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Research Papers

Rare Event Analysis Considering Data and Model Uncertainty

[+] Author and Article Information
Malak El-Gheriani

Centre for Risk, Integrity and Safety Engineering
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: maeg40@mun.ca

Faisal Khan

Centre for Risk, Integrity and Safety Engineering
(C-RISE),
Faculty of Engineering and Applied Science,
Memorial University,
St John's, NL A1B 3X5, Canada
e-mail: fikhan@mun.ca

Ming J. Zuo

Department of Mechanical Engineering,
Faculty of Engineering,
University of Alberta,
Edmonton, AB T6G 1H9, Canada
e-mail: ming.zuo@ualberta.ca

1Corresponding author.

Manuscript received September 19, 2016; final manuscript received March 2, 2017; published online March 31, 2017. Assoc. Editor: Konstantin Zuev.

ASME J. Risk Uncertainty Part B 3(2), 021008 (Mar 31, 2017) (15 pages) Paper No: RISK-16-1125; doi: 10.1115/1.4036155 History: Received September 19, 2016; Revised March 02, 2017

In risk analysis of rare events, there is a need to adopt data from different sources with varying levels of detail (e.g., local, regional, categorical data). Therefore, it is very important to identify, understand, and incorporate the uncertainty that accompanies the data. Hierarchical Bayesian analysis (HBA) addresses uncertainty among the aggregated data for each event through generating an informative prior distribution for the event's parameter of interest. The Bayesian network (BN) approach is used to model accident causation. BN enables both inductive and abductive reasoning, which helps to better understand and minimize model uncertainty. In this work, the methodology is proposed to integrate BN with HBA to model rare events, considering both data and model uncertainty. HBA considers data uncertainty, while BN uses an adaptive model to better represent and manage model uncertainty. Application of the proposed methodology is demonstrated using three types of offshore accidents. The proposed methodology provides a way to develop a dynamic risk analysis approach to rare events.

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References

Khakzad, N. , Khan, F. , and Amyotte, P. , 2011, “ Safety Analysis in Process Facilities: Comparison of Fault Tree and Bayesian Network Approaches,” Reliab. Eng. Syst. Saf., 96(8), pp. 925–932. [CrossRef]
Schöbi, R. , Sudret, B. , and Marelli, S. , 2016, “ Rare Event Estimation Using Polynomial-Chaos Kriging,” ASCE-ASME J. Risk Uncertainty Eng. Syst. Part A, (published ahead of print March 28, 2016).
Chakraborty, S. , and Chowdhury, R. , 2017, “ Hybrid Framework for the Estimation of Rare Failure Event Probability,” J. Eng. Mech., (published ahead of print).
Li, J. , Li, J. , and Xiu, D. , 2011, “ An Efficient Surrogate-Based Method for Computing Rare Failure Probability,” J. Comput. Phys., 230(24), pp. 8683–8697. [CrossRef]
Hua, B. , Bie, Z. , Au, S. K. , Li, W. , and Wang, X. , 2015, “ Extracting Rare Failure Events in Composite System Reliability Evaluation Via Subset Simulation,” IEEE Trans. Power Syst., 30(2), pp. 753–762. [CrossRef]
Siu, N. O. , and Kelly, D. L. , 1998, “ Bayesian Parameter Estimation in Probabilistic Risk Assessment,” Reliab. Eng. Syst. Saf., 62(1–2), pp. 89–116. [CrossRef]
Kelly, D. L. , and Smith, C. L. , 2009, “ Bayesian Inference in Probabilistic Risk Assessment—The Current State of the Art,” Reliab. Eng. Syst. Saf., 94(2), pp. 628–643. [CrossRef]
Yan, Z. , and Haimes, Y. Y. , 2010, “ Cross-Classified Hierarchical Bayesian Models for Risk-Based Analysis of Complex Systems Under Sparse Data,” Reliab. Eng. Syst. Saf., 95(7), pp. 764–776. [CrossRef]
Kelly, D. , and Smith, C. , 2011, Bayesian Inference for Probabilistic Risk Assessment: A Practitioner's Guidebook, Springer-Verlag, London.
Khakzad, N. , Khan, F. , and Paltrinieri, N. , 2014, “ On the Application of Near Accident Data to Risk Analysis of Major Accidents,” Reliab. Eng. Syst. Saf., 126, pp. 116–125. [CrossRef]
Yang, M. , Khan, F. I. , and Lye, L. , 2013, “ Precursor-Based Hierarchical Bayesian Approach for Rare Event Frequency Estimation: A Case of Oil Spill Accidents,” Process Saf. Environ. Prot., 91(5), pp. 333–342. [CrossRef]
Yang, M. , Khan, F. , Lye, L. , and Amyotte, P. , 2015, “ Risk Assessment of Rare Events,” Process Saf. Environ. Prot., 98, pp. 102–108. [CrossRef]
Khakzad, N. , Khakzad, S. , and Khan, F. , 2014, “ Probabilistic Risk Assessment of Major Accidents: Application to Offshore Blowouts in the Gulf of Mexico,” Nat. Hazards, 74(3), pp. 1759–1771. [CrossRef]
Apostolakis, G. , 1982, “ Data Analysis in Risk Assessments,” Nucl. Eng. Des., 71(3), pp. 375–381. [CrossRef]
Khakzad, N. , Khan, F. , and Amyotte, P. , 2012, “ Dynamic Risk Analysis Using Bow-Tie Approach,” Reliab. Eng. Syst. Saf., 104, pp. 36–44. [CrossRef]
Abimbola, M. , Khan, F. , and Khakzad, N. , 2014, “ Dynamic Safety Risk Analysis of Offshore Drilling,” J. Loss Prev. Process Ind., 30, pp. 74–85. [CrossRef]
Khakzad, N. , Khan, F. , and Amyotte, P. , 2013, “ Dynamic Safety Analysis of Process Systems by Mapping Bow-Tie Into Bayesian Network,” Process Saf. Environ. Prot., 91(1), pp. 46–53. [CrossRef]
Bobbio, A. , Portinale, L. , Minichino, M. , and Ciancamerla, E. , 2001, “ Improving the Analysis of Dependable Systems by Mapping Fault Trees Into Bayesian Networks,” Reliab. Eng. Syst. Saf., 71(3), pp. 249–260. [CrossRef]
Boudali, H. , and Dugan, J. B. , 2005, “ A Discrete-Time Bayesian Network Reliability Modeling and Analysis Framework,” Reliab. Eng. Syst. Saf., 87(3), pp. 337–349. [CrossRef]
Marquez, D. , Neil, M. , and Fenton, N. , 2010, “ Improved Reliability Modeling Using Bayesian Networks and Dynamic Discretization,” Reliab. Eng. Syst. Saf., 95(4), pp. 412–425. [CrossRef]
Bearfield, G. , and Marsh, W. , 2005, “ Generalising Event Trees Using Bayesian Networks With a Case Study of Train Derailment,” International Conference on Computer Safety, Reliability, and Security, (SAFECOMP), Fredrikstad, Norway, Sept. 28–30, pp. 52–66.
Unnikrishnan, G. , and Shrihari, N. A. , 2014, “ Application of Bayesian Methods to Event Trees With Case Studies,” Theory Appl., 9, pp. 32–45.
Lunn, D. , Spiegelhalter, D. , Thomas, A. , and Best, N. , 2009, “ The BUGS Project: Evolution, Critique and Future Directions,” Stat. Med., 28(25), pp. 3049–3067. [CrossRef] [PubMed]
Robert, C. , and Ntzoufras, I. , 2012, Bayesian Modeling Using WinBUGS, Wiley, Hoboken, NJ.
Gunawan, I. , 2014, “ Introduction to Reliability Engineering,” Fundamentals of Reliability Engineering: Applications in Multistage Interconnection Networks, Wiley, New York, pp. 1–10.
Crowl, D. A. , and Louvar, J. F. , 2001, Chemical Process Safety: Fundamentals With Applications, Prentice Hall, Upper Saddle River, NJ.
Nielsen, T. D. , and Jensen, F. V. , 2009, Bayesian Networks and Decision Graphs, Springer Science and Business Media, New York.
Khakzad, N. , Khan, F. , and Amyotte, P. , 2013, “ Quantitative Risk Analysis of Offshore Drilling Operations: A Bayesian Approach,” Saf. Sci., 57, pp. 108–117. [CrossRef]
Torres-Toledano, J. G. , and Sucar, L. E. , 1998, “ Bayesian Networks for Reliability Analysis of Complex Systems,” Ibero-American Conference on Artificial Intelligence (IBERAMIA), Lisbon, Portugal, Oct. 5–9, pp. 195–206.
Barbini, E. , Manzi, P. , and Barbini, P. , 2013, “ Bayesian Approach in Medicine and Health Management,” Current Topics in Public Health, InTech, Rijeka, Croatia, pp. 18–35.
Przytula, K. W. , and Thompson, D. , 2000, “ Construction of Bayesian Networks for Diagnostics,” IEEE Aerospace Conference (AERO), Big Sky, MT, Mar. 18–25, pp. 193–200.
Meyer, M. A. , and Booker, J. M. , 2001, Eliciting and Analyzing Expert Judgment: A Practical Guide, Society for Industrial and Applied Mathematics, Philadelphia, PA.
Liu, Z. , 2011, “ Analytical and Numerical Analysis of Iceberg Collisions With Ship Structures,” Doctoral thesis, Norwegian University of Science and Technology, Trondheim, Norway.
Li, S. , Meng, Q. , and Qu, X. , 2012, “ An Overview of Maritime Waterway Quantitative Risk Assessment Models,” Risk Anal., 32(3), pp. 496–512. [CrossRef] [PubMed]
Tangborn, A. , Kan, S. , and Tangborn, W. , 1998, “ Calculation of the Size of the Iceberg Struck by the Oil Tanker Overseas Ohio,” 14th IAHR Symposium on Ice, Potsdam, NY, July 27–31, pp. 237–241.
Antao, P. , and Soares, C. G. , 2006, “ Fault-Tree Models of Accident Scenarios of RoPax Vessels,” Int. J. Autom. Comput., 3(2), pp. 107–116. [CrossRef]
Gramling, R. , and Freudenburg, W. R. , 1992, “ The Exxon Valdez Oil Spill in the Context of U.S. Petroleum Politics,” Ind. Crisis Q., 6(3), pp. 175–196.
Clumpner, C., and Callahan, B., 2014, “ Optimizing the Value of Near Misses in Wildlife Response Preparedness: The Kulluk Incident,” International Oil Spill Conference (IOSC), Savannah, GA, May 5–8, pp. 2288–2294.
Christou, M., and Konstantinidou, M., 2012, “ Safety of Offshore Oil and Gas Operations: Lessons From Past Accident Analysis,” Joint Research Center, Ispra, Italy, Report No. EUR 25646 EN.
Assael, M. J. , and Kakosimos, K. E. , 2010, Fires, Explosions, and Toxic Gas Dispersions, CRC Press, Boca Raton, FL.
Patè-Cornell, M. E., 1993, “ Learning From the Piper Alpha Accident: A Postmortem Analysis of Technical and Organizational Factors,” Risk Analysis, 13(2), pp. 215–232.

Figures

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Fig. 1

Predictive posterior distribution for the probability of failure

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Fig. 2

Simple example of BN with four nodes [30]

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Fig. 3

Proposed methodology framework

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Fig. 4

FT for ship-iceberg collision

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Fig. 5

BN for ship-iceberg collision

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Fig. 6

Posterior predictive distributions for the basic events

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Fig. 7

BN for ship-iceberg collision

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Fig. 8

Abductive reasoning for ship-iceberg collision

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Fig. 10

BN for platform grounding

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Fig. 9

ET for grounding during a tow mission

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Fig. 11

Posterior predictive distributions for the initial event and safety barriers

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Fig. 12

BN for platform grounding

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Fig. 13

Abductive reasoning for platform grounding

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Fig. 14

Bowtie modeling for platform fire and explosion

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Fig. 15

BN for platform fire and explosion

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Fig. 16

Posterior predictive distributions for the basic events

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Fig. 17

Posterior predictive distribution for the safety barriers occurrence probabilities

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Fig. 18

BN for platform fire and explosion

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Fig. 19

Abductive reasoning for platform fire and explosion

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