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.