Performance Comparison of Different Particle Size Distribution Models in the Prediction of Soil Particle Size Characteristics

Abstract

Particle size distribution (PSD) is a rich source of information about soil properties, including soil gradation and soil particle size characteristics. This paper compared the PSD prediction ability of three types of mathematical model. We selected nine models that have been proven to accurately predict sample points in previous studies, and we fit 144 pieces of experimental data on 12 texture classes of soil samples from the UNSODA database. We compared the models’ capability for predicting non-sample points, which is important for predicting soil particle size characteristics. Each model’s ability to predict non-sample points of different texture classes of soil was studied using a comprehensive ranking method. The relative differences in the models’ prediction of non-sample points of different texture classes of soil were analyzed using the relative error method. The results showed no considerable correlation between the number of model parameters and the prediction accuracy. For the various texture classes of soil, the Skaggs model and Weipeng model had the highest accuracy in predicting non-sample points, and the Skaggs model had the widest range of application. The Zhongling model and the Weibull model were better in predicting only one texture class of soil, respectively. The Fredlund model, Kolve model, Rosin model, Van Genuchten model and Best model were not as successful as other models. The Weipeng model overestimated the solid particle mass proportion, while the Skaggs model underestimated it when the clay particle content was greater than 20%. Both the Weipeng model and the Skaggs model demonstrated good prediction accuracy when the particle size was within the silt particle size range. The Skaggs model overestimated the particle mass proportion, while the Weipeng model underestimated it when the particle size was within the sand particle size range.