Bayesian Inference of Piping Model Uncertainties Based on Field Observations

Wim Kanning; Timo Schweckendiek

doi:10.3850/978-981-11-2725-0_IS4-9-cd

Bayesian Inference of Piping Model Uncertainties Based on Field Observations

Hydraulic Structures and Flood Risk

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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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 language	English
Title of host publication	Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)
Subtitle of host publication	State-of-the-Practice in Geotechnical Safety and Risk
Editors	Jianye Ching, Dian-Qing Li, Jie Zhang
Place of Publication	Taipei, Taiwan
Pages	787-791
Number of pages	5
ISBN (Electronic)	978-981-11-2725-0
DOIs	https://doi.org/10.3850/978-981-11-2725-0_IS4-9-cd
Publication status	Published - 2019
Event	ISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk - National Taiwan University of Science and Technology, Taipei, Taiwan Duration: 11 Dec 2019 → 13 Dec 2019 http://isgsr2019.org/

Conference

Conference	ISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk
Abbreviated title	ISGSR 2019
Country/Territory	Taiwan
City	Taipei
Period	11/12/19 → 13/12/19
Internet address	http://isgsr2019.org/

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

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10.3850/978-981-11-2725-0_IS4-9-cd

IS4-9Final published version, 648 KB

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Kanning, W., & Schweckendiek, T. (2019). Bayesian Inference of Piping Model Uncertainties Based on Field Observations. In J. Ching, D.-Q. Li, & J. Zhang (Eds.), Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019): State-of-the-Practice in Geotechnical Safety and Risk (pp. 787-791). Article IS4-9. https://doi.org/10.3850/978-981-11-2725-0_IS4-9-cd

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title = "Bayesian Inference of Piping Model Uncertainties Based on Field Observations",

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. ",

keywords = "Bayesian inference, backward erosion piping, field observations, model uncertainty",

author = "Wim Kanning and Timo Schweckendiek",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – 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.; ISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk, ISGSR 2019 ; Conference date: 11-12-2019 Through 13-12-2019",

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Kanning, W & Schweckendiek, T 2019, Bayesian Inference of Piping Model Uncertainties Based on Field Observations. in J Ching, D-Q Li & J Zhang (eds), Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019): State-of-the-Practice in Geotechnical Safety and Risk., IS4-9, Taipei, Taiwan, pp. 787-791, ISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk, Taipei, Taiwan, 11/12/19. https://doi.org/10.3850/978-981-11-2725-0_IS4-9-cd

Bayesian Inference of Piping Model Uncertainties Based on Field Observations. / Kanning, Wim ; Schweckendiek, Timo.
Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019): State-of-the-Practice in Geotechnical Safety and Risk. ed. / Jianye Ching; Dian-Qing Li; Jie Zhang. Taipei, Taiwan, 2019. p. 787-791 IS4-9.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Kanning, Wim

AU - Schweckendiek, Timo

N1 - 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.

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Bayesian inference

KW - backward erosion piping

KW - field observations

KW - model uncertainty

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DO - 10.3850/978-981-11-2725-0_IS4-9-cd

M3 - Conference contribution

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EP - 791

BT - Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)

A2 - Ching, Jianye

A2 - Li, Dian-Qing

A2 - Zhang, Jie

CY - Taipei, Taiwan

T2 - ISGSR 2019: 7th International Symposium on Geotechnical Safety and Risk

Y2 - 11 December 2019 through 13 December 2019

ER -

Kanning W , Schweckendiek T. Bayesian Inference of Piping Model Uncertainties Based on Field Observations. In Ching J, Li DQ, Zhang J, editors, Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019): State-of-the-Practice in Geotechnical Safety and Risk. Taipei, Taiwan. 2019. p. 787-791. IS4-9 doi: 10.3850/978-981-11-2725-0_IS4-9-cd

Bayesian Inference of Piping Model Uncertainties Based on Field Observations

Abstract

Conference

Bibliographical note

Keywords

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