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

K. Liu, P. J. Vardon, M. A. Hicks*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

21 Citations (Scopus)
61 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

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

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

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