A modification of the Kozeny–Carman equation based on soil particle size distribution

Predicting the permeability of porous media in saturated and partially saturated conditions is crucial in geoengineering. The Kozeny–Carman equation is a pervasive method used for estimating the saturated hydraulic conductivity of soils. The previously reported modified Kozeny–Carman equation did no...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Arabian journal of geosciences 2022-06, Vol.15 (11), Article 1079
Hauptverfasser: Ye, Yan, Xu, Zengguang, Zhu, Guangchao, Cao, Cheng
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Predicting the permeability of porous media in saturated and partially saturated conditions is crucial in geoengineering. The Kozeny–Carman equation is a pervasive method used for estimating the saturated hydraulic conductivity of soils. The previously reported modified Kozeny–Carman equation did not consider the influence of a wide range of particle size distribution on the saturated hydraulic conductivity. In this study, we quantified the influence of fine particle content on the saturated hydraulic conductivity and introduced the geometrical parameters to the Kozeny–Carman equation, followed by determining the range of empirical constant in the equation by the results of seepage test. Herein, a modified Kozeny–Carman equation, considering the particle size distribution of the particle system, is proposed based on particle gradation and size characteristic parameters including non-uniformity coefficient, curvature coefficient, and effective grain diameter. A laboratory test was performed to measure the saturated hydraulic conductivity values of sand–gravel mixture with different particle size distribution, using one-dimensional seepage equipment. Data from laboratory experiments performed in this study and available data from the literature were finally used to verify this proposed model. The findings of this study demonstrated the higher prediction accuracy of the proposed modified model.
ISSN:1866-7511
1866-7538
DOI:10.1007/s12517-022-10224-0