Citation:
Dimitris Konovessis,Wenkui Cai and Dracos Vassalos.Development of Bayesian Network Models for Risk-Based Ship Design[J].Journal of Marine Science and Application,2013,(2):140-151.[doi:10.1007/s11804-013-1179-9]
Click and Copy
Development of Bayesian Network Models for Risk-Based Ship Design
Dimitris Konovessis,Wenkui Cai and Dracos Vassalos.Development of Bayesian Network Models for Risk-Based Ship Design[J].Journal of Marine Science and Application,2013,(2):140-151.[doi:10.1007/s11804-013-1179-9]
Info
- Title:
- Development of Bayesian Network Models for Risk-Based Ship Design
- Author(s):
- Dimitris Konovessis; Wenkui Cai and Dracos Vassalos
- Affilations:
- DOI:
- 10.1007/s11804-013-1179-9
- 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.
References:
Anand S, Keren N, Tretter MJ, Wang Y, O’Connor TM, Mannan MS (2006). Harnessing data mining to explore incident databases. Journal of Hazardous Materials, 130(1-2 SPEC. ISS.), 33-41.
Chen Z (2001). Data mining and uncertain reasoning: an integrated approach. New York, Chichester, Wiley.
Darwiche A (2009). Modeling and reasoning with Bayesian networks. Cambridge University Press, Cambridge.
Eleye-Datubo AG, Wall A, Saajedi A, Wang J (2006). Enabling a powerful marine and offshore decision-support solution through Bayesian network technique. Risk Analysis, 26(3), 695-721.
EPA. Environment Protection Agency. Exxon Valdez (2009). http://www.epa.gov/oem/content/learning/exxon.htm. Accessed 18 April, 2013.
Faber MH, Kroon IB, Kragh E, Bayly D, Decosemaeker P (2002). Risk assessment of decommissioning options using Bayesian networks. J. Offshore MechArct., 124(4), 231-238.
Friis-Hansen A (2000). Bayesian Networks as a Decision Support Tool in Marine Applications. Ph.D. thesis, Department of Naval Architecture and Offshore Engineering, Technical University of Denmark, Kgs. Lyngby, Denmark.
Friis-Hansen P, Garre L (2007). SAFEDOR D 2.2.1 Probabilistic Models for Load Effects: Uncertainty modeling of load and responses, Identification of most critical collision damage scenarios.
Han J, Kamber M (2006). Data mining: concepts and techniques. 2nd ed., Elsevier, Amsterdam, London.
HARDER. Harmonization of Rules and Design Rationale, Project Contact No. GDRB-CT-1998-00028 in 5th Framework Program of the European Commission. 2000-2003.
Holmes DEP, Jain LC (2008). Innovations in Bayesian networks: theory and applications. Springer, Berlin.
IMO (2006). International Maritime Organization. MSC 81/24/5 - Any other Business, FSA Study on ECDIS/ENCs, Submitted by Denmark and Norway. In: IMO, ed. MSC 81/24/5. London.
Kaneko F, Yoshida K (2007). Consideration of Bayesian network’s applicability to FSA and an evaluation of effectiveness of ECDIS by application of the network. The 3rd Annual Conference on Design for Safety, Berkeley, USA.
Kjaerulff UB, Madsen AL (2008). Bayesian networks and influence diagrams: a guide to construction and analysis. Springer, New York.
Konovessis D, Vassalos D (2008). Risk evaluation for RoPax vessels. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 222(1), 13-26.
Korb KB, Nicholson AE (2004). Bayesian artificial intelligence. Boca Raton, Chapman & Hall/CRC, London.
Leva MC (2006). SAFEDOR D 2.4.2 Model of the Operator. MAIB (1987). Marine Accident Investigation Branch. MV HERALD OF FREE ENTERPRISE. London September.
Nazeri Z, Bloedorn E, Ostwald P (2001). Experiences in mining aviation safety data. Santa Barbara, CA, United States.
Neapolitan RE (2004). Learning Bayesian networks. Prentice Hall, Harlow.
Parker V (2006). The Titanic 1912: the loss of an unsinkable liner. Raintree, Oxford.
Povel D, Dausendschon K (2007). SAFEDOR D 2.5.4 Cargo Transport Safety - Quantitative Risk Analysis (QRA).
Ravn E (2006a). SAFEDOR D 2.4.3 Modeling of Causation Factor for Ship Under Power.
Ravn E (2006b). SAFEDOR D 2.4.7 Risk-Based Model for Failure of Propulsion and Steering Gear System.
SAFEDOR (2013). Design, operation and regulation for safety. Integrated Project 516278 in 6th Framework program of the European Commission. 2005-2009; http://www.safedor.org/. Accessed 18 April, 2013.
Skjong R (2008). Chapter 3: Regulatory Framework. In: Papanikolaou A, ed. Risk-Based Ship Design - Methods, Tools and Application. Berlin: Springer, ix, 376.
Spirtes P, Glymour CN, Scheines R (2000). Causation, prediction, and search. 2nd ed. / Peter Spirtes, Clark Glymour, and Richard Scheines; with additional material by David Heckerman ... [et al.] ed. MA, MIT Press, Cambridge, USA.
SSRC (2007). Ship Stability Research Centre. SAFEDOR D 2.5.2 Quantitative Risk Analysis (QRA).
Vassalos D, Konovessis D (2008). The thematic network SAFER EURORO: An integrated approach to safe European RoRo ferry design. Marine Technology, 45(1), 1-8.
Vassalos D (2004). A Risk-Based Approach to probabilistic Damage Stability. Proceedings of the 7th International Ship Stability Workshop, Shanghai, China.
Vassalos D (2008). Risk-based ship design - methods, tools and application. In: Papanikolaou A, ed. Berlin: Springer, ix, 376.
Vose D (2008). Risk analysis: a quantitative guide, 3rd ed. Hoboken, N.J., Wiley, Chichester: John Wiley.
Memo
- Memo:
- -
