IFC-Based Algorithms for Automated Quantity Takeoff from Architectural Model: Case Study on Residential Development Project
Publication: Journal of Architectural Engineering
Volume 29, Issue 4
Abstract
The estimation of construction quantities is a critical aspect of any construction project, and accuracy in the quantity takeoff (QTO) process is an integral part in achieving project success. Accuracy can be improved by automating the QTO process. However, one of the gaps in automated QTO is found to be the lack of a platform-ignorant computing algorithm regarding different building information modeling (BIM) authoring tools. Available BIM-based commercial cost estimation software programs such as Autodesk Naviswork, Autodesk QTO, iTWO CostX, Innovaya Visual QTO, ConstrucSim, and so forth, are limited in terms of full automation, largely because full interoperability across all the different applications has not yet been achieved. This paper explores interoperable QTO algorithms using industry foundation classes (IFC)-based BIM. To evaluate the IFC-based QTO algorithms, a case study was conducted on a residential development project in Kalamazoo, MI. Comparing with commercial software used in practice, the IFC-based QTO algorithms showed consistent results, whereas were more powerful regarding their independence from BIM authoring tools in extracting the volumetric and areal quantities of building components. The IFC-based QTO algorithms processed the different types of building element configurations in the residential development project smoothly. Furthermore, the QTO algorithms are expected to reduce the manual input required in generating QTO through interfacing with IFC data which is the ISO international standard for BIM.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors would like to thank the National Science Foundation (NSF). This material is based upon work supported by NSF under Grant No. 1745374. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.
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Journal of Architectural Engineering
Volume 29 • Issue 4 • December 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 9, 2022
Accepted: May 16, 2023
Published online: Sep 15, 2023
Published in print: Dec 1, 2023
Discussion open until: Feb 15, 2024
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