ABSTRACT
Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions.TheAccident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) is one such dynamic method (Mosleh & Chang, 2004). A major goal of the ADS-IDAC project is the prediction of situational contexts that might lead to human error events, particularly knowledge driven errors of commission. The ADS-IDAC environment couples a thermal-hydraulic model with an operations crew cognitive model to permit the dynamic simulation of operator performance during nuclear power plant accidents. ADS-IDAC generates a discrete dynamic event tree using simple branching rules to model variations in crew responses.A significant advantage of theADSIDAC approach is the ability to directly assess the impact of operator actions on key plant parameters and more realistically determine the safety significance of human errors.
