An integration of human factors into quantitative risk analysis: A proof of principle

Quantitative Risk Analysis (QRA) is a standard and mandatory tool in some high-risk industries (such as the on-and offshore exploration and production and chemical industry). Presently, the human factor component is often estimated using expert opinions. In practice, experts may use heuristics such as assuming a failure probability of one in ten or one in a hundred. This practice is indicative of a lack of implementation of existing knowledge concerning human error likelihood and human reliability assessment. In this paper, we provide a proof of principle for the quantification of the human factor in QRAs, which we call QRA+. For seamless integration of existing qualitative and quantitative knowledge, we made use of a Bayesian Belief Network. The resulting model provides an integrated and potentially more accurate estimation of the failure probabilities for both technological and human factors and the uncertainty surrounding such probability estimates. Furthermore, it gives insight into the origin of failure probabilities and the interaction of components. This will make it easier for companies to identify which parameters they need to influence to optimise their management of risk. identified which may pose a hazard. Subsequently, hazard analysis makes use of failure data derived from equivalent operation and component failure rates which relate to components of a similar type that have been subjected to a similar environment. In the consequence analysis, a model is created of the system response to a particular fault or hazard. During the risk analysis and summation stage, fault and event trees are combined across all consequence categories and risks are summed. The goal of a QRA is to develop an appropriate model to determine the numerical risk to different exposed groups. See Pasman and Reniers, 2014 for a more elaborate overview of the arrival of the method