Learning from accidents: Interactions between human factors, technology and organisations as a central element to validate risk studies. (November 2017)

• Record Type: Journal Article
• Authors: Moura, Raphael; Beer, Michael; Patelli, Edoardo; Lewis, John; Knoll, Franz
• Is Part Of: Safety science. Volume 99: Part B (2017)
• Page Start: 196
• Page End: 214
• Publication Date: 2017-11
• Subjects: Risk studies validation -- Learning from accidents -- MATA-D -- Human factors -- Organisations -- Self-organising maps; Industrial accidents -- Periodicals; Accident Prevention -- Periodicals; Safety -- Periodicals; Travail -- Accidents -- Périodiques; 363.11
• DOI: 10.1016/j.ssci.2017.05.001
• Languages: English
• ISSNs: 0925-7535

Abstract

Highlights: A new approach to validate risk studies using information from past major accidents. Uses self-organising maps to disclose significant accident causation patterns. Anticipates complex interactions between humans, technology and organisations. Presents a validation checklist for safety studies based on accident trends. Builds trust by ensuring that past accident lessons will be taken into account. Abstract: Many industries are subjected to major hazards, which are of great concern to stakeholders groups. Accordingly, efforts to control these hazards and manage risks are increasingly made, supported by improved computational capabilities and the application of sophisticated safety and reliability models. Recent events, however, have revealed that apparently rare or seemingly unforeseen scenarios, involving complex interactions between human factors, technologies and organisations, are capable of triggering major catastrophes. The purpose of this work is to enhance stakeholders' trust in risk management by developing a framework to verify if tendencies and patterns observed in major accidents were appropriately contemplated by risk studies. This paper first discusses the main accident theories underpinning major catastrophes. Then, an accident dataset containing contributing factors from major events occurred in high-technology industrial domains serves as basis for the application of a clustering and data mining technique (self-organising maps – SOM), allowing the exploration of accident information gathered from in-depth investigations. Results enabled the disclosure of common patterns in major accidents, leading to the development of an attribute list to validate risk assessment studies to ensure that the influence of human factors, technological issues and organisational aspects was properly taken into account.