Smoothing process of developing the construction MEP BIM model - A case study of the fire-fighting system

C. W. Feng, W. T. Lin

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

Conflict analysis is one of the most recognized benefits of employing Building Information Modeling (BIM) to the mechanical, electrical and plumbing (MEP) system, especially at the design stage. However, at the construction stage, the MEP professional still strives to install the MEP system only according to the BIM model due to insufficient information. This is because the BIM model of the MEP system is not well developed from the information requirements of the construction professional. Furthermore, it may also take too much effort to develop an information-sufficient BIM model because of the complexity involved. Therefore, there is a need of taking the more effective and efficient approach that can quickly develop the MEP BIM model from the construction perspective. This proposed study takes the fire-fighting system as the example to smooth the processes of developing the constructive MEP BIM model. First, a review of the construction specification and an interview with fire-fighting construction professionals are conducted to identify the information requirements. Then an ontology that employs the above result to determine the rules of building the information-sufficient BIM model is developed. Furthermore, a series of Dynamo modules are established to smooth the process of developing the BIM model for the fire-fighting system.

Original languageEnglish
Pages967-974
Number of pages8
DOIs
Publication statusPublished - 2017
Event34th International Symposium on Automation and Robotics in Construction, ISARC 2017 - Taipei, Taiwan
Duration: 2017 Jun 282017 Jul 1

Other

Other34th International Symposium on Automation and Robotics in Construction, ISARC 2017
Country/TerritoryTaiwan
CityTaipei
Period17-06-2817-07-01

All Science Journal Classification (ASJC) codes

  • Building and Construction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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