A dependence assessment method based on quantum model of mass function in human reliability analysis

Human reliability analysis (HRA) has garnered widespread attention in high-reliability-demand fields such as the nuclear industry. The dependence assessment among human failure events (HFEs) constitutes a crucial component of HRA research, as it enhances the accuracy of HRA outcomes and contributes to reducing human error probabilities. This paper proposes a novel method based on the quantum model of mass function (QMMF) to address dependence assessment in HRA under uncertain dynamic scenarios. Firstly, dependence influencing factors are identified and their basic belief assignments (BBAs) are constructed based on expert evaluations. Then, a time correction model is developed to generate time-corrected BBAs, upon which the QMMF is applied to reconstruct dynamic factor BBAs. Finally, the conditional human error probability (CHEP) is calculated through the fusion of reconstructed dynamic factor BBAs and static factor BBAs. The proposed method, grounded in quantum evidence theory, assigns the physical meaning of “time” to the “phase angle” variable, enabling flexible expression of evidence evolution over time while maintaining solid theoretical foundations and compatibility. Additionally, the method allows adjustment of temporal correction intensity for dynamic factors by modifying the weight distribution in time-corrected BBAs. Case study results demonstrate that the proposed method can yield more accurate and rational outcomes.