Bayesian Inference of Piping Model Uncertainties Based on Field Observations

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

9 Downloads (Pure)

Abstract

This paper presents a Bayesian model to determine the model uncertainty of a critical horizontal gradient model for piping for dikes, such a Lane and Bligh. A Bayesian model is needed for two reasons. First, there is a large overlap in cases that failed and survived. Second, the evidence of the failed cases is limited .The model consists of a non-informative prior that is combined with likelihood functions for failed and survived cases. This involves modeling the mean and standard deviation of the model uncertainty as random variables. For survived cases we know the limit state function was larger than 0 for the observed water level. For failed cases we know the limit state function was smaller than 0; or Z = 0; which is a less conservative assumption. This information is used to determine the likelihood functions for failed and survived cases. The prior and likelihoods are combined to find the posterior distributions of the mean and standard distribution of the model uncertainty. Using integration, this finally results in the (lognormal) distribution of the model uncertainty. The model is applied to the data of Bligh and Lane and shows both a high mean and high standard deviation of the model uncertainty, where the model of Lane performs better than Bligh. It is recommended to tailor the proposed model to dikes by making a different distinction between horizontal and vertical erosion. Furthermore, it is recommended to apply the model to more dike specific data since the Bligh data mainly consists of dams instead of dikes.
Original languageEnglish
Title of host publicationProceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)
Subtitle of host publicationState-of-the-Practice in Geotechnical Safety and Risk
EditorsJianye Ching, Dian-Qing Li, Jie Zhang
Place of PublicationTaipei, Taiwan
Pages787-791
Number of pages5
ISBN (Electronic)978-981-11-2725-0
DOIs
Publication statusPublished - 2019
EventISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk - National Taiwan University of Science and Technology, Taipei, Taiwan
Duration: 11 Dec 201913 Dec 2019
http://isgsr2019.org/

Conference

ConferenceISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk
Abbreviated titleISGSR 2019
CountryTaiwan
CityTaipei
Period11/12/1913/12/19
Internet address

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

  • Bayesian inference
  • backward erosion piping
  • field observations
  • model uncertainty

Fingerprint

Dive into the research topics of 'Bayesian Inference of Piping Model Uncertainties Based on Field Observations'. Together they form a unique fingerprint.

Cite this