Simplified and Rapid Modeling of Road Embankments Slope Safety Factor Using Regularized Regression Techniques
Downloads
The primary objective of this research is to examine the viability of simplified regularized regression models in predicting the slope safety factor of road embankments. The methodology involves developing and comparing several regularized linear regressions against conventional methods. A total of 276 data points are collected from the literature, and 70% of these are utilized for model training, while 30% are employed for testing. The findings indicate that these models yield results better than established approaches, with Stochastic Gradient Descent and Bayesian Ridge achieving strong performances. This study provides an alternative technique that offers rapid and manually solvable equations, thus enhancing practical adaptability for routine professional tasks. The novelty lies in bridging the gap between traditional finite element-based investigations and emerging data-driven methods, demonstrating that regularized regression can be both simple and sufficiently accurate. Overall, the study outcomes emphasize the significance of these advanced yet computationally light models for road embankment stability assessments, presenting a valuable and time-efficient tool for practitioners.
Downloads
[1] Kainthola, A., Verma, D., Thareja, R., & Singh, T. N. (2013). A review on numerical slope stability analysis. International Journal of Science, Engineering and Technology Research, 2(6), 1315-1320.
[2] Dwivedi, N., Chauhan, V. B., Kumar, A., Jaiswal, S., & Singh, T. (2024). Enhancing strip footing bearing capacity on soil slopes with geogrid reinforcement: insights from finite element analysis and machine learning. Innovative Infrastructure Solutions, 9(11), 440. doi:10.1007/s41062-024-01769-y.
[3] Chen, Z.-Y., & Shao, C.-M. (1988). Evaluation of minimum factor of safety in slope stability analysis. Canadian Geotechnical Journal, 25(4), 735–748. doi:10.1139/t88-084.
[4] Donald, I. B., & Chen, Z. (1997). Slope stability analysis by the upper bound approach: Fundamentals and methods. Canadian Geotechnical Journal, 34(6), 853–862. doi:10.1139/t97-061.
[5] 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. doi:10.1007/s00366-015-0400-7.
[6] Abdollahi, A., Li, D., Deng, J., & Amini, A. (2024). An explainable artificial-intelligence-aided safety factor prediction of road embankments. Engineering Applications of Artificial Intelligence, 136, 108854. doi:10.1016/j.engappai.2024.108854.
[7] Gael, C. N., Luc Leroy, M. N., & Christian, F. B. (2025). Research on slope stability assessment methods: a comparative analysis of limit equilibrium, finite element, and analytical approaches for road embankment stabilization. AI in Civil Engineering, 4(1), 2. doi:10.1007/s43503-024-00046-2.
[8] Wang, C., Li, D., & Lai, X. (2024). Evaluation of Deformation Pattern and Safety Stability of Road Embankment on Sloping Foundation. 8th International Conference on Civil Architecture and Structural Engineering (ICCASE 2024), 613–620. doi:10.2991/978-94-6463-449-5_60.
[9] Ashiq, H. M., Sabab, S. R., Joy, J. A., Zahid, C. Z. Bin, & Kabir, M. U. (2024). Analysis of cement-stabilized soil on road embankment employing finite element analysis - a case study. Engineering Research Express, 6(4), 45101. doi:10.1088/2631-8695/ad7d63.
[10] Sharma, A., & Shrivastava, N. (2025). Slope Stability Assessment in Highway Embankments: A Comprehensive Study on Incorporating C&D Waste as Fill Material. Journal of Mining and Environment, 16(1), 57–73. doi:10.22044/jme.2024.14510.2728.
[11] Kong, X., & Doré, G. (2024). Assessment of thermal stability and application of engineering solutions to preserve the degrading road embankment near Hudson Bay Coast, Canada. Cold Regions Science and Technology, 224, 104230. doi:10.1016/j.coldregions.2024.104230.
[12] Gao, R., & Chu, L. (2024). Deformation and stability of soft soil highway subgrade and embankment. In Advances in Measurement Technology and Disaster Prevention, 189–195. doi:10.1201/9781032629506-24.
[13] Rajan, K. C., Aryal, M., Sharma, K., Bhandary, N. P., Pokhrel, R., & Acharya, I. P. (2025). Development of a framework for the prediction of slope stability using machine learning paradigms. Natural Hazards, 121(1), 83–107. doi:10.1007/s11069-024-06819-3.
[14] Kurnaz, T. F., Erden, C., Dağdeviren, U., Demir, A. S., & Kökçam, A. H. (2024). Comparison of machine learning algorithms for slope stability prediction using an automated machine learning approach. Natural Hazards, 120(8), 6991–7014. doi:10.1007/s11069-024-06490-8.
[15] Yadav, D. K., Chattopadhyay, S., Tripathy, D. P., Mishra, P., & Singh, P. (2025). Enhanced slope stability prediction using ensemble machine learning techniques. Scientific Reports, 15(1), 7302. doi:10.1038/s41598-025-90539-6.
[16] Chlahbi, S., Elghali, A., Inabi, O., Belem, T., Zerouali, E., & Benzaazoua, M. (2024). Integrated approach to sustainable utilization of phosphate waste rock in road embankments: Experimental insights, stability analysis, and preliminary economic evaluation. Case Studies in Construction Materials, 20, 3222. doi:10.1016/j.cscm.2024.e03222.
[17] Srivastava, A., Uniyal, K., & Singh, D. (2024). Slope Stability and Reliability Analysis of Earth Embankment Constructed for the Doubling of Railway Track. Indonesian Geotechnical Journal, 3(1), 35–48. doi:10.56144/igj.v3i1.82.
[18] Jacobs, E., & Sinclair, M. (2024). Mathematical evaluation of the safety effects of embankment on curves for roads in the Southern African context. 42nd International Southern African Transport Conference, 8-11 July, 2024, Pretoria, South Africa.
[19] Manzoor, B., Othman, I., Durdyev, S., Ismail, S., & Wahab, M. H. (2021). Influence of artificial intelligence in civil engineering toward sustainable development—a systematic literature review. Applied System Innovation, 4(3), 52. doi:10.3390/asi4030052.
[20] Chakraborty, A., & Goswami, D. (2017). Prediction of slope stability using multiple linear regression (MLR) and artificial neural network (ANN). Arabian Journal of Geosciences, 10(17), 1–11. doi:10.1007/s12517-017-3167-x.
[21] Chakraborty, A., & Goswami, D. D. (2017). Slope Stability Prediction using Artificial Neural Network (ANN). International Journal Of Engineering And Computer Science, 6(6), 21845–21848. doi:10.18535/ijecs/v6i6.49.
[22] Meng, J., Mattsson, H., & Laue, J. (2021). Three-dimensional slope stability predictions using artificial neural networks. International Journal for Numerical and Analytical Methods in Geomechanics, 45(13), 1988–2000. doi:10.1002/nag.3252.
[23] Das, S. K., Biswal, R. K., Sivakugan, N., & Das, B. (2011). Classification of slopes and prediction of factor of safety using differential evolution neural networks. Environmental Earth Sciences, 64(1), 201–210. doi:10.1007/s12665-010-0839-1.
[24] Mesa-Lavista, M., Álvarez-Pérez, J., Tejeda-Piusseaut, E., & Lamas-Fernández, F. (2021). Safety-factor dataset for high embankments determined with different analytical methods. Data in Brief, 38, 107315. doi:10.1016/j.dib.2021.107315.
[25] Morman-Wątor, J., & Pilecka, E. (2024). An Analysis of the Use of Mining Waste from Coal Mines in Flood and Road Embankments. Energies , 17(13), 3303. doi:10.3390/en17133303.
[26] Lu, P., & Rosenbaum, M. S. (2003). Artificial neural networks and grey systems for the prediction of slope stability. Natural Hazards, 30(3), 383–398. doi:10.1023/B:NHAZ.0000007168.00673.27.
[27] Kumar, S., & Basudhar, P. K. (2018). A neural network model for slope stability computations. Geotechnique Letters, 8(2), 149–154. doi:10.1680/jgele.18.00022.
[28] Hoerl, A. E., & Kennard, R. W. (1981). Ridge regression—1980: Advances, algorithms, and applications. American Journal of Mathematical and Management Sciences, 1(1), 5-83. doi:10.1080/01966324.1981.10737061.
[29] Efendi, A. (2017). A simulation study on Bayesian Ridge regression models for several collinearity levels. AIP Conference Proceedings, 1913, 1. doi:10.1063/1.5016665.
[30] Abdalla, J. A., Attom, M. F., & Hawileh, R. (2015). Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network. Environmental Earth Sciences, 73(9), 5463–5477. doi:10.1007/s12665-014-3800-x.
[31] Abellán García, J., Fernández Gómez, J., & Torres Castellanos, N. (2022). Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks. European Journal of Environmental and Civil Engineering, 26(6), 2319–2343. doi:10.1080/19648189.2020.1762749.
[32] Bakhary, N., Hao, H., & Deeks, A. J. (2007). Damage detection using artificial neural network with consideration of uncertainties. Engineering Structures, 29(11), 2806–2815. doi:10.1016/j.engstruct.2007.01.013.
[33] Chakraverty, S., Gupta, P., & Sharma, S. (2010). Neural network-based simulation for response identification of two-storey shear building subject to earthquake motion. Neural Computing and Applications, 19(3), 367–375. doi:10.1007/s00521-009-0279-6.
[34] Movsessian, A., García Cava, D., & Tcherniak, D. (2021). An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade. Mechanical Systems and Signal Processing, 159, 107766. doi:10.1016/j.ymssp.2021.107766.
[35] Nguyen, H. D., Dao, N. D., & Shin, M. (2021). Prediction of seismic drift responses of planar steel moment frames using artificial neural network and extreme gradient boosting. Engineering Structures, 242, 112518. doi:10.1016/j.engstruct.2021.112518.
[36] Zhang, J., Ma, G., Huang, Y., sun, J., Aslani, F., & Nener, B. (2019). Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression. Construction and Building Materials, 210, 713–719. doi:10.1016/j.conbuildmat.2019.03.189.
[37] Xi, X., Yin, Z., Yang, S., & Li, C. Q. (2021). Using artificial neural network to predict the fracture properties of the interfacial transition zone of concrete at the meso-scale. Engineering Fracture Mechanics, 242, 107488. doi:10.1016/j.engfracmech.2020.107488.
[38] Yuan, X., Chen, G., Jiao, P., Li, L., Han, J., & Zhang, H. (2022). A neural network-based multivariate seismic classifier for simultaneous post-earthquake fragility estimation and damage classification. Engineering Structures, 255, 113918. doi:10.1016/j.engstruct.2022.113918.
[39] Xue, J., Shao, J. F., & Burlion, N. (2021). Estimation of constituent properties of concrete materials with an artificial neural network based method. Cement and Concrete Research, 150, 106614. doi:10.1016/j.cemconres.2021.106614.
[40] Tran-Ngoc, H., Khatir, S., Ho-Khac, H., De Roeck, G., Bui-Tien, T., & Abdel Wahab, M. (2021). Efficient Artificial neural networks based on a hybrid metaheuristic optimization algorithm for damage detection in laminated composite structures. Composite Structures, 262, 113339. doi:10.1016/j.compstruct.2020.113339.
- Authors retain all copyrights. It is noticeable that authors will not be forced to sign any copyright transfer agreements.
- This work (including HTML and PDF Files) is licensed under a Creative Commons Attribution 4.0 International License.
