Probabilistic characterization of the soil-water retention curve and hydraulic conductivity and its application to slope reliability analysis

Soil-water retention curve (SWRC) and hydraulic conductivity are two indispensable inputs in unsaturated seepage analysis for analyzing water flow in unsaturated soils, which can be obtained through direct measurements. Generally, the test data of the SWRC and unsaturated hydraulic conductivity obtained from direct measurements are limited because such measurements are time-consuming and costly. Thus, it is hard to determine an SWRC and hydraulic conductivity with certainty from a probabilistic perspective. How to quantify the uncertainty in them remains an unresolved question. This paper aims to propose a Bayesian approach to characterize the SWRC and hydraulic conductivity. The proposed approach is illustrated through a slope example. Results show that the proposed Bayesian approach is not only able to quantify the uncertainty in the estimated SWRC and hydraulic conductivity, but also capable of predicting the SWRC and hydraulic conductivity with reasonable accuracy. This facilitates the determination of both the SWRC and hydraulic conductivity in geotechnical applications for engineers by making use of the available test data. Moreover, the proposed approach offers a viable way to provide a robust estimate of slope failure probability by virtue of Bayesian model averaging, allowing SWRC parameter uncertainty and model selection uncertainty to be considered simultaneously in reliability analysis. This addresses the dilemma of over-reliance on a single SWRC model in geo-technical reliability analysis involving unsaturated soils. It is worthy to note that among the four candidate models, three of them belong to one category which may have similar abilities, other parametric models in different categories need to be considered in future research.