Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks

Abstract

Bayesian networks are proposed as a tool to integrate reliability and influential variables relating to the slope stability of an idealized embankment. The site investigation (extent) and slope geometry, as well as the material properties and their spatial variability, are considered within a Bayesian network. The random finite element method (RFEM) is used to quantify the slope reliability and demonstrate the overall methodology. Prior probabilities of geometry, material parameters and their heterogeneity are obtained from ‘initial’ site investigation data. Probabilistic distributions of slope performance (factor of safety) are obtained by Bayesian inference in the network to investigate the impact of additional site investigation. The amount of additional site investigation required to increase the geotechnical reliability is assessed. This work illustrates the applicability of Bayesian networks as an effective reliability and uncertainty assessment tool that can aid decision making for site investigation and during maintenance, where new observations can be readily integrated to obtain updated reliability estimates.