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Quantitative human reliability assessment in Marine Engineering Operations

Abujaafar, K M (2012) Quantitative human reliability assessment in Marine Engineering Operations. Doctoral thesis, Liverpool John Moores University.

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Marine engineering operations rely substantially on high degrees of automation and supervisory control. This brings new opportunities as well as the threat of erroneous human actions, which account for 80-90% of marine incidents and accidents. In this respect, shipping environments are extremely vulnerable. As a result, decision makers and stakeholders have zero tolerance for accidents and environmental damage, and require high transparency on safety issues. The aim of this research is to develop a novel quantitative Human Reliability Assessment (HRA) methodology using the Cognitive Reliability and Error Analysis Method (CREAM) in the maritime industry. This work will facilitate risk assessment of human action and its applications in marine engineering operations. The CREAM model demonstrates the dynamic impact of a context on human performance reliability through Contextual Control Model controlling modes (COCOM-CMs). CREAM human action analysis can be carried out through the core functionality of a method, a classification scheme and a cognitive model. However, CREAM has exposed certain practical limitations in its applications especially in the maritime industry, including the large interval presentation of Human Failure Probability (HFP) values and the lack of organisational factors in its classification scheme. All of these limitations stimulate the development of advanced techniques in CREAM as well as illustrate the significant gap between industrial needs and academic research. To address the above need, four phases of research study are proposed. In the first phase, the adequacy of organisation, one of the key Common Performance Conditions (CPCs) in CREAM, is expanded by identifying the associated Performance Influencing Factors (PIFs) and sub-PIFs in a Bayesian Network (BN) for realising the rational quantification of its assessment. In the second phase, the uncertainty treatment methods' BN, Fuzzy Rule Base (FRB) , Fuzzy Set (FS) theory are used to develop new models and techniques' that enable users to quantify HFP and facilitate the identification of possible initiating events or root causes of erroneous human action in marine engineering operations. In the third phase, the uncertainty treatment method's Evidential Reasoning (ER) is used in correlation with the second phase's developed new models and techniques to produce the solutions to conducting quantitative HRA in conditions in which data is unavailable, incomplete or ill-defined. In the fourth phase, the CREAM's prospective assessment and retrospective analysis models are integrated by using the established Multiple Criteria Decision Making (MCDM) method based on, the combination of Analytical Hierarchical Process (AHP), entropy analysis and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). These enable Decision Makers (DMs) to select the best developed Risk Control Option (RCO) in reducing HFP values. The developed methodology addresses human actions in marine engineering operations with the significant potential of reducing HFP, promoting safety culture and facilitating the current Safety Management System (SMS) and maritime regulative frameworks. Consequently, the resilience of marine engineering operations can be further strengthened and appreciated by industrial stakeholders through addressing the requirements of more safety management attention at all levels. Finally, several real case studies are investigated to show end users tangible benefits of the developed models, such as the reduction of the HFPs and optimisation of risk control resources, while validating the algorithms, models, and methods developed in this thesis.

Item Type: Thesis (Doctoral)
Subjects: V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions: Maritime & Mechanical Engineering (merged with Engineering 10 Aug 20)
Date Deposited: 29 Mar 2017 10:09
Last Modified: 03 Sep 2021 23:30
URI: https://researchonline.ljmu.ac.uk/id/eprint/6115
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