RISA risa˙1854 Dispatch: May 29, 2012 CE: AFL Journal MSP No. No. of pages: 15 PE: Martha 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 Risk Analysis DOI: 10.1111/j.1539-6924.2012.01854.x Domino Effect Analysis Using Bayesian Networks Nima Khakzad, 1 Faisal Khan, 1, * Paul Amyotte, 2 and Valerio Cozzani 3 A new methodology is introduced based on Bayesian network both to model domino effect propagation pattern and to estimate the domino effect probability at different levels. The flexible structure and the unique modeling techniques offered by Bayesian network make it possible to analyze domino effects through a probabilistic framework, considering synergis- tic effects, noisy probabilities, and common cause failures. Further, the uncertainties and the complex interactions among the domino effect components are captured using Bayesian net- work. The probabilities of events are updated in the light of new information, and the most probable path of the domino effect is determined on the basis of the new data gathered. This study shows how probability updating helps to update the domino effect model either qualita- tively or quantitatively. The methodology is applied to a hypothetical example and also to an earlier-studied case study. These examples accentuate the effectiveness of Bayesian network in modeling domino effects in processing facility. KEY WORDS: Bayesian network; domino effect; risk analysis 1. INTRODUCTION Domino effects or chains of accidents in which an accident in a unit propagates into nearby units have recently been recognized as a priority issue among the risk and safety community experts (e.g., see the requirements of the EU Seveso-II Directive (1) and its amendments). This is partly owing to the fact that to- day’s ever growing industries are complex and con- gested by dense pipelines, process equipment, and storage tanks most of which contain or transport haz- ardous material. Thus, it is not unlikely for a primary Q1 event to evolve to a much more severe sequence of Q2 secondary accidents as nearby equipment items or units are involved in the accident by means of heat, 1 Process Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL, Canada A1B 3×5. 2 Department of Process Engineering and Applied Science, Dalhousie University, Halifax, Nova Scotia, Canada B3J 2×4. 3 Dipartimento di Ingegneria Chimica, Mineraria e delle Technolo- gie Ambientali, Alma Mater Studiorum, Universit ` a di Bologna, via Terracini 28, Bologna, Italy. ∗ Address correspondence to Faisal Khan; fikhan@mun.ca. overpressure, and/or by the impact of explosion- induced airborne fragments. Although a remarkable progress in the risk and safety analysis of individual accident scenarios, lim- ited to a single unit, has been achieved in recent years, domino accidents have gained less attention in the context of quantitative risk assessment (QRA) both because of their lower probability and higher complexity. However, frequent violent domino acci- dents took place in the last decade (2,3) such as that occurred in the BP Texas City refinery, where a va- por cloud explosion (VCE) was followed by sev- eral other fires and explosions. (4) These severe events have urgently raised the demand for consideration of domino scenarios in quantitative risk analysis and safety reports. Accordingly, the study of domino effects in the literature has primarily been focused either on dam- age probability or on domino effect frequency es- timation. Damage probability, alternatively known as escalation probability, has been estimated using distance-based models, (5) threshold values, (6) pro- bit models, (7-9) combination of threshold values and 1 0272-4332/12/0100-0001$22.00/1 C  2012 Society for Risk Analysis