Derivation of site-specific soil-water characteristic curve (SWCC) from extremely sparse experimental data by hierarchical Bayesian method with consideration of geotechnical sites similarity

Soil-water characteristic curves (SWCCs) play a crucial role in understanding soil behavior related to water movement and soil moisture effects, rendering them an essential tool in engineering geology and geotechnical engineering applications. Traditionally, SWCCs are determined through labor-intensive laboratory experiments involving varying levels of suction, a process that can take several months. Moreover, obtaining a high-quality SWCC from numerous measurements becomes particularly challenging when immediate site-specific data are required for design and analysis. This paper introduces a hierarchical Bayesian method for deriving site-specific SWCCs by integrating extremely sparse data (e.g., one or two measurements) for the site of interest with existing data from sites with similar geological and sedimentary characteristics. The SWCC parameters are estimated using a Bayesian framework and Markov chain Monte Carlo simulations. This approach not only enables the derivation of accurate SWCCs but also helps quantify the associated uncertainties. The effectiveness of the proposed method is demonstrated using both numerical and real-world data from different types of loess and unsaturated soils in the unsaturated soil database (UNSODA). The results show that site-specific SWCCs of unsaturated soils can be accurately estimated from sparse measurements by incorporating information from similar sites. This work offers an efficient and reasonably accurate approach for deriving SWCCs of unsaturated soils for geotechnical applications, especially when the number of site-specific SWCC measurement is extremely sparse and limited.