Bayesian Approach for Predicting Soil-Water Characteristic Curve from Particle-Size Distribution Data

Soil-water characteristic curve (SWCC) is a significant prerequisite for slope stability analysis involving unsaturated soils. However, it is difficult to measure an entire SWCC over a wide suction range using in-situ or laboratory tests. As an alternative, the Arya and Paris (AP) model provides a feasible way to predict SWCC from the routinely available particle-size distribution (PSD) data by introducing a scaling parameter. The accuracy of AP model is generally dependent on the calibrated database which contains test data collected from other sites. How to use the available test data to determine the scaling parameter and to predict the SWCC remains an unresolved problem. This paper develops a Bayesian approach to predict SWCC from PSD. The proposed approach not only determines the scaling parameter, but also identifies fitting parameters of the parametric SWCC model. Finally, the proposed approach is illustrated using real data in Unsaturated Soil Database (UNSODA). Results show that the proposed approach provides a proper prediction of SWCC by making use of the available test data. Additionally, the proposed approach is capable of predicting SWCC in the high suction range, allowing engineers to obtain a complete SWCC in practice with reasonable accuracy.