Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.733840
Title: Learning from accidents : human errors, preventive design and risk mitigation
Author: Moura, Raphael N.
ISNI:       0000 0004 6495 9296
Awarding Body: University of Liverpool
Current Institution: University of Liverpool
Date of Award: 2017
Availability of Full Text:
Access from EThOS:
Access from Institution:
Abstract:
Recent technological accidents, which resulted in severe material losses, multiple fatalities and environmental damage, were deeply associated with human errors. Direct human actions or flawed decision-making processes have been increasingly tied to devastating consequences, raising major concerns regarding industry's ability to control risks. The most common approach to estimate the probability of human errors and weigh their impact to the overall risk is the application of a suitable Human Reliability Analysis (HRA) technique. However, uncertainties associated with behavioural aspects of humans dealing with advanced technology in complex organisational arrangements turn this type of evaluation into a challenging task to perform, an issue that brings difficulties to ensure sound predictions for human actions when interfacing with complex systems. Consequently, the development of innovative strategies to overcome existing limitations to understand how these sociotechnical systems could fail is of paramount importance, particularly the intricate relationship between humans, technology and organisations. This PhD research project is devoted to approach this multidisciplinary problem in a systematic manner, providing means to recognise and tackle surrounding factors and tendencies that could lead to the manifestation of human errors, improving risk communication and decision making-processes and ultimately increasing confidence in safety studies. The initial part of this thesis comprises a large-scale analysis of human errors identified during major accidents in high-technology systems. Detailed accident accounts were collected from regulators, independent investigation panels, government bodies, insurance companies and industry experts. The raw data is then scrutinised and classified under a common framework, resulting in a novel and comprehensive major-accident dataset, the Multi-attribute Technological Accidents Dataset (MATA-D). The second stage applies advanced data analytic techniques to gain further insight into the conditions leading to the genesis and perpetuation of errors, essentially making use of cluster analysis and classification. The application of different clustering methods reveals common patterns among accidents, and the usage of an artificial neural network approach (self-organising maps) algorithm allows the translation of the multidimensional data into visual representations (2-D maps) of accidents' contributing factors. This stage generates appropriate information to increase the understanding of these sociotechnical systems, to overcome barriers to communicate risk and to enable a wide-ranging 'learning from accidents' process. The final part of the research project builds upon the self-organising maps algorithm output, focusing on a deeper interpretation of specific clusters to disclose strategies to minimise human factors weaknesses and reduce major accidents. An important practical implication suggested by the data analysis is that human errors, in most of the cases, constitute reasonable responses to disruptive transactions between the technology and the organisation, which impact human cognitive functions. Accordingly, the recognition that human errors are mistakenly seen as root-causes of major accidents and the examination of these interaction problems from a new perspective provided an effective way to recognise hazards and tackle major risks, delivering realistic proposals to improve design, decision-making processes and to build trust in safety assessments.
Supervisor: Patelli, E. ; Beer, M. ; Lewis, J. ; Knoll, F. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.733840  DOI:
Share: