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- Title
A Novel Multi-Objective Particle Swarm Optimized RF-SVR Model for Reinforced Soil Slope Stability Analysis.
- Authors
Saikrishnamacharyulu, Ippili; Marrapu, Balendra Mouli; Madhavarao, Vasala
- Abstract
In geotechnical engineering, the stability analysis of reinforced soil slopes is important to ensure the safety and longevity of infrastructures. In this study, a novel multi-objective particle swarm optimized RF-SVR (random forest and support vector regression) model aimed to evaluate the stability of reinforced soil slopes. The proposed Hybrid RF-SVR-MOPSOA model combines the advantages of both machine learning techniques and optimization algorithms and offers enhanced predictive accuracy and efficiency. Assessing the model's effectiveness involves proposed model was compared with three traditional regression models: Elastic net regression (ENR), ridge regression (RR), and lasso regression (LR). Various performance assessment parameters and ROC curve plots were employed to determine the most suitable model for reinforced soil slope stability analysis. The findings indicate that the proposed hybrid RF-SVR-MOPSOA model demonstrates superior performance compared to other traditional regression models. This innovative approach significantly expands the possibilities for enhancing the assessment of slope stability and ensuring safer and more resilient infrastructural development.
- Subjects
REINFORCED soils; SLOPE stability; OPTIMIZATION algorithms; SOIL testing; RANDOM forest algorithms; ALUMINUM composites
- Publication
International Journal of Intelligent Engineering & Systems, 2024, Vol 17, Issue 1, p732
- ISSN
2185-310X
- Publication type
Article
- DOI
10.22266/ijies2024.0229.62