Numerical Determination of Hydraulic Conductivity of Aggregate Drainage Layer

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Authors: Tangjie Wang, Longjia Chu, Tien Fang Fwa
Cited by: 0
References: 64

Abstract

In practice, the actual hydraulic conductivity ( k ) of an aggregate drainage layer in a pavement structure is often not known, and is estimated empirically. This leads to estimation errors of unknown magnitude. The main difficulty encountered in practice is that a standard highway laboratory does not have the required facilities for k determination, and road contractors are typically not equipped to conduct in situ k measurements. Other problems include the large size of test cylinders required for either falling- or constant-head tests, the difficulty in laboratory testing of sample preparation to achieve the desired porosity, and, in the field, the interference of underlying materials of lower k . One solution is to derive k theoretically and eliminate the need for experimental measurements. This research proposes a numerical simulation method to theoretically determine k of a given aggregate blend. The study first employs the Laguerre tessellation method to randomly generate a virtual microstructure of an aggregate blend with known gradation and porosity. Next, the lattice Boltzmann method is utilized to calculate k by simulating the process of a falling-head test. The application of the proposed numerical method is demonstrated by comparing the predicted k with experiment-measured values of standard AASHTO aggregate drainage materials. The proposed method is applicable for calculation of k for any aggregate drainage material in the laboratory or field with known gradation and porosity (or density), both of which can be more readily determined by experiment than k .

Fields of Study & Subjects

Soil and Unsaturated Flow Geotechnical and construction materials studies Heat and Mass Transfer in Porous Media

Publication details

Journal / source
Transportation Research Record Journal of the Transportation Research Board
Volume
2680
Issue
9
Pages
737–749
Publication date
17 January 2026
Language
EN