Assessment of Reinforced Slope Stability of Soils using Multiple Regression Models
Keywords:
Slope stability, Coefficient of determination, Mean Square Error, Artificial Intelligence, slopesAbstract
Assessing the stability of slopes holds immense significance in geotechnical engineering. However, the conventional and soft computing methods used for this purpose come with notable challenges. Unlike the black-box nature of soft computing techniques such as artificial intelligence and fuzzy logic, employing traditional limit equilibrium procedures for slope stability assessment is often arduous and time-intensive. In contrast, multiple regression (MR) analysis emerges as a pragmatic alternative for evaluating slope stability. MR offers a simplified equation that can determine the critical factor of slope safety without the need for complex iterative processes. This utilization of MR models streamlines the assessment process, reducing both time and complexity and overall enhancing the evaluation process. In this study, we explored the accuracy of MR models in estimating slope stability using real-world field data. Our dataset comprises six key variables: unit weight, cohesiveness, internal friction angle, slope angle, slope height, and pore water pressure ratio. We constructed multiple regression models to assess their effectiveness in determining slope stability, accounting for both dry and wet slope conditions. The study successfully developed several multiple regression models for both dry and wet slopes. Furthermore, we employed performance metrics like Mean Square Error (MSE) and Coefficient of Determination (R2) to rigorously evaluate and validate the accuracy of these models in comparison to traditional limit equilibrium methods. The performance of the dry slopes R2 is 0.835 and wet slope of R2 value is 0.818.
Downloads
References
He X, Li S J, Liu Y X, Zhou Y P. Analyzing method of rock slope stability based on artificial neural network. Rock and Soil Mechanics, 2003, 24: 73−76.
Ausilio, E., Conte, E., & Dente, G. (2000). Seismic stability analysis of reinforced slopes. Soil Dynamics and Earthquake Engineering, 19(3), 159-172.
Tian, H. M., Li, D. Q., Cao, Z. J., Xu, D. S., & Fu, X. Y. (2021). Reliability-based monitoring sensitivity analysis for reinforced slopes using BUS and subset simulation methods. Engineering Geology, 293, 106331.
Wang, Y., Fu, H., Wan, Y., & Yu, X. (2022). Reliability and parameter sensitivity analysis on geosynthetic-reinforced slope with considering spatially variability of soil properties. Construction and Building Materials, 350, 128806.
Ikeagwuani, C. C., & Nwonu, D. C. (2021). Influence of Dilatancy Behavior on the Numerical Modeling and Prediction of Slope Stability of Stabilized Expansive Soil Slope. Arabian Journal for Science and Engineering, 46(11), 11387-11413.
Li, C., & Espinoza, R. D. (2017). Assessment of reinforced embankment stability over soft soils based on monitoring results. Geosynthetics International, 24(3), 264-279.
Wang, L., Wu, C., Gu, X., Liu, H., Mei, G., & Zhang, W. (2020). Probabilistic stability analysis of earth dam slope under transient seepage using multivariate adaptive regression splines. Bulletin of Engineering Geology and the Environment, 79(6), 2763-2775.
Irfan, M., Safdar, A., Syed, Q., & Nadeem, M. (2012). Isolation and screening of cellulolytic bacteria from soil and optimization of cellulase production and activity. Turkish Journal of Biochemistry/Turk Biyokimya Dergisi, 37(3).
Ellouze, W., Esmaeili Taheri, A., Bainard, L. D., Yang, C., Bazghaleh, N., Navarro-Borrell, A., & Hamel, C. (2014). Soil fungal resources in annual cropping systems and their potential for management. BioMed research international, 2014.
Mojtahedi, S. F. F., Tabatabaee, S., Ghoroqi, M., Soltani Tehrani, M., Gordan, B., & Ghoroqi, M. (2019). A novel probabilistic simulation approach for forecasting the safety factor of slopes: a case study. Engineering with Computers, 35(2), 637-646.
Gordan, B., Jahed Armaghani, D., Hajihassani, M., & Monjezi, M. (2016). Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Engineering with Computers, 32(1), 85-97.
Goh, A. T., Zhang, Y., Zhang, R., Zhang, W., & Xiao, Y. (2017). Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression. Tunnelling and Underground Space Technology, 70, 148-154.
Sah, N. K., Sheorey, P. R., & Upadhyaya, L. N. (1994, February). Maximum likelihood estimation of slope stability. In International journal of rock mechanics and mining sciences & geomechanics abstracts (Vol. 31, No. 1, pp. 47-53). Pergamon.
Erzin, Y., & Cetin, T. (2013). The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions. Computers & Geosciences, 51, 305-313.
Chen, Z. Y., & Dai, Z. H. (2021). Application of group decision-making AHP of confidence index and cloud model for rock slope stability evaluation. Computers & Geosciences, 155, 104836.
Koopialipoor, M., Jahed Armaghani, D., Hedayat, A., Marto, A., & Gordan, B. (2019). Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions. Soft Computing, 23(14), 5913-5929.
Salmasi, F., & Jafari, F. (2019). A simple direct method for prediction of safety factor of homogeneous finite slopes. Geotechnical and Geological Engineering, 37(5), 3949-3959.
Liu, Z., Shao, J., Xu, W., Chen, H., & Zhang, Y. (2014). An extreme learning machine approach for slope stability evaluation and prediction. Natural hazards, 73(2), 787-804.
Marrapu, B. M., & Jakka, R. S. (2017). Assessment of slope stability using multiple regression analysis. Geomech Eng, 13, 237-254.
Malkawi, A. I. H., Hassan, W. F., & Abdulla, F. A. (2000). Uncertainty and reliability analysis applied to slope stability. Structural safety, 22(2), 161-187.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
