Bayesian perspective on geotechnical variability and site characterization
Abstract Geotechnical characterization of a project site for engineering applications is indispensable in engineering geology and geotechnical engineering, and there are many unavoidable variabilities and uncertainties during characterization of a project site. Different variabilities and uncertainties usually are lumped together and observed as the total variability, which includes both the actual variability of soil and rock properties and other knowledge uncertainties, such as measurement errors and statistical uncertainty. It is the actual variability, not the total variability, which affects directly the observed performance (i.e., actual response) of geotechnical and geological systems and is of primary interest in site characterization. This paper aims to consolidate recent advancement in Bayesian studies in site characterization and develops a Bayesian inverse analysis framework for direct quantification of the actual variability of various soil and rock properties. To facilitate development of the framework, the procedure of geotechnical site characterization is revisited from a Bayesian perspective, and the occurrence and propagation of inherent variability, statistical uncertainty, measurement errors, and transformation uncertainty during characterization of a project site are mapped explicitly to different stages in site investigation. Based on the mapping, a robust framework is developed that streamlines the formulation of likelihood functions for various soil and rock properties when estimated using different field or laboratory tests, leading to a streamlined process for applications of the proposed Bayesian framework to different site characterization problems. Application examples are provided to illustrate the implementation and step-by-step procedures of the proposed Bayesian framework.