A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation

Trucco, Paolo; Cagno, Enrico; Ruggeri, Fabrizio; Grande, O.

doi:10.1016/j.ress.2007.03.035

Cited by 428 publications

(258 citation statements)

References 7 publications

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“…Sequential models work well for accidents caused by failure of physical components and for relatively simple systems (Leveson, 2004). But, they do not work well in all situations and systems because these models do not account for and therefore cannot correctly model complex phenomena and systems (Leveson, 2004;Trucco et al, 2008). They only provide partial answers to questions concerning safety measures (Harrald et al, 1998).…”

Section: Accident Modelsmentioning

confidence: 99%

“…Further, many models are based on theories or concepts, for example systems theory concepts (Leveson, 2004;Laracy, 2006;Larsson et al, 2010), the Bayesian Belief Network concept (BBN) (Trucco et al, 2008;Merrick and Singh, 2003), Neural Networks (NN) concept (Hashemi et al, 1995;Le Blanc et al, 2001), fuzzy logic (Sii et al, 2001), risk-based approaches (Vanem and Skjong, 2006;Celik et al, 2010), simulation and expert judgment (Harrald et al, 1998). Harrald et al (1998) have pointed out that a theoretical framework is of little use in an analysis unless there is relevant data to support it.…”

Section: Accident Modelsmentioning

confidence: 99%

“…Accident studies in many domains including the maritime industry are firmly rooted in probabilistic methods (Leveson, 2004;Trucco et al, 2008) and other summary statistics (Vanem and Skjong, 2006;Grabowski et al, 2000;Van Drop et al, 2001;Özgecan and Ulusç u, 2009;Konovessis and Vassalos, 2008;Merrick and Singh, 2003). The studies are largely confined to a few variables, such as vessel type, flag, and business line, total loss incidents (Li et al, 2009), and some categories of accidents, such as fire/explosion, collision (Van Drop et al, 2001;Le Blanc et al, 2001), contact and grounding (Vanem and Skjong, 2006;Özgecan and Ulusç u, 2009).…”

Section: Accident Modelsmentioning

confidence: 99%

“…But, the results of this study suggest that this statement may not be quite accurate. The earlier studies have been largely based on summary statistics (Vanem and Skjong, 2006;Grabowski et al, 2000;Van Drop et al, 2001;Özgecan and Ulusç u, 2009) and accident models that do not account for complex phenomena and systems (Leveson, 2004;Trucco et al, 2008). Prevention programs are often proposed to reduce the incidence of human error (Harrald et al, 1998).…”

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confidence: 99%

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A grounded theory model for analysis of marine accidents

Mullai

Paulsson

2011

Accident Analysis & Prevention

104

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Link to publication Citation for published version (APA): Mullai, A., & Paulsson, U. (2011). A grounded theory model for analysis of marine accidents. Accident; analysis and prevention, 43(4), 1590 -1603 . DOI: 10.1016 /j.aap.2011 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. b s t r a c tThe purpose of this paper was to design a conceptual model for analysis of marine accidents. The model is grounded on large amounts of empirical data, i.e. the Swedish Maritime Administration database, which was thoroughly studied. This database contains marine accidents organized by ship and variable. The majority of variables are non-metric and some have never been analyzed because of the large number of values. Summary statistics were employed in the data analysis. In order to develop a conceptual model, the database variables were clustered into eleven main categories or constructs, which were organized according to their properties and connected with the path diagram of relationships. For demonstration purposes, one non-metric and five metric variables were selected, namely fatality, ship's properties (i.e. age, gross register tonnage, and length), number of people on board, and marine accidents. These were analyzed using the structural equation modeling (SEM) approach. The combined prediction power of the 'ship's properties' and 'number of people on board' independent variables accounted for 65% of the variance of the fatality. The model development was largely based on the data contained in the Swedish database. However, as this database shares a number of variables in common with other databases in the region and the world, the model presented in this paper could be applied to other datasets. The model has both theoretical and practical values. Recommendations for improvements in the database are also suggested.

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Section: Accident Modelsmentioning

confidence: 99%

Section: Accident Modelsmentioning

confidence: 99%

Section: Accident Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

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A grounded theory model for analysis of marine accidents

Mullai

Paulsson

2011

Accident Analysis & Prevention

104

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“…Few specific methods are able to model risk correlations with a network structure. Several papers on the application of the Bayesian belief network (BBN) have appeared in recent years in the field of project risk management (Fan and Yu 2004, Lee et al 2008, Trucco et al 2008), which could model risk interrelations, from multiple inputs to multiple outputs. Nevertheless, BBN demands oriented links, is inherently acyclic, and hence does not easily model the loop phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

Fang

Marle

2013

Journal of Engineering Design

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Bayesian Implementation of the Fault Tree Analysis

Joshi

Ruggeri

Wilson

2022

Wiley StatsRef: Statistics Reference Online

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This article illustrates why a fully Bayesian approach to Fault Tree Analysis can be useful, describes how it can be performed, and discusses one of the important considerations to be made regarding such analysis, namely, prior elicitation. It then describes an easy‐to‐use prior robustness approach for Fault Tree Analysis to quantify the sensitivity of the prior elicitation of the failure probability to misspecification of uncertainty in elementary events.

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A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation

Cited by 428 publications

References 7 publications

A grounded theory model for analysis of marine accidents

A grounded theory model for analysis of marine accidents

Dealing with project complexity by matrix-based propagation modelling for project risk analysis

Bayesian Implementation of the Fault Tree Analysis

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