języki publikacji: EN

Abstrakty

EN

This study aims at developing a machine learning based classification and regression-based models for slope stability analysis. 1140 different cases have been analysed using the Morgenstern price method in GeoSlope for non-homogeneous cohesive slopes as input for classification and regression-based models. Slope failures presents a serious challenge across many countries of the world. Understanding the various factors responsible for slope failure is very crucial in mitigating this problem. Therefore, different parameters which may be responsible for failure of slope are considered in this study. 9 different parameters (cohesion, specific gravity, slope angle, thickness of layers, internal angle of friction, saturation condition, wind and rain, blasting conditions and cloud burst conditions) have been identified for the purpose of this study including internal, external and factors representing the geometry of the slope has been included. Four different classification algorithms namely Random Forest, logistic regression, Support Vector Machine (SVM), and K Nearest Neighbor (KNN) has been modelled and their performances have been evaluated on several performance metrics. A similar comparison based on performance indices has been made among three different regression models Decision tree, random forest, and XGBoost regression.

Słowa kluczowe

EN

slope stability; non-homogeneous; classification; regression

PL

stabilność zbocza; klasyfikacja; regresja

• Wydawca: Instytut Mechaniki Górotworu PAN
• Czasopismo: Archives of Mining Sciences
• Rocznik: 2023
• Tom: Vol. 68, no. 1
• Strony: 87--102
• Opis fizyczny: Bibliogr. 22 poz., rys., tab., wykr.

Twórcy

autor

Singh Sudhir Kumar

• Indian Institute of Technology, Kharagpur, India

• https://orcid.org/0000-0002-9832-1683

autor

Chakravarty Debashish

• Indian Institute of Technology, Kharagpur, India

• https://orcid.org/0000-0002-1247-8963

Bibliografia

• [1] Y.H. Chok, et al., Neural network prediction of the reliability of heterogeneous cohesive slopes. International Journal for Numerical and Analytical Methods in Geomechanics 40, 11, 1556-1569 (2016). DOI: https://doi.org/10.1002/nag.2496.
• [2] B. Gordan, et al., Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers 32, 1, 85-97 (2016). DOI: https://doi.org/10.1007/s00366-015-0400-7.
• [3] S.K. Das, et al., Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences 64, 1, 201-210 (2011). DOI: https://doi.org/10.1007/s12665-010-0839-1.
• [4] N.K. Sah, P.R. Sheorey, L.N. Upadhyaya, Maximum likelihood estimation of slope stability. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 31, 1, 47-53 (1994). DOI: https://doi.org/10.1016/0148-9062(94)92314-0.
• [5] A.J. Choobbasti, F. Farrokhzad, A. Barari, Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran). Arabian Journal of Geosciences 2, 4, 311-319(2009). DOI: https://doi.org/10.1007/s12517-009-0035-3.
• [6] F. Kang, et al., System probabilistic stability analysis of soil slopes using Gaussian process regression with Latin hypercube sampling. Computers and Geotechnics 63, 13-25 (2015). DOI: https://doi.org/10.1016/j.compgeo.2014.08.010.
• [7] Y. Erzin, T. Cetin, The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Computers & Geosciences 51, 305-313 (2013). DOI: https://doi.org/10.1016/j.cageo.2012.09.003.
• [8] S.K. Sarma, Stability analysis of embankments and slopes. Geotechnique 23, 3, 423-433 (1973). DOI: https://doi.org/10.1680/geot.1973.23.3.423.
• [9] S.K. Sarma, Stability analysis of embankments and slopes. Journal of the Geotechnical Engineering Division 105, 12, 1511-1524 (1979).
• [10] R. Baker, A relation between safety factors with respect to strength and height of slopes. Computers and Geotechnics 33, 4-5, 275-277 (2006). DOI: https://doi.org/10.1016/j.compgeo.2006.07.001.
• [11] Z. Shangguan, S. Li, M. Luan, Electron. Intelligent forecasting method for slope stability estimation by using probabilistic neural networks, J. Geotech. Eng. Bundle 13 (2009).
• [12] B.H. Zhao, Z.S. Zou, Z.L. Ru, Chaotic particle swarm optimization for non-circular critical slip surface identification in slope stability analysis. Boundaries of Rock Mechanics 605-608, 2008 CRC Press.
• [13] Z. Qian, Slope stability assessments considering material inhomogeneity. PhD Thesis, Deakin University, 2018.
• [14] A. McQuillan, A risk-based slope stability assessment methodology (SSAM) for excavated coal mine slopes. PhDThesis, University of New South Wales, 2019.
• [15] M.A. Shahin, M.B. Jaksa, H.R. Maier, Artificial neural network applications in geotechnical engineering. Australian Geomechanics 36, 1, 49-62 (2001).
• [16] Y. Erzin, T. Cetin, The use of neural networks for the prediction of the critical factor of safety of an artificial slope subjected to earthquake forces. Scientia Iranica 19, 2, 188-194 (2012). DOI: https://doi.org/10.1016/j.scient.2012.02.008.
• [17] S.E. Cho, Probabilistic stability analyses of slopes using the ANN-based response surface. Computers and Geotechnics 36, 5, 787-797 (2009). DOI: https://doi.org/10.1016/j.compgeo.2009.01.003.
• [18] D. Tien Bui, et al., Predicting slope stability failure through machine learning paradigms. ISPRS International Journal of Geo-Information 8, 9, 395 (2019). DOI: https://doi.org/10.3390/ijgi8090395 [19] N.R. Morgenstern, V.E. Price, The analysis stability of general slip surfaces, Geotechnique 15, 1, 79-93 (1965). DOI: https://doi.org/10.1680/geot.1965.15.1.79.
• [20] M.R. Saharan, D.M. Surana, S.K. Parihar, B.R. Bishnoiand, M.A. Saharan, Factor of Safety (FoS) based Slope Design Acceptance Criterion: A Case Study. Annual Technical Volume of Mining Engineering Division Board 2(2020).
• [21] B.M. Adams, Slope stability acceptance criteria for opencast mine design, 12th ANZ Conference on Geomechanics and Human Influence. Wellington, New Zealand (2015).
• [22] A.K. Agarwal, Fundamentals of Slope Stability. Annual Technical Volume of Mining Engineering Division Board 2 (2020).

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)