Document Detail

Title: RR106-11 - An Analysis of the Impacts of Using Three-Dimensional Computer Models in the Management of Construct
Publication Date: 9/1/1995
Product Type: Research Report
Status: Archived Reference
Pages: 228
This publication has been archived, but is available for download for informational purposes only.

Griffis, Hogan, Lee, Columbia Univ.
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This research project was performed in order to identify the current applications of, impediments to and the benefits in the use of 3D computer models in construction. In order to collect the information required to answer these questions, two questionnaires were circulated throughout the membership of the Construction Industry Institute and a series of site visits were performed at a project under construction that was using a 3D computer model on site.

The first questionnaire was designed to collect information regarding the current “state of belief” within the industry regarding the use of 3D computer models in construction and their current usage. One hundred and fifty seven responses were received to this survey. The second questionnaire was designed to collect project specific data from projects that used 3D computer models in the management of construction and from projects that did not. This data was collected and analyzed for 93 projects (primarily from the process/power industry). Finally, a case study project was selected that was being built by the DuPont Company in the southeastern United States. This case study site was used to illustrate how the 3D computer models can be used on the construction site and to validate some of the more general results from the questionnaire responses.

Based on the information from the sources described above, the construction management applications currently being performed with the assistance of 3D computer models were fairly consistent across the user population. The most common applications included:

  1. Checking clearances and access
  2. Visualizing details from non-standard viewpoints
  3. Using them as reference during project meetings
  4. Performing constructability reviews


The greatest perceived impediments to the use of 3D computer models in construction were found to be:

  1. Undetermined economic impacts
  2. Inertia (resistance to change) within the industry
  3. Lack of trained people


Legal concerns, security concerns and regulatory agencies were not seen to be impediments. Cost issues, however, were seen as impediments, but only to the non-users. Strategies recommended by the respondents to overcome the impediments to the use of 3D in construction included:

  1. Performing a meaningful cost benefits analysis
  2. Standardizing the hardware and software
  3. Improving the functionality of the systems available


The benefits in the use of 3D computer models in construction were found to be clear and conclusive. The respondents to the “state of belief” questionnaire felt that the benefits in the use of this technology included:

  1. Reducing interference problems
  2. Assisting in visualization
  3. Reducing rework
  4. Improving engineering accuracy
  5. Improving jobsite communication


These opinions were substantiated by the results of the analysis of the 93 projects in the study sample. The differences in project outcomes between the projects that did not use 3D computer models and the projects that used them in an “average” to “very good” way (excluding the projects that used them poorly) included an average of 5% reduction in cost growth, a 4% reduction in schedule slip and a 65% reduction in total rework.

These benefits have been further substantiated in the detailed analysis of the case study project. At this point in the project’s construction, more than $122,000 in direct cost savings due to the use of the 3D computer model have been itemized and documented in this report. This direct cost savings does not include many of the additional benefits seen in the use of the 3D computer model that are less quantifiable but that are apparent on this project.

In an effort to take into account other factors that may have an effect on the project outcomes mentioned above, predictive models were constructed using multivariate linear regression and artificial neural networks. The resulting models are presented and explained in this report.