Symptom-based context quantification for dynamic accident analysis

Abstract

Accident analysis requires development and improvement of the concepts, methods and tools for support of human performance, creation of resilient technology and implementation of reliable organization for effective decision-making and management. The decision-maker in any situation is a human and her/his successful or erroneous actions are always a meaningful part of the accident investigation. According to current understanding of accidents, human failure events are more often caused by work interactions and conditions than by people. That is why in human reliability assessment (HRA) these investigations are unavailing without a comprehensive description and evaluation of the context in which the socio-technical systems interact and keep balance. The paper presents the capacities of the Performance Evaluation of Teamwork (PET) method and its procedure for context quantification based on the human, organization and technology symptoms during accidents for monitoring, evaluation and analysis of their progression. It explains how this method assesses accident context potential as probability on the macroscopic socio-technical system level. The intermediate use of symptoms for the quantification of the context, as opposed to direct description and use of the causes, gives better opportunities for the dynamic, systematic identification and quantitative interpretation of these causes in complex systems and high reliable organizations. The context quantification is exemplified by thermo-hydraulic simulation of a severe nuclear accident: a long-term total blackout.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.