A structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations

Abstract

Resilience engineering is in the ascendant across diverse disciplines and research fields. Researchers and engineers work to create resilient systems and emphasize human elements for providing resilience. It prompts a new paradigm for viewing human performance in socio-technical systems, where variability is regarded as a measure of the potential for resilient human performance. This paper proposes a structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations. In the structured framework, a context-based Bayesian network model is developed to evaluate the internal and external variability of operator functions. For this purpose, a taxonomy of variability shaping factors is developed for operations at sea involving human-automation collaboration. Further , this study proposes integrating the functional resonance analysis method (FRAM) and fuzzy cognitive maps (FCM) to model the dynamic interaction of operator functions and simulate the coupling of operator variability in performing safety-critical operations. The integration augments both methods to tackle human performance modeling from both qualitative and quantitative aspects. A case study is presented in the context of offshore dynamic positioning operations to demonstrate the applicability of the approach. The results confirm that it provides a useful tool for managing operator variability and resilient performance to improve human-related safety.