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Lookup NU author(s): Professor Mohamad Kassem
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© 2020 Elsevier B.V. BIM for Facilities Management (BIM for FM) is a relatively new and growing topic of inquiry aiming to fulfil the informational needs of the operational phase of assets within increasingly digitalised project workflows. Research into the management of structured (i.e. graphical and non-graphical) and unstructured data (i.e. documents) has largely focused on design and construction phases. Information management in facilities management and maintenance is still challenged by the lack of a structured framework that can simultaneously fulfil these three capabilities: (1) the delivery of information models (i.e. Asset Information Models) from distributed data sources; (2) the validation of these information models against the requirements; and (3) the use of their information in facilities management (e.g. operation and maintenance). This research aims to develop and test a framework and a prototype Common Data Environment (CDE) to achieve these three capabilities. The framework and the developed CDE are entirely based on use open standards and integration of existing technologies. A requirements model, underpinning the framework and the CDE was developed during three iterative stages of interviews –in line with the adopted Grounded Theory and Design Science Research methodologies– with industry experts and through a three-stage coding process at each iteration. The framework and the CDE were tested in pilot demonstrations with a use case focused on preventive and reactive maintenance. The testing demonstrated that the implementation of ‘BIM for FM’ processes is feasible with the proposed framework and CDE relying entirely on open standards and existing technologies. Some additional requirements for BIM for FM processes were also identified during the verification sessions with industry and are proposed for future research.
Author(s): Patacas J, Dawood N, Kassem M
Publication type: Article
Publication status: Published
Journal: Automation in Construction
Year: 2020
Volume: 120
Print publication date: 01/12/2020
Online publication date: 11/08/2020
Acceptance date: 15/07/2020
ISSN (print): 0926-5805
ISSN (electronic): 1872-7891
Publisher: Elsevier BV
URL: https://doi.org/10.1016/j.autcon.2020.103366
DOI: 10.1016/j.autcon.2020.103366
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