Development of Bayesian Network Models for Risk-Based Ship Design

Dimitrios Konovessis, Wenkui Cai, Dracos Vassalos

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

In the past fifteen years, the attention of ship safety treatment as an objective rather than a constraint has started to sweep through the whole maritime industry. The risk-based ship design (RBD) methodology, advocating systematic integration of risk assessment within the conventional design process has started to takeoff. Despite this wide recognition and increasing popularity, important factors that could potentially undermine the quality of the results come from both quantitative and qualitative aspects during the risk assessment process. This paper details a promising solution by developing a formalized methodology for risk assessment through effective storing and processing of historical data combined with data generated through first-principle approaches. This method should help to generate appropriate risk models in the selected platform (Bayesian networks) which can be employed for decision making at design stage.
Original languageEnglish
Pages (from-to)140-151
Number of pages12
JournalJournal of Marine Science and Application
Volume12
Issue number2
DOIs
Publication statusPublished - 1 Jun 2013

Keywords

  • risk based ship design
  • risk assessment
  • data mining
  • Bayesian networks
  • ship safety

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