Automated Indoor Network Generation using Building Information Model

Authors: Jinwoo Park*, Texas A&M University, Zhengcong Yin, Texas A&M University, Daniel W. Goldberg, Texas A&M University
Topics: Geographic Information Science and Systems, Geography and Urban Health
Keywords: Indoor Network, BIM, Automation
Session Type: Paper
Day: 4/6/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Roosevelt 4, Marriott, Exhibition Level
Presentation File: No File Uploaded

This research investigates how to automatically generate an indoor network using the Building Information Model (BIM) without a person’s intervention and compares ways that produce indoor network in terms of accuracy, speed, and elasticity. Due to the advantage of BIM providing indoor spatial information, researchers have examined the methodologies to combine BIM data with GIS. Previous research has only focused on how to import and visualize BIM by using GIS software; however, people who want to use BIM data are required to have knowledge of its structure, which hampers researchers’ access to the data source. Therefore, this research covers how to automate indoor network generation and determines which method is better than others for the automated manner.
To come up with the fully automated generation, we classify the components of Industry Foundation Classes (IFC) and decide which classes are selected for which network generation method. Next, this research comprehends the script to automate the generation method by using relational tables of IFC. We then determine the accuracy of each method by comparing the real world distance and each output. Our research measures the generation time for each process by assuming massive quantities of buildings. Lastly, it determines each network models’ elasticity by applying each method to other buildings that have different purposes. The significance of this study is to make researchers’ access to BIM easy and efficient because they do not need to understand the complicated structure of BIM to employ it to their own work.

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