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Safety
Special Issues
Application of Bayesian Networks to System Safety and Reliability
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Application of Bayesian Networks to System Safety and Reliability

A special issue of Safety (ISSN 2313-576X).

Deadline for manuscript submissions: closed (20 December 2019) | Viewed by 6092

Special Issue Editors

Prof. Dr. Nima Khakzad

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Guest Editor
Faculty of Technology, Policy, and Management, Delft University of Technology, 2628 BX Delft, The Netherlands
Interests: system safety; Bayesian network; cascading effects
Special Issues, Collections and Topics in MDPI journals
Dr. H.R. Noel Van Erp

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Guest Editor
Deutsche Forschungszentrum für Künstliche Intelligenz GmbH (DFKI), Bremen, Germany
Interests: Bayesian probability theory; information theory; decision theory; numerical estimation; cascading effects
Prof. Pieter van Gelder

E-Mail Website
Guest Editor
Section of Safety and Security Science, Department of Values, Technology and Innovation, Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands
Interests: system safety; probabilistic risk analysis; reliability engineering; decision-making under uncertainties; extreme value statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Conventional Bayesian network (BN) and its advanced extensions the dynamic Bayesian network (DBN) and the limited memory influence diagram (LIMID) have become very popular in the last decade among safety and reliability communities. This has been mostly due to flexibility and modelling features that BNs offer in modelling conditional dependencies, belief updating, combining subjective and objective data, modelling stochastic events and dynamic failures, and decision making, alone or in conjunction with other techniques.

The present Special Issue aims to deal with novel and innovative applications of BN to system safety and reliability assessment, other than its ordinary applications that prevail in cause–effect modelling and probability updating. Of particular interest are unpublished and original applications of BN (as well as DBN and LIMID) to the following:

  • Safety/reliability assessment of dynamic systems, and modelling and analysis of dynamic failures and cascading effects
  • Optimal/cost–benefit/safety-based design and decision-making
  • Resilience/vulnerability assessment of systems with regards to random failures, intentional attacks, and natural disasters
  • Human error probability assessment
  • Innovative integration of BN with information theory, game theory, agent-based modelling, optimization techniques, etc., for foregoing application domains

Prof. Dr. Nima Khakzad
Dr. H.R. Noel van Erp
Prof. Dr. P.H.A.J.M. van Gelder
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Safety is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • probabilistic safety assessment 
  • system safety 
  • system reliability 
  • Bayesian network 
  • limited memory influence diagram 
  • decision support systems

Published Papers (1 paper)

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Review

24 pages, 10747 KiB  
Review
Levee System Reliability Modeling: The Length Effect and Bayesian Updating
by Kathryn Roscoe, Anca Hanea, Ruben Jongejan and Ton Vrouwenvelder
Safety 2020, 6(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/safety6010007 - 03 Feb 2020
Cited by 2 | Viewed by 5681
Abstract
In levee system reliability, the length effect is the term given to the phenomenon that the longer the levee, the higher the probability that it will have a weak spot and fail. Quantitatively, it is the ratio of the segment failure probability to [...] Read more.
In levee system reliability, the length effect is the term given to the phenomenon that the longer the levee, the higher the probability that it will have a weak spot and fail. Quantitatively, it is the ratio of the segment failure probability to the cross-sectional failure probability. The literature is lacking in methods to calculate the length effect in levees, and often over-simplified methods are used. An efficient (but approximate) method, which we refer to as the modified outcrossing (MO) method, was developed for the system reliability model used in Dutch national flood risk analysis and for the provision of levee assessment tools, but it is poorly documented and its accuracy has not been tested. In this paper, we propose a method to calculate the length effect in levees by sampling the joint spatial distribution of the resistance variables using a copula approach, and represented by a Bayesian Network (BN). We use the BN to verify the MO method, which is also described in detail in this paper. We describe how both methods can be used to update failure probabilities of (long) levees using survival observations (i.e., high water levels and no levee failure), which is important because we have such observations in abundance. We compared the methods via a numerical example, and found that the agreement between the segment failure probability estimates was nearly perfect in the prior case, and very good in the posterior case, for segments ranging from 500 m to 6000 m in length. These results provide a strong verification of both methods, either of which provide an attractive alternative to the more simplified approaches often encountered in the literature and in practice. Full article
(This article belongs to the Special Issue Application of Bayesian Networks to System Safety and Reliability)
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