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Extracting and Integrating Industrial Construction Steel Trade Data in ill-formed BIM Models
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- Author / Creator
- Ali, Mostafa A A
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Oil and gas are known for their huge-size and complex projects that consist of multiple trades’ subprojects such as concrete, steel, and piping. These subprojects are executed within a confined area during a limited time frame. This requires careful planning and coordination between these different trades. Each trade creates a separate Building Information Modelling (BIM) model which are merged with others into one huge model. This model is used for coordinating work packages and detect any possible clashes. From the contractor perspective, the BIM model has additional uses such as defining scope and obtaining a preliminary estimate during early stages of the project. The model’s potential depends on its degree of completeness and time of availability. However, the current practice in the industry and the usage of specialized BIM solutions means that during early stages of the project the BIM model will immature, incomplete, and inconsistent. This means the model usability becomes limited and the contractor has to review the model manually to extract the required information including the scope of each trade, a preliminary estimate of quantities, etc. The objective of this research is to investigate and provide a new methodology that can automatically fill the missing data in the BIM model and leverage its usage. This objective is achieved through three main steps. 1) automatically cluster the BIM objects based on their trade, 2) refine cluster results by identifying the shape and size of BIM objects automatically, and 3) leverage BIM model usage by merging its data with other data sources. Accordingly, this research is subdivided into three sections. The first section focuses on clustering BIM models based on the trade (e.g. steel, piping, concrete) of BIM objects. The research provides four mathematical models that are able to automatically cluster the BIM models with a purity level up to 91%. The second step focuses on obtaining a preliminary quantity take-off for the steel trade in BIM models using shape recognition techniques. This approach focuses on geometries rather than the incomplete descriptive attributes. The research reviews different shape recognition techniques to select the most suitable technique. Then, it introduces a method to estimate steel sections using the shape distribution technique. Finally, it optimizes method parameters and tests the method using three real-world industrial project models. Results indicate that the proposed method works best using around 50,000 random distances with an 8.8% margin of error at a 95% confidence level. The third section demonstrates how the enhanced BIM data can be automatically merged with heterogeneous data sources using semantic web standards. This sections discusses developing an ontology which captures concepts related to the visualization process. Then, heterogeneous data sources that are commonly used in construction are fed into the ontology. The potential of this approach has been demonstrated by providing multiple visualization scenarios that cover different audiences, levels of detail, and time resolutions. The methodology has been implemented and validated using three real-case projects. Results show that the proposed framework can automatically process ill-defined and incomplete BIM model to fill the missing data. It provides a quicker way than the manual one to provide a preliminary estimate of quantities. Additionally, the framework allows automatic merge of data between BIM models and other heterogeneous data sources that are commonly used in the industry. Data merge has many usages; the research proves its usability by providing a way to automatically generate visualizations based on customized queries.
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- Graduation date
- Fall 2017
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.