Sequential reduction of slope stability uncertainty based on temporal hydraulic measurements via the ensemble Kalman filter

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
30 Downloads (Pure)

Abstract

A data assimilation framework, utilising measurements of pore water pressure to sequentially improve the estimation of soil hydraulic parameters and, in turn, the prediction of slope stability, is proposed. Its effectiveness is demonstrated for an idealised numerical example involving the spatial variability of saturated hydraulic conductivity, ksat. It is shown that the estimation of ksat generally improves with more measurement points. The degree of spatial correlation of ksat influences the improvement in the predicted performance, as does the selection of initial input statistics. However, the results are robust with respect to moderate uncertainty in the spatial and point statistics.

Original languageEnglish
Pages (from-to)147-161
Number of pages15
JournalComputers and Geotechnics
Volume95
DOIs
Publication statusPublished - 1 Mar 2018

Keywords

  • Data assimilation
  • Ensemble Kalman filter
  • Finite elements
  • Random fields
  • Slope reliability
  • Spatial variability

Fingerprint Dive into the research topics of 'Sequential reduction of slope stability uncertainty based on temporal hydraulic measurements via the ensemble Kalman filter'. Together they form a unique fingerprint.

  • Cite this