مشخصات پژوهش

This study reviews soil modeling techniques used in numerical simulations for geotechnical and geoenvironmental engineering applications. It aims to assess the effectiveness of different soil models, highlighting their strengths, limitations, and applicability in various scenarios. By analyzing existing literature, the study seeks to provide insights into selecting appropriate soil models for specific engineering problems, considering factors such as soil behavior, material properties, and computational efficiency. Research Methods: The study employs a systematic literature review approach, analyzing various soil modeling techniques used in numerical simulations. It categorizes soil models based on their complexity, theoretical foundations, and practical applications. The research includes an evaluation of constitutive models, such as linear elastic, nonlinear, and plasticitybased models, focusing on their assumptions, input parameters, and validation through experimental data. Finite element and finite difference methods are reviewed to understand their effectiveness in solving geotechnical problems. Additionally, case studies from previous research are examined to illustrate the performance of different soil models in real-world applications. Findings: The review highlights that while simple elastic models are computationally efficient, they lack accuracy in capturing complex soil behavior. Advanced models, such as elastoplastic and critical state models, provide better predictions but require extensive calibration and computational resources. The study identifies a gap in integrating data-driven and machine-learning approaches with traditional soil models to improve predictive accuracy. Conclusion: The study concludes that selecting an appropriate soil model depends on the specific application, data availability, and computational constraints. A balance between model complexity and practical usability is essential. Future research should focus on hybrid modeling approaches, combining conventional numerical methods with machine learning to enhance the predictive capabilities of soil behavior in geotechnical engineering.