摘要: Flexible pavements rely heavily on unbound granular materials for their base and subbase layers, where their performance under repetitive loading is critical. This performance is gauged by two main parameters: resilient modulus and permanent deformation, which are influenced by both intrinsic material properties such as gradation, moisture content, and density and external factors such as applied stress and load repetitions. Over time, variations in these elements can lead to diminished strength and increased non-recoverable strain, highlighting the necessity for routine evaluation of the pavement's resilient behavior to maintain its longevity and durability. Given the practical difficulties of frequent field testing, computational models have emerged as vital tools for simulating the resilient modulus and predicting plastic strain, incorporating diverse influencing factors, and calibrated with lab and in-situ data. This paper delves into the properties affecting the resilient behavior of unbound granular pavement materials, emphasizing that stress level and moisture content significantly impact the resilient modulus, while load cycles and stress level notably influence permanent deformation. Central to this study is the exploration of the integrated effect of these factors on resilient behavior. Additionally, it evaluates the current landscape of computational modeling, showcasing the capabilities of the most used models for predicting these parameters through comparative analysis of existing literature. It suggests that to enhance pavement reliability and durability, models must evolve to include predictions on density and gradation for future improvement. This study further identifies key causes of pavement deterioration, helping develop targeted rehabilitation strategies and select accurate models for predicting resilience, ensuring robust pavement design. Furthermore, this review advances the field by merging new insights on the resilience of unbound granular materials with a critical evaluation of computational models. It introduces fresh perspectives and trends, bridging gaps in earlier reviews and paving the way for future research in pavement engineering.