Download PDFOpen PDF in browser

Effectiveness of 3D Models in Estimating Course

9 pagesPublished: May 26, 2024

Abstract

3-Dimensional (3D) Building Information Model (BIM) can help students’ learning in Construction Management (CM) courses. 3D models can help students visualize the information from plans in format of 2D into 3D. Due to this benefit, 3D models have been used in diverse CM courses, and the effectiveness of 3D models in diverse CM courses have been proven to be positive. However, the usage of 3D models in estimating courses has not been clearly proven by existing literatures. Furthermore, accurate estimating requires multiple estimating tasks starting with visualizing building components. There is no existing study on how effective the usage of 3D models is for each of the estimating sub-tasks. This study aimed to explore effectiveness of 3D models in estimating related tasks through students’ perception on effectiveness of 3D models in an estimating course at a 4-year college in the U.S. The results of this study show that overall, the 3D models were effective in all the estimating tasks. Also, ‘visualizing building components’, ‘locating building components’, and ‘identifying building components' are the estimating sub-tasks in which the 3D models were the most effective.

Keyphrases: 3d visualization skill, bim, estimating

In: Tom Leathem, Wes Collins and Anthony Perrenoud (editors). Proceedings of 60th Annual Associated Schools of Construction International Conference, vol 5, pages 285-293.

BibTeX entry
@inproceedings{ASC2024:Effectiveness_3D_Models_Estimating,
  author    = {Euysup Shim and Haiyan Xie},
  title     = {Effectiveness of 3D Models in Estimating Course},
  booktitle = {Proceedings of 60th Annual Associated Schools of Construction International Conference},
  editor    = {Tom Leathem and Wes Collins and Anthony Perrenoud},
  series    = {EPiC Series in Built Environment},
  volume    = {5},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2632-881X},
  url       = {/publications/paper/7MDD},
  doi       = {10.29007/wmc4},
  pages     = {285-293},
  year      = {2024}}
Download PDFOpen PDF in browser