Civil and Geoengineering Letters Editorial Office info@jcivilgeo.com mEDRA 20260525 DOI Registration 01

06 10.22034/CGEL.3.1.e100049 https://jcivilgeo.com/article/e100049 Civil and Geoengineering Letters mEDRA 01 01 3115-8129 Civil and Geoengineering Letters Editorial Office IR 01 3115-8129 JB 3 1 01 202605 1 1 A01 Parviz Anari Anari, Parviz Parviz Anari 2 A01 Alireza Piri Bonab Piri Bonab, Alireza Alireza Piri Bonab 01 eng 202605 03 00 This study presents a comprehensive risk analysis of ground-anchor-reinforced slopes using a Mamdani-type fuzzy inference system implemented in MATLAB. The objective is to effectively capture the inherent uncertainties and nonlinear behaviors associated with soil properties, anchor configurations, and slope geometry parameters that traditional deterministic methods often fail to represent adequately. A systematic literature review of over 60 peer-reviewed papers was conducted, focusing on slope stabilization techniques in sandy-clayey soils. From this, a database of 150 stabilization scenarios was compiled, covering a wide range of geotechnical conditions and reinforcement schemes. Key parameters such as slope angle, internal friction angle, cohesion, anchor length and position, and initial factor of safety were extracted and normalized for use in the fuzzy model. The Mamdani fuzzy system was designed with multiple inputs and a single output (risk level), integrating expert knowledge with data-driven insights derived from the compiled database. The fuzzy rules were developed through iterative calibration and validated using sensitivity analysis and cross-validation techniques. The model was implemented and tested in MATLAB. Results demonstrate that the fuzzy-based approach can effectively identify high-risk zones and provide a more nuanced understanding of slope behavior under reinforced conditions. Compared to conventional analysis methods, the proposed system offers improved interpretability and adaptability, making it a valuable decision-support tool during preliminary design and optimization phases. The model’s predictions showed strong agreement with documented outcomes from field case studies, confirming its reliability and practical relevance. 20 Slope stability; Earth slopes; Fuzzy logic; MATLAB 01 00 20251219 02 00 20260227 2026 Alireza Piri Bonab