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A Systematic Review of Artificial Intelligence Applied to Facility Management in the Building Information Modeling Context and Future Research Directions

Auteur(s): ORCID
ORCID
ORCID
Médium: article de revue
Langue(s): anglais
Publié dans: Buildings, , n. 11, v. 12
Page(s): 1939
DOI: 10.3390/buildings12111939
Abstrait:

Throughout the operation and maintenance (O&M) stage, facility management (FM) teams collect and process data from different sources, often needing to be adequately considered when making future decisions. This data could feed statistical models based on artificial intelligence (AI), thus improving decision-making in FM. Building information modeling (BIM) appears in this context, leveraging how data and information are systematized, enabling structured information and its use. This article addresses the state-of-the-art of using AI techniques applied to FM in the BIM context, analyzing articles between 2012 and 2021 related to this area. It is interesting to note that only from 2018 onwards, there is a substantial increase in these publications, from about 8 publications (2012 to 2017) to 24 publications (2018 to 2021) on average. This growth shows the progressive application of the optimization methods mentioned above, which opens new opportunities for the FM profession. This study contributes to the body of knowledge by highlighting the investigated tendency and gaps in critical areas and their relationship with the research topic. Noteworthy future directions are suggested, directing on (i) data and system integration; (ii) predictive models; (iii) automatic as-built/classification; (iv) internet of things; (v) energy management; and (vi) augmented/virtual reality.

Copyright: © 2022 by the authors; licensee MDPI, Basel, Switzerland.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10700287
  • Publié(e) le:
    10.12.2022
  • Modifié(e) le:
    15.02.2023
 
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