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    Risk assessment in the maintenance of offshore caisson operations

    Access Status
    Fulltext not available
    Authors
    Tokede, O.
    Ayinla, A.
    Love, Peter
    Ahiaga-Dagbui, D.
    Date
    2019
    Type
    Journal Article
    
    Metadata
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    Citation
    Tokede, O. and Ayinla, A. and Love, P. and Ahiaga-Dagbui, D. 2019. Risk assessment in the maintenance of offshore caisson operations. Structure and Infrastructure Engineering. 15 (3): pp. 376-391.
    Source Title
    Structure and Infrastructure Engineering
    DOI
    10.1080/15732479.2018.1547769
    ISSN
    1573-2479
    School
    School of Civil and Mechanical Engineering (CME)
    URI
    http://hdl.handle.net/20.500.11937/74920
    Collection
    • Curtin Research Publications
    Abstract

    Undertaking maintenance of offshore caissons is a pervasive challenge that involves an array of risks and uncertainties. Despite the adoption of standardised and routine maintenance procedures that are subject to on-going improvements, the rate of failure in offshore caissons remains relatively high. To manage the effect of risk and uncertainties associated with the maintenance of offshore caissons, expert judgment can be drawn upon to better understand and capture the issues that contribute to their failure. Using a case study of an offshore hydrocarbon discharge caisson in the North Sea of the United Kingdom, expert judgements from industry practitioners are obtained using the Delphi Interview and Analytic Hierarchy Process (AHP) technique to rank 14 possible recovery options. Sensitivity analysis is used to ascertain the suitability of each of the options. Results reveal that the Delphi technique employed provided a robust means to garner invaluable insights from experts about the issues associated with caisson failure. The corollary is the development of a framework that serves as a point of reference to identify and assess the risks in offshore caisson maintenance operations. This framework can also be used in conjunction with existing probabilistic methods to improve the effectiveness of decision-making during operations and maintenance.

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