TY - GEN
T1 - Quantitative risk assessment using hybrid causal logic model
AU - Wang, Yan Fu
AU - Xie, Min
AU - Roohi, Shahrzad Faghih
PY - 2011
Y1 - 2011
N2 - This paper presents a hybrid causal logic model, which integrates the traditional Quantitative Risk Assessment (QRA) models with Bayesian Network (BN) incorporating human and organizational factors. The multi-phase model allows different risk assessment methods to be applied to different parts. In the first phase, Event Tree (ET) defines the base scenarios for the source of risk issues. In the second phase, Fault Tree (FT) is used to model the factors how to contributing to the final failures. BN comprise the third phase, which extends the causal chain of basic events to potential human and organizational roots and provide a more precise quantitative links between the event nodes. The new model integrates the power of typical QRA for modeling deterministic causal paths with the flexibility of BN for modeling non-deterministic cause-effect relationships. The integration algorithm is demonstrated on an offshore fire case study. It clearly shows the new model is more flexible and useful than traditional QRA models.
AB - This paper presents a hybrid causal logic model, which integrates the traditional Quantitative Risk Assessment (QRA) models with Bayesian Network (BN) incorporating human and organizational factors. The multi-phase model allows different risk assessment methods to be applied to different parts. In the first phase, Event Tree (ET) defines the base scenarios for the source of risk issues. In the second phase, Fault Tree (FT) is used to model the factors how to contributing to the final failures. BN comprise the third phase, which extends the causal chain of basic events to potential human and organizational roots and provide a more precise quantitative links between the event nodes. The new model integrates the power of typical QRA for modeling deterministic causal paths with the flexibility of BN for modeling non-deterministic cause-effect relationships. The integration algorithm is demonstrated on an offshore fire case study. It clearly shows the new model is more flexible and useful than traditional QRA models.
KW - Bayesian network
KW - Fault tree
KW - Human and organizational factors
KW - Hybrid causal logic
KW - Quantitative risk assessment
UR - https://www.scopus.com/pages/publications/80051974235
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80051974235&origin=recordpage
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781617828478
VL - 1
SP - 121
EP - 133
BT - International Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, PSA 2011
T2 - International Topical Meeting on Probabilistic Safety Assessment and Analysis 2011, PSA 2011
Y2 - 13 March 2011 through 17 March 2011
ER -