Learning soil parameters and updating geotechnical reliability estimates under spatial variability–theory and application to shallow foundations

Iason Papaioannou, Daniel Straub

Research output: Contribution to journalArticlepeer-review

52 Scopus citations

Abstract

Field data is commonly used to determine soil parameters for geotechnical analysis. Bayesian analysis allows combining field data with other information on soil parameters in a consistent manner. We show that the spatial variability of the soil properties and the associated measurements can be captured through two different modelling approaches. In the first approach, a single random variable (RV) represents the soil property within the area of interest, while the second approach models the spatial variability explicitly with a random field (RF). We apply the Bayesian concept exemplarily to the reliability assessment of a shallow foundation in a silty soil with spatially variable data. We show that the simpler RV approach is applicable in cases where the measurements do not influence the correlation structure of the soil property at the vicinity of the foundation. In other cases, it is expected to underestimate the reliability, and a RF model is required to obtain accurate results.

Original languageEnglish
Pages (from-to)116-128
Number of pages13
JournalGeorisk
Volume11
Issue number1
DOIs
StatePublished - 2 Jan 2017

Keywords

  • Bayesian analysis
  • random fields
  • reliability analysis
  • shallow foundations
  • spatial variability

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