The previous paragraphs described the development of HRA methods from their origin to the last generation. In this generation, there are literally dozens of HRA methods from which to choose. However, many difficulties remain: Most of the techniques, in fact, do not have solid empirical bases and are essentially static, unable to capture the dynamics of an accident in progress or general human behaviour. Therefore, the limitations of current methods are natural starting point for future studies and work.
As described in this paper, the path has been paved for the next generation of HRA through simulation and modelling. The human performance simulation reveals important new data sources and possibilities for exploring human reliability, but there are significant challenges to be resolved, both as regards the dynamic nature of HRA versus the mostly static nature of conventional first and second generation HRA methods both for the weakness of the simulators themselves1.The simulator PROCOS, in particular, requires further optimisation, as evidenced by the same Trucco and Leva in2. Additionally, in its development, some sensitivity analysis has still to be performed on the main elements on which the simulator is based – blocks of the flow chart, decision block criteria, PSF importance – to test the robustness of the method3. Mosleh and Chang, instead, are conducting their studies to eliminate the weak points of IDAC as outlined in4. First of all, is development of an operator behaviour model more comprehensive and realistic; it can be used not only for nuclear power plants but also for more general applications. This is a subject of current research effort by the authors.
Many researchers are moving to the integration of their studies with those of other researchers to optimise HRA techniques. Some future plans include, for example, extending AHP–SLIM into other HRAs methods to exploit its performance5. The method proposed by De Ambroggi and Trucco for modelling and assessment of dependent performance shaping factors through analytic network process6 is moving towards better identification of dependencies among PSFs using the simulator PROCOS or Bayesian networks.
Bayesian networks (BN) represent, in particular, an important field of study for future developments. Many experts are studying these networks with the aim of exploiting the features and properties in the techniques HRA7, 8. Bayesian methods are appealing since they can combine prior assumptions of human error probability (i.e. based on expert judgement) with available human performance data. Some results already show that the combination of the model conceptual causal model with a BN approach can not only qualitatively model the causal relationships between organisational factors and human reliability but can also quantitatively measure human operational reliability, identifying the most likely root causes or prioritisation of root causes of human error9. This is a subject of current research effort by the authors of the IDAC model as an alternative way for calculating branch probability and representing PIF states as opposed to the current method; in the current method, branch probabilities are dependent on the branch scores that are calculated based on explicit equations reflecting the causal model built, based on the influence of PIFs and other rules of behaviour.
Additional research and efforts are related to the performance shaping factors (PSFs). Currently, there are more than a dozen HRA methods that use PIFs/PSFs, but there is no standard set of PIFs used among methods. The performance shaping factors at present are not defined specifically enough to ensure consistent interpretation of similar PIFs across methods. There are few rules governing the creation, definition, and usage of PIF sets. Within the HRA community, there is a widely acknowledged need for an improved HRA method with a more robust scientific basis. Currently, there are several international efforts to collect human performance data that can be used to improve HRA10 .
Of course, many studies that are being carried out are aimed at improving the application of HRA methods in complex environments, such as nuclear power plants. The methods already developed in these areas are adapting to different situations by expanding their scope.
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