Enhanced Automated Quantity Take-Off in Building Information Modeling Enhanced automated quantity take-off in building information modeling

Document Type : Article

Authors

1 Department of Civil and Environmental Engineering, University of Utah, Utah, USA

2 - School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran - Tecnosa R and D Centre, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box 1417613131, Iran

3 Tecnosa R and D Centre, School of Civil Engineering, College of Engineering, University of Tehran, Tehran, P.O. Box: 1417613131, Iran

Abstract

Material quantity take-off is a necessary factor in estimating the cost of construction projects; accordingly, fast and precise estimations would better facilitate the overall construction process. In recent years, several Building Information Modeling (BIM) based applications (e.g., Autodesk Revit, Tekla Structure, Autodesk Navisworks Manage, and Solibri Model Checker) have emerged to assist in performing quantity take-off. Quantity take-off measurement using these applications is accurate when the elements length multiplies with their precise section area. Still, the process encounters errors when using element volumes or Industry Foundation Classes (IFC). In this study, the authors examined the embedded quantity take-off feature of these applications for sample steel and reinforced concrete structure and provided precautions in employing BIM properties. Consequently, an automated approach has been applied to facilitate an accurate quantity take-off by using an Application Program Interface (API) extracting information from a Navisworks model as well as database management systems. A case study is subsequently presented to demonstrate and validate the proposed methodology.

Keywords

References

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Scientia Iranica
Volume 29, Issue 3
Transactions on Civil Engineering (A)
May and June 2022
Pages 1024-1037

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History

  • Receive Date: 25 August 2020
  • Revise Date: 17 February 2021
  • Accept Date: 13 September 2021

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