Groth, K. M., Wang, C., Zhu, D., Mosleh, A. Methodology and software platform for multi-layer causal modeling (Inproceedings) Proceedings of the European Society for Reliability Annual Meeting (ESREL 2008), Valencia, Spain, 2008.

BibTeX

@inproceedings{GrothESREL2008,
title = {Methodology and software platform for multi-layer causal modeling},
author = {K M Groth and C Wang and D Zhu and A Mosleh},
year = {2008},
date = {2008-09-01},
booktitle = {Proceedings of the European Society for Reliability Annual Meeting (ESREL 2008)},
address = {Valencia, Spain},
abstract = {This paper introduces an integrated framework and software platform that uses a three layer approach to modeling complex systems. The multi-layer PRA approach implemented in IRIS (Integrated Risk Information System) combines the power of Event Sequence Diagrams and Fault Trees for modeling risk scenarios and system risks and hazards, with the flexibility of Bayesian Belief Networks for modeling nondeterministic system components (e.g. human, organizational). The three types of models combined in the IRIS integrated framework form a Hybrid Causal Logic (HCL) model that addresses deterministic and probabilistic elements of systems and quantitatively integrates system dependencies. This paper will describe the HCL algorithm and its implementation in IRIS by use of an example from aviation risk assessment (a risk scenario model of aircraft taking off from the wrong runway.},
keywords = {Bayesian Networks, causal models, Hybrid causal logic, IRIS, Probabilistic risk assessment (PRA), Quantitative risk assessment (QRA), software},
pubstate = {published},
tppubtype = {inproceedings}
}

Abstract

This paper introduces an integrated framework and software platform that uses a three layer approach to modeling complex systems. The multi-layer PRA approach implemented in IRIS (Integrated Risk Information System) combines the power of Event Sequence Diagrams and Fault Trees for modeling risk scenarios and system risks and hazards, with the flexibility of Bayesian Belief Networks for modeling nondeterministic system components (e.g. human, organizational). The three types of models combined in the IRIS integrated framework form a Hybrid Causal Logic (HCL) model that addresses deterministic and probabilistic elements of systems and quantitatively integrates system dependencies. This paper will describe the HCL algorithm and its implementation in IRIS by use of an example from aviation risk assessment (a risk scenario model of aircraft taking off from the wrong runway.