An Improved Predictive Model for Determining the Permeability Coefficient of Artificial Clayey Soil Based on Double T2 Cut-Offs

2020 
Permeability is one of the most important engineering properties of clayey soil. However, the traditional method for determining the permeability coefficient is time-consuming. To establish a simple and accurate predictive method to obtain the permeability coefficient of artificial clayey soil based on the double cut-off transverse relaxation times (T2 cut-offs) using low-field nuclear magnetic resonance (NMR) technology, eight kinds of artificial clayey soil with different mineralogical compositions were prepared in the laboratory. Evaporation tests at 40°C were carried out on the saturated artificial clayey soil samples in an oven. During the evaporation process, NMR tests were also performed on the artificial clayey soil every hour. The results showed that the evaporation process could be divided into three stages according to different evaporation rates: the constant rate stage (CRS), the falling rate stage (FRS), and the residual stage (RS). The water evaporated in the CRS and FRS was defined as the absolute movable water and the partially movable water, respectively. The water that could not evaporate in the RS was defined as the immovable water. Based on the cumulative signal amplitudes in the T2 spectrum corresponding to different kinds of water, the double T2 cut-offs were defined. On the basis of the double T2 cut-offs and T2 spectrum of the saturated sample, an improved Timur–Coates (TC) model was established. The prediction capability of the improved model was evaluated by finding the determination coefficient (R2), mean absolute error (MAE), and root-mean-square error (RMSE). Compared with the typical TC model, the prediction accuracy of the improved model was much higher. In addition, the relationships between the double T2 cut-offs and fractal dimension (D) of the T2 spectrum of saturated artificial clayey soil were also identified.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    39
    References
    0
    Citations
    NaN
    KQI
    []