Permeability of granular layers: a critical review of predictive techniques

Estimating in-plane permeability of the granular subbase layers of the pavement is an essential aspect of pavement engineering, as it significantly influences the effectiveness of subsurface drainage systems. However, obtaining these values through laboratory or in-situ methods is often time-consuming, expensive, and impractical, particularly for large-scale applications. As an alternative, analytical prediction models have become a versatile tool for estimating permeability efficiently. However, these models are formulated based on specific assumptions and are applicable only within a limited range of variance. Unfortunately, this limitation is often overlooked during practical implementation, leading to inaccuracies in permeability estimation. To address this issue, the present study proposes a zonation approach to categorize prediction models according to their applicability for various granular materials, considering their grain size distribution. By focusing on granular pavement layers, the study critically evaluates the performance of existing models, drawing on extensive literature analysis to improve practical relevance and accuracy.