Quantification of prior knowledge in geotechnical site characterization
Quantification of prior knowledge in geotechnical site characterization
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DOI：
10.1016/j.enggeo.2015.08.018
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发表时间：
2016-03-25
影响因子：
7.4
通讯作者：
Li, Dianqing
中科院分区：
文献类型：
--
作者：
Cao, Zijun;Wang, Yu;Li, Dianqing
项目场地的岩土特征通常从案头研究和现场勘察开始，这两项工作提供了项目前可用的场地信息(例如，文献中的现有数据、工程经验和工程师的专业知识)。这种信息可以作为贝叶斯框架下的“先验知识”，并通过贝叶斯方法中的先验分布来定量反映。然而，对于工程从业者来说，将先验知识适当量化为先验分布并不是一件微不足道的任务。本文开发了两种不同的方法来量化工程场地岩土工程表征过程中的先验知识。如果现场没有普遍的先验知识，则使用非信息性的先验分布(例如，统一的先验分布)来定量地反映工程常识和判断。随着先验知识的改进和信息量的增加，提出了一种基于先验知识估计先验分布的主观概率评估框架。拟议的SPAF框架协助岩土工程师以可量化和透明的方式制定和表达他们的工程判断。这两种不同的方法是使用来自德克萨斯农工大学(TAMU)美国国家岩土实验场地(NGES)的沙地信息来说明的。(C)2015爱思唯尔B.V.保留所有权利。
Geotechnical characterization of a project site often starts with desk-study and site reconnaissance, which provide site information available prior to the project (e.g., existing data in literature, engineering experience, and engineers' expertise). Such information can be used as "prior knowledge" under a Bayesian framework and be quantitatively reflected by a prior distribution in Bayesian methods. However, it is not a trivial task for engineering practitioners to properly quantify prior knowledge as a prior distribution. This paper develops two different methods to quantify prior knowledge during geotechnical characterization of a project site. Where there is no prevailing prior knowledge on the site, a non-informative prior distribution (e.g., uniform prior distribution) is used to reflect quantitatively the engineering common sense and judgment. As prior knowledge improves and becomes much more informative, a subjective probability assessment framework (SPAF) is proposed to estimate the prior distribution from prior knowledge. The proposed SPAF framework assists geotechnical engineers in formulating and expressing their engineering judgments in a quantifiable and transparent manner. These two different methods are illustrated using information from a sand site of the US National Geotechnical Experimentation Sites (NGES) at Texas A&M University (TAMU). (C) 2015 Elsevier B.V. All rights reserved.