Slope Stability Analysis using Hybrid BPSO-SVM Machine Learning Techniques
DOI:
https://doi.org/10.20397/2177-6652/2025.v25i5.3129Palavras-chave:
Support Vector Machine, Binary PSO, Slope Stability, Grid Search, Cross ValidationResumo
Objective: This study aims to enhance the forecasting performance of slope stability predictions by applying and comparing the Binary Particle Swarm Optimization (BPSO) coupled with Support Vector Machine (BPSO-SVM) models. The objective is to classify the slope status, turning the problem into a classification task.
Originality/relevance: The BPSO technique is utilized to select relevant features from the dataset, thereby improving the overall effectiveness of the predictive models.
Methodology: The research includes 108 slope stability examples, with the dataset split between 70% training and 30% validation. The dataset comprises seven input parameters: cohesiveness, slope angle, unit weight, angle of internal friction, slope height, pore water pressure coefficient, and factor of safety. To obtain optimal hyper-parameters for the SVM model, Grid Search was exploited. The accuracy of the slope stability predictions given by several models was assessed using receiver operating characteristic (ROC) curves.
Main results: It is concluded that the BPSO-SVM model outperforms the standalone SVM and BPSO models, serving as a robust computational tool capable of accurately predicting slope stability.
Theoretical and methodological contributions: This research contributes to the evolution of the state of the art on the subject, presenting new constructs that are verified and tested. It introduces new constructs in this new frontier of knowledge.
Social and executive contributions: this work demonstrates to organizations the potential and challenges of applying innovative optimization methods in corporate management, resulting in efficiency gains and benefiting both companies and society.
Referências
Duncan, J. M. (1996). State of the art: limit equilibrium and finite-element analysis of slopes. Journal of Geotechnical engineering, 122(7), 577-596.https://doi.org/10.1061/(ASCE)0733-9410(1996)122:7(577)
Luo, H., Zeng, C., He, X., Li, W., & Li, Q. (2020, October). Analysis of the Finite Element Strength Reduction Method to Slope Stability with a thin weak layer. In IOP Conference Series: Earth and Environmental Science (Vol. 570, No. 6, p. 062014). https://doi.org/10.1088/1755-1315/570/6/062014
Sungkar, M., Munirwansyah, M., Munirwan, R. P., & Safrina, D. (2020, September). Slope stability analysis using Bishop and finite element methods. In IOP conference series: materials science and engineering (Vol. 933, No. 1, p. 012035). IOP Publishing.
Huang, Y. H. (2014, February). Slope stability analysis by the limit equilibrium method: Fundamentals and methods. American Society of Civil Engineers. https://doi.org/10.1061/9780784412886
Mebrahtu, T. K., Heinze, T., Wohnlich, S., & Alber, M. (2022). Slope stability analysis of deep-seated landslides using limit equilibrium and finite element methods in Debre Sina area, Ethiopia. Bulletin of Engineering Geology and the Environment, 81(10), 403.https://doi.org/10.1007/s10064-022-02906-6
Zhou, Y., Zou, P., Wang, F., Liu, Z., Hu, W., & Ma, Z. (2020). Study on high and steep slope stability and slope angle optimization of open-pit based on limit equilibrium and numerical simulation. Geotechnical and Geological Engineering, 38(6), 5737-5753.https://doi.org/10.1007/s10706-020-01390-7
Chen, X., Ren, J., & Liu, J. (2017). Analysis of slope stability and software development based on single-grid and two-grid finite element methods. Geotechnical and Geological Engineering, 35, 1369-1382.https://doi.org/10.1007/s10706-017-0182-3
Vieira, C. S., Lopes, M. D. L., & Caldeira, L. M. (2013). Limit equilibrium analyses for internal design of geosynthetic reinforced slopes: influence of potential failure surface and strength distribution. Geotechnical and Geological Engineering, 31, 1123-1135.https://doi.org/10.1007/s10706-013-9639-1
Rathod, G. W., & Rao, K. S. (2012). Finite element and reliability analyses for slope stability of Subansiri Lower Hydroelectric Project: a case study. Geotechnical and Geological Engineering, 30, 233-252.https://doi.org/10.1007/s10706-011-9465-2
Bhandary, R. P., Krishnamoorthy, A., & Rao, A. U. (2019). Stability analysis of slopes using finite element method and genetic algorithm. Geotechnical and Geological Engineering, 37, 1877-1889.https://doi.org/10.1007/s10706-018-0730-5
Sun, J., Li, J., & Liu, Q. (2008). Search for critical slip surface in slope stability analysis by spline-based GA method. Journal of geotechnical and geoenvironmental engineering, 134(2), 252-256.https://doi.org/10.1061/(ASCE)1090-0241(2008)134:2(252)
Zhang, Q., Wang, Z., & Xia, X. (2012). Interface stress element method and its application in analysis of anti-sliding stability of gravity dam. Science China Technological Sciences, 55, 3285-3291.https://doi.org/10.1007/s11431-012-5059-3
Liang, S., Ren, X., & Li, J. (2013). A random medium model for simulation of concrete failure. Science China Technological Sciences, 56, 1273-1281.https://doi.org/10.1007/s11431-013-5200-y
Rong, G., Huang, K., Zhou, C., Wang, X., & Peng, J. (2012). A new constitutive law for the nonlinear normal deformation of rock joints under normal load. Science China Technological Sciences, 55, 555-567.https://doi.org/10.1007/s11431-011-4654-z
Hammouri, N. A., Malkawi, A. I. H., & Yamin, M. M. (2008). Stability analysis of slopes using the finite element method and limiting equilibrium approach. Bulletin of Engineering Geology and the Environment, 67, 471-478.https://doi.org/10.1007/s10064-008-0156-z
Arvin, M. R., Zakeri, A., & Bahmani Shoorijeh, M. (2019). Using finite element strength reduction method for stability analysis of geocell-reinforced slopes. Geotechnical and Geological Engineering, 37, 1453-1467.https://doi.org/10.1007/s10706-018-0699-0
Liu, F. (2020). Stability analysis of geotechnical slope based on strength reduction method. Geotechnical and Geological Engineering, 38(4), 3653-3665.https://doi.org/10.1007/s10706-020-01243-3
Chen, X., Ren, J., Wang, D., Lyu, Y., & Zhang, H. (2019). A generalized strength reduction concept and its applications to geotechnical stability analysis. Geotechnical and Geological Engineering, 37, 2409-2424.https://doi.org/10.1007/s10706-018-00765-1
Feizizadeh, B., Roodposhti, M. S., Blaschke, T., & Aryal, J. (2017). Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arabian Journal of Geosciences, 10, 1-13.https://doi.org/10.1007/s12517-017-2918-z
Hong, H., Pradhan, B., Bui, D. T., Xu, C., Youssef, A. M., & Chen, W. (2017). Comparison of four kernel functions used in support vector machines for landslide susceptibility mapping: a case study at Suichuan area (China). Geomatics, Natural Hazards and Risk, 8(2), 544-569.https://doi.org/10.1080/19475705.2016.1250112
Zhao, S., & Zhao, Z. (2021). A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO‐SVM Models Based on Grid and Slope Units. Mathematical problems in Engineering, 2021(1), 8854606.https://doi.org/10.1155/2021/8854606
Ballabio, C., & Sterlacchini, S. (2012). Support vector machines for landslide susceptibility mapping: the Staffora River Basin case study, Italy. Mathematical geosciences, 44, 47-70.https://doi.org/10.1007/s11004-011-9379-9
Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2002). Predicting settlement of shallow foundations using neural networks. Journal of geotechnical and geoenvironmental engineering, 128(9), 785-793.https://doi.org/10.1061/(ASCE)1090-0241(2002)128:9(785)
Shahin, M. A., Maier, H. R., & Jaksa, M. B. (2003). Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models. Computers and Geotechnics, 30(8), 637-647.https://doi.org/10.1016/j.compgeo.2003.09.004
Sharma, S., Venkateswarlu, H., & Hegde, A. (2019). Application of machine learning techniques for predicting the dynamic response of geogrid reinforced foundation beds. Geotechnical and Geological Engineering, 37, 4845-4864.https://doi.org/10.1007/s10706-019-00945-7
Kumar, M., & Samui, P. (2019). Reliability analysis of pile foundation using ELM and MARS. Geotechnical and geological engineering, 37, 3447-3457.https://doi.org/10.1007/s10706-018-00777-x
Samui, P. (2012). Application of relevance vector machine for prediction of ultimate capacity of driven piles in cohesionless soils. Geotechnical and Geological Engineering, 30, 1261-1270.https://doi.org/10.1007/s10706-012-9539-9
Samui, P. (2011). Prediction of pile bearing capacity using support vector machine. International Journal of Geotechnical Engineering, 5(1), 95-102.https://doi.org/10.3328/IJGE.2011.05.01.95-102
Lee, I. M., & Lee, J. H. (1996). Prediction of pile bearing capacity using artificial neural networks. Computers and geotechnics, 18(3), 189-200.https://doi.org/10.1016/0266-352X(95)00027-8
Besalatpour, A., Hajabbasi, M. A., Ayoubi, S., Gharipour, A., & Jazi, A. Y. (2012). Prediction of soil physical properties by optimized support vector machines. International Agrophysics, 26(2).
Suman, S., Mahamaya, M., & Das, S. K. (2016). Prediction of maximum dry density and unconfined compressive strength of cement stabilised soil using artificial intelligence techniques. International Journal of Geosynthetics and Ground Engineering, 2, 1-11.https://doi.org/10.1007/s40891-016-0051-9
Rashid, H. M. A., Sufyan, M., Ismail, A., & Waqas, U. (2023). Experimental Characterization-Based Machine Learning Modeling for the Estimation of Geotechnical Properties of Clay Liners. Geotechnical and Geological Engineering, 41(2), 1503-1519.https://doi.org/10.1007/s10706-022-02350-z
Liu, Y., Wang, H., Zhang, H., & Liber, K. (2016). A comprehensive support vector machine-based classification model for soil quality assessment. Soil and Tillage Research, 155, 19-26.https://doi.org/10.1016/j.still.2015.07.006
Talamkhani, S., Naeini, S. A., & Ardakani, A. (2023). Prediction of static liquefaction susceptibility of sands containing plastic fines using machine learning techniques. Geotechnical and Geological Engineering, 41(5), 3057-3074.https://doi.org/10.1007/s10706-023-02444-2
Lee, C. Y., & Chern, S. G. (2013). Application of a support vector machine for liquefaction assessment. Journal of Marine Science and Technology, 21(3), 10.
Hanandeh, S. M., Al-Bodour, W. A., & Hajij, M. M. (2022). A comparative study of soil liquefaction assessment using machine learning models. Geotechnical and Geological Engineering, 40(9), 4721-4734.https://doi.org/10.1007/s10706-022-02180-z
Kumar, D., Samui, P., Kim, D., & Singh, A. (2021). A novel methodology to classify soil liquefaction using deep learning. Geotechnical and Geological Engineering, 39, 1049-1058.https://doi.org/10.1007/s10706-020-01544-7
Qin, W., & Fan, G. (2023). Estimation and predicting of soil water characteristic curve using the support vector machine method. Earth Science Informatics, 16(1), 1061-1072.https://doi.org/10.1007/s12145-022-00909-1
Zhang, Y., Qiu, J., Zhang, Y., & Xie, Y. (2021). The adoption of a support vector machine optimized by GWO to the prediction of soil liquefaction. Environmental Earth Sciences, 80, 1-9.https://doi.org/10.1007/s12665-021-09648-w
Rahbarzare, A., & Azadi, M. (2019). Improving prediction of soil liquefaction using hybrid optimization algorithms and a fuzzy support vector machine. Bulletin of Engineering Geology and the Environment, 78(7), 4977-4987.
Yang, R., & Yuan, S. (2022). Monitoring and prediction of highway foundation settlement based on particle swarm optimization and support vector machine. Mathematical Problems in Engineering, 2022(1), 2754965.https://doi.org/10.1155/2022/2754965
Kalatehjari, R., A Rashid, A. S., Ali, N., & Hajihassani, M. (2014). The Contribution of Particle Swarm Optimization to Three‐Dimensional Slope Stability Analysis. The Scientific World Journal, 2014(1), 973093.https://doi.org/10.1155/2014/973093
Sadoghi Yazdi, J., Kalantary, F., & Sadoghi Yazdi, H. (2012). Calibration of soil model parameters using particle swarm optimization. International Journal of Geomechanics, 12(3), 229-238.https://doi.org/10.1061/(ASCE)GM.1943-5622.0000142
Zou, J., Chen, H., Jiang, Y., Zhang, W., & Liu, A. (2023). An effective method for real-time estimation of slope stability with numerical back analysis based on particle swarm optimization. Applied Rheology, 33(1), 20220143.https://doi.org/10.1515/arh-2022-0143
Khajehzadeh, M., Taha, M. R., El-Shafie, A., & Eslami, M. (2014). Stability assessment of earth slope using modified particle swarm optimization. Journal of the Chinese Institute of Engineers, 37(1), 79-87.https://doi.org/10.1080/02533839.2012.757041
Pham, B. T., Qi, C., Ho, L. S., Nguyen-Thoi, T., Al-Ansari, N., Nguyen, M. D., Nguyen, H. D., Ly, H. B., Le, H. V., & Prakash, I. (2020). A novel hybrid soft computing model using random forest and particle swarm optimization for estimation of undrained shear strength of soil. Sustainability, 12(6), 2218.https://doi.org/10.3390/su12062218
Xue, X., Yang, X., & Chen, X. (2014). Application of a support vector machine for prediction of slope stability. Science China Technological Sciences, 57, 2379-2386.https://doi.org/10.1007/s11431-014-5699-6
Xue, X. (2017). Prediction of slope stability based on hybrid PSO and LSSVM. Journal of Computing in Civil Engineering, 31(1), 04016041.https://doi.org/10.1061/(ASCE)CP.1943-5487.0000607
Liu, Z., Shao, J., Xu, W., & Xu, F. (2014). Comprehensive stability evaluation of rock slope using the cloud model-based approach. Rock Mechanics and Rock Engineering, 47, 2239-2252.https://doi.org/10.1007/s00603-013-0507-3
Li, S., Zhao, H. B., & Ru, Z. (2013). Slope reliability analysis by updated support vector machine and Monte Carlo simulation. Natural Hazards, 65, 707-722.https://doi.org/10.1007/s11069-012-0396-x
Cortes, C. (1995). Support-Vector Networks. Machine Learning.https://doi.org/10.1007/BF00994018
Vapnik, V. N., & Chervonenkis, A. Y. (1964). On a perceptron class. Avtomatika i Telemkhanika, 25(1), 112-120.
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 144-152).https://doi.org/10.1145/130385.130401
Platt, J. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3), 61-74.
Joachims, T. (2006, August). Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 217-226).https://doi.org/10.1145/1150402.1150429
Khanesar, M. A., Teshnehlab, M., & Shoorehdeli, M. A. (2007, June). A novel binary particle swarm optimization. In 2007 Mediterranean conference on control & automation (pp. 1-6). IEEE.
Manouchehrian, A., Gholamnejad, J., & Sharifzadeh, M. (2014). Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environmental Earth Sciences, 71, 1267-1277.https://doi.org/10.1007/s12665-013-25318.
Downloads
Publicado
Como Citar
Edição
Seção
Licença
Copyright (c) 2025 Revista Gestão & Tecnologia
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial 4.0 International License.
Os direitos, inclusive os de tradução, são reservados. É permitido citar parte de artigos sem autorização prévia desde que seja identificada a fonte. A reprodução total de artigos é proibida. Em caso de dúvidas, consulte o Editor.
