Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.gsf.2017.10.014
Title: Determination of site-specific soil-water characteristic curve from a limited number of test data – A Bayesian perspective
Authors: Wang, L.
Cao, Z.-J.
Li, D.-Q.
Phoon, K.-K. 
Au, S.-K.
Keywords: Bayesian approach
Degrees-of-belief
Soil–water characteristic curve
Unsaturated soils
UNSODA
Issue Date: 2018
Publisher: Elsevier B.V.
Citation: Wang, L., Cao, Z.-J., Li, D.-Q., Phoon, K.-K., Au, S.-K. (2018). Determination of site-specific soil-water characteristic curve from a limited number of test data – A Bayesian perspective. Geoscience Frontiers 9 (6) : 1665-1677. ScholarBank@NUS Repository. https://doi.org/10.1016/j.gsf.2017.10.014
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Abstract: Determining soil–water characteristic curve (SWCC) at a site is an essential step for implementing unsaturated soil mechanics in geotechnical engineering practice, which can be measured directly through various in-situ and/or laboratory tests. Such direct measurements are, however, costly and time-consuming due to high standards for equipment and procedural control and limits in testing apparatus. As a result, only a limited number of data points (e.g., volumetric water content vs. matric suction) on SWCC at some values of matric suction are obtained in practice. How to use a limited number of data points to estimate the site-specific SWCC and to quantify the uncertainty (or degrees-of-belief) in the estimated SWCC remains a challenging task. This paper proposes a Bayesian approach to determine a site-specific SWCC based on a limited number of test data and prior knowledge (e.g., engineering experience and judgment). The proposed Bayesian approach quantifies the degrees-of-belief on the estimated SWCC according to site-specific test data and prior knowledge, and simultaneously selects a suitable SWCC model from a number of candidates based on the probability logic. To address computational issues involved in Bayesian analyses, Markov Chain Monte Carlo Simulation (MCMCS), specifically Metropolis-Hastings (M-H) algorithm, is used to solve the posterior distribution of SWCC model parameters, and Gaussian copula is applied to evaluating model evidence based on MCMCS samples for selecting the most probable SWCC model from a pool of candidates. This removes one key limitation of the M-H algorithm, making it feasible in Bayesian model selection problems. The proposed approach is illustrated using real data in Unsaturated Soil Database (UNSODA) developed by U.S. Department of Agriculture. It is shown that the proposed approach properly estimates the SWCC based on a limited number of site-specific test data and prior knowledge, and reflects the degrees-of-belief on the estimated SWCC in a rational and quantitative manner. © 2017 China University of Geosciences (Beijing) and Peking University
Source Title: Geoscience Frontiers
URI: https://scholarbank.nus.edu.sg/handle/10635/211680
ISSN: 1674-9871
DOI: 10.1016/j.gsf.2017.10.014
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1016_j_gsf_2017_10_014.pdf3.36 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

30
checked on Sep 30, 2022

Page view(s)

62
checked on Oct 6, 2022

Download(s)

2
checked on Oct 6, 2022

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons