Sequence Prediction Applied to BIM Log Data, An Approach to Develop a Command Recommender System for BIM Software Application

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Radnia, A. (2021). Sequence Prediction Applied to BIM Log Data, An Approach to Develop a Command Recommender System for BIM Software Application. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Building Information Modeling has become an industry standard for designing, documenting, and collaborating within the architecture, engineering, and construction industry. Architects spend most of their time developing projects from the feasibility study phase through post occupancy operation using BIM software such as Autodesk Revit. There are many tools created to automate tedious tasks or speed up the design process, however, most tasks require manual work, therefore taking longer to complete and increasing the probability of design errors. With each new release software developers are adding more features and tools to increase application capabilities; however, these are often underutilized since the proper training and resources for adopting the tools are not always available. Due to this limitation, designers will tend to stick to the tools they are already comfortable that are not necessarily best fit for all tasks. This thesis aims to investigate using Autodesk Revit Journals as a non-intrusive method to extract sequential data about user interactions with the software and use collected data to conduct analysis on design and work patterns of users. Additionally, collected data is used to develop a neural network that takes sequence of user commands and trains to predict the next command. The developed neural network can serve as a recommender system suggesting the most suitable commands for the user, enabling improved and efficient workflows and enforcement of best modeling practices.

Details
Author

Radnia, Ashkan

Title

Sequence Prediction Applied to BIM Log Data, An Approach to Develop a Command Recommender System for BIM Software Application

Physical Description

1 online resource (47 pages) : PDF

Date

2021

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Building Information Modeling has become an industry standard for designing, documenting, and collaborating within the architecture, engineering, and construction industry. Architects spend most of their time developing projects from the feasibility study phase through post occupancy operation using BIM software such as Autodesk Revit. There are many tools created to automate tedious tasks or speed up the design process, however, most tasks require manual work, therefore taking longer to complete and increasing the probability of design errors. With each new release software developers are adding more features and tools to increase application capabilities; however, these are often underutilized since the proper training and resources for adopting the tools are not always available. Due to this limitation, designers will tend to stick to the tools they are already comfortable that are not necessarily best fit for all tasks. This thesis aims to investigate using Autodesk Revit Journals as a non-intrusive method to extract sequential data about user interactions with the software and use collected data to conduct analysis on design and work patterns of users. Additionally, collected data is used to develop a neural network that takes sequence of user commands and trains to predict the next command. The developed neural network can serve as a recommender system suggesting the most suitable commands for the user, enabling improved and efficient workflows and enforcement of best modeling practices.

Genre

masters theses

Subjects--Topics

Computer science
Information technology
Architecture

Degree

M.Arch.

Keywords

Bim Logs
Building Information Modeling
Natural Language Processing
Revit Journal
Sequence Prediction
Software Recommender System

Subject Area

Architecture

Advisor(s)

Ellinger, Jefferson

Committee Members

Lee, Minwoo
Shaikh, Samira

Degree Note

Thesis (M.Arch.)--University of North Carolina at Charlotte, 2021.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Radnia_uncc_0694N_12837

Permalink

http://hdl.handle.net/20.500.13093/etd:2099