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Artificial Intelligence in Cloud Computing technology in the Construction industry: a bibliometric and systematic review

Autor(en): ORCID
ORCID
ORCID
ORCID
ORCID
Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Journal of Information Technology in Construction, , v. 29
Seite(n): 480-502
DOI: 10.36680/j.itcon.2024.022
Abstrakt:

The integration and impact of artificial intelligence (AI) and cloud computing (CC) technology in the construction industry (CI) would support their implementation process and adoption. However, there is a lack of research in the extant literature, and recent advances in this field have not been explored. As such, the key research question focuses on the extent of existing literature, main research hotspots, and recent advances (i.e., research gaps and future directions) in AI in CC in the CI. To address this research question, this study aims to conduct a state-of-the-art review of AI in CC in the CI by providing a qualitative discussion of the main research hotspots, research gaps, and future research directions. This review study used a four-step bibliometric-systematic review approach consisting of literature search, literature screening, science mapping analysis, and qualitative dis-cussion. The results found four main research hotspots, namely (1) construction project performance indicators, (2) data analysis and visualization, (3) construction quality control and safety, and (4) construction energy efficiency. These findings would provide valuable insights for scholars and practitioners seeking to understand and integrate AI and CC technology applications in the CI. This review study will lay a better foundation for future developments in construction project management processes, data-sharing protocols, real-time safety monitoring, and ethical implications of AI and CC technologies.

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.36680/j.itcon.2024.022.
  • Über diese
    Datenseite
  • Reference-ID
    10798141
  • Veröffentlicht am:
    01.09.2024
  • Geändert am:
    01.09.2024
 
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