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Integration of Building Information Modeling (BIM) and Process Mining for Design Authoring Processes


Integration of Building Information Modeling (BIM) and Process Mining for Design Authoring Processes

Kouhestani, Sobhan (2019) Integration of Building Information Modeling (BIM) and Process Mining for Design Authoring Processes. Masters thesis, Concordia University.

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Building Information Modelling (BIM) corresponds to the generation and management of the digital representation for building products by wrapping building elements and their information in a unique source file. Open BIM, relying on platform-independent standards, such as IFC (Industry Foundation Classes), is supposed to increase the interoperability in the BIM environment. BIM, as a shared work platform in AEC (Architecture, Engineering and Construction) industry, can be upgraded to act as an Enterprise Resource Management (ERM) system and support data mining for the management of design and construction processes. ERM systems rely on transaction data, also known as “event logs”. eXtensibile Event Stream (XES) is an XML (Extensible Markup Language) schema aiming to provide a format for supporting the interchange of event logs. XES-based Event logs commonly include some semantics (called extensions) regarding events.
This work aims to enable BIM to act as an ERM system. To realize this goal, four research objectives were defined and achieved. First, an ‘IFC archiver algorithm’ was developed to take snapshots, on a regular basis, from different stages of building modeling process (performed in Autodesk Revit), throughout the design phase from start to the end. Second, an ‘IFC logger algorithm’ was created to consecutively compare archived IFC files, detect design activities and save them in the CSV format event log. Then, XESame module is used to map the CSV format event log to the appropriate data format for Process Mining (i.e., XES format event logs). The activities were categorized in five classes: Addition, Removal, Rotation, Relocation of elements (e.g., a wall), and changes in their properties (e.g., the size, type or family of an element). Five attributes for each activity were stored in the database. Those included: Element ID, Designer, Element Name (Name of the Activity), Start and End time of each activity. Third, Process Mining techniques were used to detect the as-happened processes. Last but not least, Process Mining helped to derive different types of design process information (analytics) such as social networks of actors, bottlenecks of processes and process deviations.
Two case studies were performed to validate and verify the research methodology. Around 300 and 30,000 events were captured respectively, during the design phase of our first and second case studies. Then, the activity log was fed to a Process Mining tool to mine the as-happened design processes. Two levels of process maps were discovered: As-happened level 2 and “level 3” BIM maps. As-happened maps were derived and represented in Petri net and process tree formats. Moreover, different types of animations of the as-happened design processes were derived for level 2 and “level 3” BIM maps from replaying the event logs on top of the captured processes. Those animations showed project paths, activities queue lengths and service times.
In a nutshell, the study successfully applied Process Mining on the foundation of BIM (as an ERM system) and accordingly made discovery, monitoring and optimizing BIM processes possible. The present study aims to assist BIM and project managers by enabling BIM as a management tool for design processes. These processes are important, because the design phase is at the early stage of every construction project.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (Masters)
Authors:Kouhestani, Sobhan
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Building Engineering
Date:18 March 2019
Thesis Supervisor(s):Nik-Bakht, Mazdak
Keywords:BIM processes, data mining, process mining, enterprise resource management, Design Authoring, IFC
ID Code:985149
Deposited By: Sobhan Kouhestani
Deposited On:17 Jun 2019 18:45
Last Modified:17 Jun 2019 18:45
Related URLs:



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