ZHANG Guoping,WANG Lin,CAO Zijun,et al.Comparative study of different methods for determining the soil water characteristic curve model considering uncertainty[J].,2018,(04):151-158.[doi:10.13577/j.jnd.2018.0420]





Comparative study of different methods for determining the soil water characteristic curve model considering uncertainty
张国萍 王林 曹子君 李典庆
武汉大学水资源与水电工程科学国家重点实验室, 工程风险与防灾研究所, 湖北 武汉 430072
ZHANG Guoping WANG Lin CAO Zijun LI Dianqing
State Key Laboratory of Water Resources and Hydropower Engineering Science, Institute of Engineering Risk and Disaster Prevention, Wuhan University, Wuhan 430072, China
unsaturated soilsoil-water characteristic curveuncertaintyBayesian approachmodel selection
土水特征曲线(SWCC)描述了非饱和土中体积含水量(或有效饱和度)与压力水头(或基质吸力)之间的关系。SWCC作为非饱和土力学中的基本函数,可用于研究非饱和土的抗剪强度、渗透特性以及变形特性等,在非饱和土力学应用中发挥着重要作用。工程实践中,通常可采用直接试验方法测得SWCC数据。由于直接试验方法条件苛刻且耗时较长,难以获得体积含水量范围内完整的SWCC数据。在有限试验数据条件下,如何更好的选择SWCC模型是一个关键问题。本文系统对比了考虑不确定性条件下解决该问题的3种方法,即贝叶斯模型比选方法(Bayesian)、Akaike information criterion (AIC)识别准则和Bayesian information criterion (BIC)识别准则。首先介绍了上述3种模型比选方法;然后,采用UNSODA数据库中试验数据开展研究,分别从拟合度和罚值2个方面综合比较3种方法在SWCC模型比选过程中的差异。结果表明:给定试验数据条件下,3种方法所确定的最优SWCC模型不尽相同,Bayesian方法在考虑模型罚值的同时更好地反映了试验数据信息,AIC、BIC准则中不同SWCC模型的拟合度可能十分接近,此时模型比选结果受模型罚值影响显著。
Soil water characteristic curve (SWCC) describes the relationship between volumetric water content (or effective saturation) and pressure head (or matric suction) in unsaturated soils. As an essential element of unsaturated soil mechanics, SWCC can be used to study the shear strength, permeability and deformation properties of unsaturated soils, which plays an important role in the application of unsaturated soil mechanics. In engineering practice, SWCC data can be measured directly by direct test methods. Because the direct test method requires high-standard control and time-consuming, it is difficult to obtain complete SWCC data in the volumetric water content range. Under the condition of a limited number of test data, how to select the SWCC model in a rational manner is a key issue. This paper summarizes three methods to solve the problem, namely, Bayesian model selection method (Bayesian), Akaike information criterion (AIC) and Bayesian information criterion (BIC). First, the theoretical background of the three SWCC methods is introduced. Then, the experimental data in the UNSODA database are used to carry out the research. Differences of the three methods in the determination of the SWCC model are compared from two aspects of the fitting degree and the penalty value. Results show that, for a given set of SWCC data, different optimal models can be obtained from the three methods. The optimal model selected by the Bayesian method reasonably reflects information with the consideration of model penalty while the goodness-of-fit of the optimal models obtained from AIC and BIC can be similar so that the model penalty plays key roles in model selection.


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