A Cognitive-Mechanistic Human Reliability Analysis Framework: A Nuclear Power Plant Case Study
This work addresses the need for more credible and computationally efficient human reliability analysis in nuclear power plant operations, though it appears incremental as it enhances an existing methodology rather than introducing a new paradigm.
This study tackled the problem of traditional human reliability analysis methods overlooking cognitive underpinnings and being impractical for advanced nuclear power plants by proposing a cognitive-mechanistic framework (COGMIF) that integrates an ACT-R-based human digital twin with TimeGAN-augmented simulation, resulting in scalable, mechanism-informed estimation of human error probabilities with demonstrated robustness and practical advantages.
Traditional human reliability analysis (HRA) methods, such as IDHEAS-ECA, rely on expert judgment and empirical rules that often overlook the cognitive underpinnings of human error. Moreover, conducting human-in-the-loop experiments for advanced nuclear power plants is increasingly impractical due to novel interfaces and limited operational data. This study proposes a cognitive-mechanistic framework (COGMIF) that enhances the IDHEAS-ECA methodology by integrating an ACT-R-based human digital twin (HDT) with TimeGAN-augmented simulation. The ACT-R model simulates operator cognition, including memory retrieval, goal-directed procedural reasoning, and perceptual-motor execution, under high-fidelity scenarios derived from a high-temperature gas-cooled reactor (HTGR) simulator. To overcome the resource constraints of large-scale cognitive modeling, TimeGAN is trained on ACT-R-generated time-series data to produce high-fidelity synthetic operator behavior datasets. These simulations are then used to drive IDHEAS-ECA assessments, enabling scalable, mechanism-informed estimation of human error probabilities (HEPs). Comparative analyses with SPAR-H and sensitivity assessments demonstrate the robustness and practical advantages of the proposed COGMIF. Finally, procedural features are mapped onto a Bayesian network to quantify the influence of contributing factors, revealing key drivers of operational risk. This work offers a credible and computationally efficient pathway to integrate cognitive theory into industrial HRA practices.