Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks

Varenya Duvvuru Mohan; Phil Vardon; Michael Hicks; Pieter van Gelder

doi:10.3850/978-981-11-2725-0-IS3-5-cd

Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks

Varenya Duvvuru Mohan, Phil Vardon, Michael Hicks, Pieter van Gelder

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

71 Downloads (Pure)

Abstract

Bayesian networks are proposed as a tool to integrate reliability and influential variables relating to the slope stability of an idealized embankment. The site investigation (extent) and slope geometry, as well as the material properties and their spatial variability, are considered within a Bayesian network. The random finite element method (RFEM) is used to quantify the slope reliability and demonstrate the overall methodology. Prior probabilities of geometry, material parameters and their heterogeneity are obtained from ‘initial’ site investigation data. Probabilistic distributions of slope performance (factor of safety) are obtained by Bayesian inference in the network to investigate the impact of additional site investigation. The amount of additional site investigation required to increase the geotechnical reliability is assessed. This work illustrates the applicability of Bayesian networks as an effective reliability and uncertainty assessment tool that can aid decision making for site investigation and during maintenance, where new observations can be readily integrated to obtain updated reliability estimates.

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	631-636
Number of pages	6
ISBN (Electronic)	978-981-11-2725-0
DOIs	https://doi.org/10.3850/978-981-11-2725-0-IS3-5-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 network
geotechnical reliability
random fields
slope reliability
spatial variability
uncertainty

Access to Document

10.3850/978-981-11-2725-0-IS3-5-cd

IS3-5Final published version, 2.1 MB

Cite this

Duvvuru Mohan, V., Vardon, P., Hicks, M., & van Gelder, P. (2019). Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks. 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. 631-636). Article IS3-5. https://doi.org/10.3850/978-981-11-2725-0-IS3-5-cd

Duvvuru Mohan, Varenya ; Vardon, Phil ; Hicks, Michael et al. / Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks. Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019): State-of-the-Practice in Geotechnical Safety and Risk. editor / Jianye Ching ; Dian-Qing Li ; Jie Zhang. Taipei, Taiwan, 2019. pp. 631-636

@inproceedings{7f9d5eaf87dc4e59b59969783594662c,

title = "Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks",

abstract = "Bayesian networks are proposed as a tool to integrate reliability and influential variables relating to the slope stability of an idealized embankment. The site investigation (extent) and slope geometry, as well as the material properties and their spatial variability, are considered within a Bayesian network. The random finite element method (RFEM) is used to quantify the slope reliability and demonstrate the overall methodology. Prior probabilities of geometry, material parameters and their heterogeneity are obtained from {\textquoteleft}initial{\textquoteright} site investigation data. Probabilistic distributions of slope performance (factor of safety) are obtained by Bayesian inference in the network to investigate the impact of additional site investigation. The amount of additional site investigation required to increase the geotechnical reliability is assessed. This work illustrates the applicability of Bayesian networks as an effective reliability and uncertainty assessment tool that can aid decision making for site investigation and during maintenance, where new observations can be readily integrated to obtain updated reliability estimates.",

keywords = "Bayesian network, geotechnical reliability, random fields, slope reliability, spatial variability, uncertainty",

author = "{Duvvuru Mohan}, Varenya and Phil Vardon and Michael Hicks and {van Gelder}, Pieter",

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",

year = "2019",

doi = "10.3850/978-981-11-2725-0-IS3-5-cd",

language = "English",

pages = "631--636",

editor = "Ching, {Jianye } and Dian-Qing Li and Jie Zhang",

booktitle = "Proceedings of the 7th International Symposium on Geotechnical Safety and Risk (ISGSR 2019)",

url = "http://isgsr2019.org/",

}

Duvvuru Mohan, V , Vardon, P , Hicks, M & van Gelder, P 2019, Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks. 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., IS3-5, Taipei, Taiwan, pp. 631-636, 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-IS3-5-cd

Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks. / Duvvuru Mohan, Varenya ; Vardon, Phil ; Hicks, Michael et al.
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. 631-636 IS3-5.

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

TY - GEN

T1 - Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks

AU - Duvvuru Mohan, Varenya

AU - Vardon, Phil

AU - Hicks, Michael

AU - van Gelder, Pieter

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 - Bayesian networks are proposed as a tool to integrate reliability and influential variables relating to the slope stability of an idealized embankment. The site investigation (extent) and slope geometry, as well as the material properties and their spatial variability, are considered within a Bayesian network. The random finite element method (RFEM) is used to quantify the slope reliability and demonstrate the overall methodology. Prior probabilities of geometry, material parameters and their heterogeneity are obtained from ‘initial’ site investigation data. Probabilistic distributions of slope performance (factor of safety) are obtained by Bayesian inference in the network to investigate the impact of additional site investigation. The amount of additional site investigation required to increase the geotechnical reliability is assessed. This work illustrates the applicability of Bayesian networks as an effective reliability and uncertainty assessment tool that can aid decision making for site investigation and during maintenance, where new observations can be readily integrated to obtain updated reliability estimates.

AB - Bayesian networks are proposed as a tool to integrate reliability and influential variables relating to the slope stability of an idealized embankment. The site investigation (extent) and slope geometry, as well as the material properties and their spatial variability, are considered within a Bayesian network. The random finite element method (RFEM) is used to quantify the slope reliability and demonstrate the overall methodology. Prior probabilities of geometry, material parameters and their heterogeneity are obtained from ‘initial’ site investigation data. Probabilistic distributions of slope performance (factor of safety) are obtained by Bayesian inference in the network to investigate the impact of additional site investigation. The amount of additional site investigation required to increase the geotechnical reliability is assessed. This work illustrates the applicability of Bayesian networks as an effective reliability and uncertainty assessment tool that can aid decision making for site investigation and during maintenance, where new observations can be readily integrated to obtain updated reliability estimates.

KW - Bayesian network

KW - geotechnical reliability

KW - random fields

KW - slope reliability

KW - spatial variability

KW - uncertainty

U2 - 10.3850/978-981-11-2725-0-IS3-5-cd

DO - 10.3850/978-981-11-2725-0-IS3-5-cd

M3 - Conference contribution

SP - 631

EP - 636

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 -

Duvvuru Mohan V , Vardon P , Hicks M , van Gelder P. Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks. 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. 631-636. IS3-5 doi: 10.3850/978-981-11-2725-0-IS3-5-cd

Uncertainty Tracking and Geotechnical Reliability Updating Using Bayesian Networks

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Cite this