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

Lin Wang,Wengang Zhang,Fuyong Chen

doi:10.3390/en12152992

Lin Wang, Wengang Zhang + Show 1 more

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Journal: Energies	Publication Date: Aug 2, 2019
Citations: 27	License type: CC BY 4.0

Affiliation: Chongqing University, Hohai University

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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.

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Determination of soil-water characteristic curve (SWCC) is a necessary requirement for slope stability analysis involving unsaturated soils, which can be measured from different tests (e.g., [1,2]).it is well recognized that a limited number of discrete data points are typically obtained from direct measurements (e.g., [2,3]), instead of an entire curve of SWCC over a wide suction range
This paper aims to propose a Bayesian approach for predicting SWCC from particle-size distribution (PSD) by making use of the available test data
The Unsaturated Soil Database (UNSODA) established by U.S Department of Agriculture (USDA) contains a total of 790 soil samples, which can be categorized into 12 soil classes by virtue of the USDA soil conservation service classification scheme

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Determination of soil-water characteristic curve (SWCC) is a necessary requirement for slope stability analysis involving unsaturated soils, which can be measured from different tests (e.g., [1,2]).it is well recognized that a limited number of discrete data points are typically obtained from direct measurements (e.g., [2,3]), instead of an entire curve of SWCC over a wide suction range (i.e., from 0 to 106 kPa). A considerable number of approaches have been developed to predict the SWCC, which can be classified into two categories (e.g., [12,13,14]), namely, statistical approach and physico-empirical model. Empirical functions are developed to relate water content or SWCC model parameters to other common soil properties (e.g., [12,15,16,17]). These empirical functions are Energies 2019, 12, 2992; doi:10.3390/en12152992 www.mdpi.com/journal/energies

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