BIM-based quantity takeoff: Current state and future opportunities
Building Information Modeling (BIM) has a significant role in the digital revolution of the AEC industry. The adoption of this technology has been increasing rapidly, enabling the industry to facilitate several construction tasks and enhance collaboration among stakeholders. Despite the growth of BI...
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Veröffentlicht in: | Automation in construction 2024-09, Vol.165, p.105549, Article 105549 |
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Sprache: | eng |
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Zusammenfassung: | Building Information Modeling (BIM) has a significant role in the digital revolution of the AEC industry. The adoption of this technology has been increasing rapidly, enabling the industry to facilitate several construction tasks and enhance collaboration among stakeholders. Despite the growth of BIM-related publications, no systematic review has been conducted on the BIM-Based Quantity Takeoff (QTO) approach. This paper uses the PRISMA method to present a systematic review of 52 selected journal articles. Scopus® and Web of Science® databases were employed for literature retrieval to investigate the benefits, challenges, and future opportunities of the BIM-based QTO approach. Bibliometric analysis identified key benefits and challenges in management, professional skills, software functionalities, and implementation cost. Future opportunities, including software development and upskill training for seasoned professionals, were identified from the records and the research gaps. The study contributes a conceptual model for a comprehensive and sustainable implementation approach of BIM-based QTO.
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•Implementation techniques and upskilling are the current frequent trends of 5D BIM.•Leveraging 5D BIM approach enhances productivity, accuracy, and completeness.•Limited professionals' skills in BIM are the biggest challenge on 5D BIM adoption.•BIM databases are beneficial for 5D BIM practices, API developments, LCC and LCA.•Natural Language Processing has a strong relationship with the 5D BIM revolution. |
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ISSN: | 0926-5805 |
DOI: | 10.1016/j.autcon.2024.105549 |