Because of changes in pore structure

the permeability coefficient of soil with different dry densities varies. In the same site

the pore structures of soil at different positions are quite different. In order to obtain the permeability coefficient of soil at different positions

it is necessary to do the falling head permeability test on soil samples from different positions

which is a time-consuming process. Therefore

it is crucial to propose a model for quickly estimating the permeability coefficient of soil with different pore structures. In this paper

a capillary model for rapid prediction of the permeability coefficient of soil using T distribution is developed based on the advantages of nuclear magnetic resonance (NMR) in quickly determining pore size distribution. With the permeability coefficient of a certain dry density soil and the T distribution curves of different dry density soils

the model is able to estimate the permeability coefficient of soil under any dry density condition. The research results show that the capillary model is highly efficient for estimating the permeability coefficient of soil at different dry densities since it does not need to calculate the transverse surface relaxation strength ; The probability of each T time value is the same as that of the corresponding pore volumetric probability

and the permeability coefficient can be calculated directly by substituting the T time into the capillary model. The predicted results of the model are basically consistent with the measured values

indicating that the method is fast

reliable and unaffected by human behaviors.