Article

 

Quantitative Analysis of Warnings in Building Information Modeling (BIM) Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/articles/g445cg17t

Descriptions

Attribute NameValues
Creator
Abstract
  • Building Information Modeling (BIM) provides automatic detection of design-related errors by issuing warning messages for potential problems related to model elements. However, if not properly managed, the otherwise useful warning feature of BIM can significantly reduce the speed of model processing and increase the size of models. As the first study of its kind, this study proposes to apply the Pareto analysis to investigate BIM warnings in terms of type and frequency. Based on warning data collected from three California healthcare projects, the analysis revealed that the 15-80 rule applies across the case projects and their design phases—15% of the warning messages are responsible for nearly 80% of the warnings. Two other noteworthy findings include: (1) only the schematic design phase indicates a different Pareto rule of 25-80, as well as warning pattern from other design phases due to its unique purpose; and (2) the decisions of individual design teams are a major variable in the pattern of warning types. Lastly, time estimation for warning corrections is proposed based on learning curve theory to support efficient BIM warning management practices. The results and warning classifications presented in this study are expected to contribute to the design management and modeling practices of design teams involved in large, complex projects.
Resource Type
DOI
Date Available
Date Issued
Citation
  • Lee, H. W., Oh, H., Kim, Y., & Choi, K. (2015). Quantitative analysis of warnings in building information modeling (BIM). Automation in Construction, 51, 23-31. doi:10.1016/j.autcon.2014.12.007
Journal Title
Journal Volume
  • 51
Keyword
Rights Statement
Funding Statement (additional comments about funding)
  • The authors acknowledge that this work was supported by a seed grant from Oregon State University.
Publisher
Peer Reviewed
Language
Replaces
Additional Information
  • description.provenance : Made available in DSpace on 2015-07-27T21:57:54Z (GMT). No. of bitstreams: 1 LeeHyunWooCivilConstructionEngineeringQuantitativeAnalysisOfWarnings.pdf: 1011167 bytes, checksum: 77ead8c8c414af94bd04cb3e36a6a3ac (MD5) Previous issue date: 2015-03
  • description.provenance : Submitted by Deanne Bruner (deanne.bruner@oregonstate.edu) on 2015-07-27T21:57:01Z No. of bitstreams: 1 LeeHyunWooCivilConstructionEngineeringQuantitativeAnalysisOfWarnings.pdf: 1011167 bytes, checksum: 77ead8c8c414af94bd04cb3e36a6a3ac (MD5)
  • description.provenance : Approved for entry into archive by Deanne Bruner(deanne.bruner@oregonstate.edu) on 2015-07-27T21:57:54Z (GMT) No. of bitstreams: 1 LeeHyunWooCivilConstructionEngineeringQuantitativeAnalysisOfWarnings.pdf: 1011167 bytes, checksum: 77ead8c8c414af94bd04cb3e36a6a3ac (MD5)

Relationships

Parents:

This work has no parents.

Items