Multiple Regression Models for Predicting Stability of Reinforced Soil Slope

TOMAR, RADHA; TUNG, SMITA

doi:10.22044/jme.2024.15233.2915

Document Type : Original Research Paper

Authors

Department of Civil Engineering, GLA University, Mathura, India

https://doi.org/10.22044/jme.2024.15233.2915

Abstract

Slope failures are prevalent issue in the construction sector. Thus the engineers must use appropriate slope stabilization techniques to reduce the risk of human life and property. This work investigates the efficacy of multiple regression analysis in predicting slope stability, specifically focusing on the slopes in the Kullu district, Himachal Pradesh, India. A total of 160 cases with different parameters were analyzed by using the well-known Limit Equilibrium Method (LEM), Morgenstern and Price on PLAXIS LE. Numerical analysis was performed using different nail lengths (6 m, 8 m, 10 m, and 12 m) and nail inclinations (0^°, 5^°, 10^°, 15^°, 20^°, 25^°, 30^°, and 35^°), applied to a homogeneous soil slope with 45^°, 50^°, 60^°, and 70^° inclinations, respectively. The limit equilibrium analysis may not offer predictive capabilities for future scenarios directly. In contrast, Multiple Regressions (MR) can provide predictive insights based on the historical data, allowing for forecasting of stability under different conditions or design scenarios. The utilization of MR provides the coefficients that quantify the influence of each variable on slope stability, enabling a detailed understanding of how each factor contributes. To develop the prediction models using Multiple Regression Analysis (MRA), the factor of safety values obtained by the numerical method were used. The accuracy of this model was evaluated against the conventional LE methods. The results indicate that multiple regression provides a good predictive performance with an R² value equal to 0.774, offering a more nuanced and accurate assessment of slope stability compared to the traditional LE techniques.

Keywords

Main Subjects

Slope Stability

References

[1]. Journal, T. S. W. (2015). Retracted: Slope Stability Analysis Using Limit Equilibrium Method in Nonlinear Criterion. The Scientific World Journal, 2015.

[2]. Oh, W. T., & Vanapalli, S. K. (2010). Influence of rain infiltration on the stability of compacted soil slopes. Computers and Geotechnics, 37(5), 649-657.

[3]. Singh, S., & Shrivastava, A. K. (2017). Effect of soil nailing on stability of slopes. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 1.

[4]. Li, J., Wang, X., Jia, H., Liu, Y., Zhao, Y., Shi, C., & Zhang, F. (2022). Effect of herbaceous plant root density on slope stability in a shallow landslide-prone area. Natural hazards, 112(3), 2337-2360.

[5]. Eberhardt, E. (2003). Rock slope stability analysis–utilization of advanced numerical techniques. Earth and Ocean sciences at UBC, 41.

[6]. Taher, N. R., Gör, M., Aksoy, H. S., & Ahmed, H. (2022). Numerical investigation of the effect of slope angle and height on the stability of a slope composed of sandy soil. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 12(2), 664-675.

[7]. Sahu, G., Singh, A., Kumar, I., & Gupta, D. (2017). Studies on improvement of shear strength of sandy soil using egg shell powder and quarry dust. Int. J. of Eng. Res. & Tech., 6, 440-443.

[8]. Park, S., Lim, H., Tamang, B., Jin, J., Lee, S., Chang, S., & Kim, Y. (2019). A study on the slope failure monitoring of a model slope by the application of a displacement sensor. Journal of Sensors, 2019(1), 7570517.

[9]. Khan, M. I., & Wang, S. (2021). Slope Stability Analysis to Develop Correlations between Different Soil Parameters and Factor of Safety Using Regression Analysis. Polish Journal of Environmental Studies, 30(5).

[10]. Kumar, S. A., & Ilamparuthi, K. (2009). Response of footing on sand slopes. In Indian geotechnical conference (Vol. 77, pp. 622-626).

[11]. Sahoo, S., Manna, B., & Sharma, K. G. (2016). Seismic stability analysis of un-reinforced and reinforced soil slopes. In Geo-China 2016 (pp. 74-81).

[12]. Himanshu, N., & Burman, A. (2019). Seepage and stability analysis of Durgawati earthen dam: a case study. Indian Geotechnical Journal, 49, 70-89.

[13]. Altalhe, E. B., & Abdalftah, H. (2019, March). Study using nails in sand soil: stability, anchored length. In International Conference on Technical Sciences (ICST2019) (Vol. 60, p. 40).

[14]. Rawat, S., & Gupta, A. K. (2016). Analysis of a nailed soil slope using limit equilibrium and finite element methods. International Journal of Geosynthetics and Ground Engineering, 2, 1-23.

[15]. Naderi, E., & Hataf, N. (2014). Model testing and numerical investigation of interference effect of closely spaced ring and circular footings on reinforced sand. Geotextiles and Geomembranes, 42(3), 191-200.

[16]. Burman, A., Acharya, S. P., Sahay, R. R., & Maity, D. (2015). A comparative study of slope stability analysis using traditional limit equilibrium method and finite element method.

[17]. Fan, C. C., & Luo, J. H. (2008). Numerical study on the optimum layout of soil–nailed slopes. Computers and Geotechnics, 35(4), 585-599.

[18]. Tien Bui, D., Moayedi, H., Gör, M., Jaafari, A., & Foong, L. K. (2019). Predicting slope stability failure through machine learning paradigms. ISPRS International Journal of Geo-Information, 8(9), 395.

[19]. Nanehkaran, Y. A., Licai, Z., Chengyong, J., Chen, J., Anwar, S., Azarafza, M., & Derakhshani, R. (2023). Comparative analysis for slope stability by using machine learning methods. Applied Sciences, 13(3), 1555.

[20]. Koushik, P., Shantanu, S., Manojit, S., & Mahesh, S. (2016, November). Stability analysis and design of slope reinforcement techniques for a Himalayan landslide. In Recent Advances in Rock Engineering (RARE 2016) (pp. 97-104). Atlantis Press.

[21]. D. Lei, Y. Zhang, Z. Lu, H. Lin, B. Fang, and Z. Jiang, “applied sciences and Hybrid Machine Learning Approaches,” 2024.

[22]. Chakraborty, A., & Goswami, D. (2017). Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences, 10, 1-11.

[23]. Kainthura, P., & Sharma, N. (2021). Machine learning techniques to predict slope failures in Uttarkashi, Uttarakhand (India). Journal of Scientific & Industrial Research, 80(01), 66-74.

[24]. Komadja, G. C., Pradhan, S. P., Oluwasegun, A. D., Roul, A. R., Stanislas, T. T., Laïbi, R. A., ... & Onwualu, A. P. (2021). Geotechnical and geological investigation of slope stability of a section of road cut debris-slopes along NH-7, Uttarakhand, India. Results in Engineering, 10, 100227.

[25]. Lin, S., Zheng, H., Han, C., Han, B., & Li, W. (2021). Evaluation and prediction of slope stability using machine learning approaches. Frontiers of Structural and Civil Engineering, 15(4), 821-833.

[26]. Suman, S., Khan, S. Z., Das, S. K., & Chand, S. K. (2016). Slope stability analysis using artificial intelligence techniques. Natural Hazards, 84, 727-748.

[27]. Singh, S. K., & Chakravarty, D. (2023). Assessment of slope stability using classification and regression algorithms subjected to internal and external factors. Archives of Mining Sciences, 68(1).

[28]. Samui, P. (2008). Slope stability analysis: a support vector machine approach. Environmental Geology, 56, 255-267.

Journal of Mining and Environment

Multiple Regression Models for Predicting Stability of Reinforced Soil Slope

References

References

Volume 16, Issue 2
April 2025
Pages 569-582

Multiple Regression Models for Predicting Stability of Reinforced Soil Slope

References

References

Volume 16, Issue 2April 2025Pages 569-582

Volume 16, Issue 2
April 2025
Pages 569-582