Abstract
The cone penetration test (CPT) has been widely used to determine the soil stratigraphy (including the number N and thicknesses
H
N
of soil layers) during geotechnical site investigation because it is rapid, repeatable, and economical. For this purpose, several deterministic and probabilistic approaches have been developed in the literature, but these approaches generally only give the “best” estimates (e.g., the most probable values) of N and
H
N
based on CPT data according to prescribed soil stratification criteria, providing no information on the identification uncertainty (degrees-of-belief) in these “best” estimates. This paper develops a Bayesian framework for probabilistic soil stratification based on the profile of soil behaviour type index I
c
calculated from CPT data. The proposed Bayesian framework not only provides the most probable values of N and
H
N
, but also quantifies their associated identification uncertainty based on the I
c
profile and prior knowledge. Equations are derived for the proposed approach, and they are illustrated and validated using real and simulated I
c
profiles. Results show that the proposed approach properly identifies the most probable soil stratigraphy based on the I
c
profile and prior knowledge, and rationally quantifies the uncertainty in identified soil stratigraphy with consideration of inherent spatial variability of I
c
.