Nowadays, digitalization supports and even improves more and more areas such as educa- tion and healthcare. Actually, areas like the building industry benefit from those advantages as well. Pencil drawings have been replaced by feature-rich 3D models with the help of computer-aided design (CAD) software. Moreover, models of buildings became increasingly “smarter” by appending additional information – which is widely known as Building Infor- mation Modeling (BIM). Yet, the most-used data modeling standard – Industry Foundation Classes (IFC) – has shortcomings regarding maintainablity and extensibility.
Therefore, this thesis focuses on improving these aspects with the help of role-oriented modeling. A motivating introduction will mark the beginning by familiarizing the idea of BIM, proposing the methodology and the research questions for this thesis, and elaborating on the status quo. Afterwards, a deeper understanding of IFC and its core problems will set the basis for the development of a solution to the identified deficiencies. Prior to that, the basics in role-oriented modeling will be explained. Consequently, the developed role-oriented so- lution – namely Industry Foundation Classes with Roles (IFC-R) – will be introduced, followed by a comparison of IFC and IFC-R in order to prove its effects. This will be supported by an evaluation of the comparison, which leads to the conclusion of this thesis and a brief outlook for future research.:1. Introduction
2. Understanding Industry Foundation Classes (IFC)
2.1. Structure and Fundamental Concepts of IFC
2.1.1. Organization and Architecture of IFC
2.1.2. Examination of the Concepts
2.2. The Modeling Language EXPRESS
2.2.1. Building Blocks of EXPRESS
2.2.2. The Influence of EXPRESS on IFC
2.3. Analysis of Core Issues
2.3.1. Adding properties by means of property sets
2.3.2. Orthogonal classification utilizing object typing
3. Developing a Role-oriented Solution
3.1. Industry Foundation Classes with Roles (IFC-R)
3.1.1. Role-oriented Modeling with CROM
3.1.2. IFC-R:Models and Tools
3.2. Prototypical Implementation applying IFC-R
4. Comparing IFC and IFC-R
4.1. Definition of used Software Metrics
4.1.1. Identifying suitable measurement methods
4.1.2. The Use Case Points (UCP) method
4.1.3. Adapting the UCP method
4.1.4. Supporting Metrics
4.2. Evaluation of IFC and IFC-R
4.2.1. Gathering the supporting metrics
4.2.2. Applying the UCP method
4.3. Problems and Interim Conclusion
5. Conclusion and Outlook
References
Appendix A. Figures
Appendix B. Code Listings
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:74049 |
| Date | 25 February 2021 |
| Creators | Klaude, Martin |
| Contributors | Götz, Sebastian, Aßmann, Uwe, Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:masterThesis, info:eu-repo/semantics/masterThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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