Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{183859,
        author = {CHETAN ANAND},
        title = {A Comprehensive Review of Deterministic and Probabilistic Approaches for Retaining Wall Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {3},
        pages = {3312-3316},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=183859},
        abstract = {The stability assessment of retaining walls has traditionally relied on deterministic approaches, where soil and structural parameters are considered fixed and safety factors are employed to account for uncertainties. While these methods offer simplicity and ease of application, they often fail to capture the inherent variability in geotechnical and seismic parameters, leading to either over-conservative or under conservative designs. To address this limitation, probabilistic approaches have emerged as a powerful alternative, enabling the explicit treatment of uncertainties associated with soil properties, seismic loading, and structural performance. This review provides a comprehensive synthesis of deterministic and probabilistic methods applied to retaining wall analysis, with a focus on seismic conditions and reliability-based design. Deterministic frameworks, including limit equilibrium and numerical techniques such as finite element analysis, are discussed in relation to their applicability and limitations. In contrast, probabilistic approaches—including Monte Carlo simulation, First-Order and Second-Order Reliability Methods (FORM/SORM), and hybrid machine learning-based techniques such as ANFIS coupled with metaheuristic optimization—are highlighted for their capacity to quantify failure probabilities and improve risk-informed decision making. The review also identifies current research trends, such as the integration of artificial intelligence in probabilistic analysis, and emphasizes the importance of balancing computational efficiency with accuracy. Finally, critical knowledge gaps and potential directions for future research are outlined, particularly in the context of developing practical design frameworks for earthquake-resistant retaining walls. This work aims to serve as a valuable resource for researchers and practitioners seeking a deeper understanding of deterministic and probabilistic approaches in retaining wall engineering.},
        keywords = {Retaining wall, Deterministic Analysis, Probabilistic Analysis, Monte Carlo simulation, Reliability-based design, Seismic reliability, Machine learning (ANFIS / metaheuristic optimization).},
        month = {August},
        }