DEVELOPING A KNOWLEDGE DISCOVERY FRAMEWORK IN HIGHWAY PROJECTS USING INDUSTRIAL FOUNDATION CLASS (IFC) EXTENSION

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Shen, Z. (2016). DEVELOPING A KNOWLEDGE DISCOVERY FRAMEWORK IN HIGHWAY PROJECTS USING INDUSTRIAL FOUNDATION CLASS (IFC) EXTENSION. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Highway-project data are being accumulated at a dramatic pace with the advance of information technology; consequently, the need for discovering useful knowledge among the historical data to prevent highway-project construction delay is increasing. However, the efficiency and effectiveness of most current highway-project knowledge discovery approaches are very low. A new systematic approach integrating Industrial Foundation Class (IFC), data warehouse, and data mining technologies is needed to address this issue. The proposed highway IFC data schema adopts an object oriented design approach to integrate interdisciplinary highway information into one universal intelligent format. IFC data is machine readable and suitable for complex query and reasoning, thus the highway-project knowledge discovery process can be highly automated by deploying IFC in the data warehouse with data mining function. This above-mentioned approach is tested with a 3D highway data warehouse prototype system in a case study of KICT project. The prototype system stores the highway IFC data and automatically generate the feature data and target data according to user requirements. Two data mining models are built to find useful patterns among the data. The performances of the models are compared. A cost sensitive analysis is especially performed to take into consideration of the practical cost of a project manager’s decision. Overall, this research has developed a highly automated approach to discover highway-project knowledge from historical data in an effective and efficient manner, which can help highway-project managers make timely and wise decisions to avoid potential construction delay and the associated costs.

Details
Author

Shen, Zhenhua

Title

DEVELOPING A KNOWLEDGE DISCOVERY FRAMEWORK IN HIGHWAY PROJECTS USING INDUSTRIAL FOUNDATION CLASS (IFC) EXTENSION

Physical Description

1 online resource (135 pages) : PDF

Date

2016

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Highway-project data are being accumulated at a dramatic pace with the advance of information technology; consequently, the need for discovering useful knowledge among the historical data to prevent highway-project construction delay is increasing. However, the efficiency and effectiveness of most current highway-project knowledge discovery approaches are very low. A new systematic approach integrating Industrial Foundation Class (IFC), data warehouse, and data mining technologies is needed to address this issue. The proposed highway IFC data schema adopts an object oriented design approach to integrate interdisciplinary highway information into one universal intelligent format. IFC data is machine readable and suitable for complex query and reasoning, thus the highway-project knowledge discovery process can be highly automated by deploying IFC in the data warehouse with data mining function. This above-mentioned approach is tested with a 3D highway data warehouse prototype system in a case study of KICT project. The prototype system stores the highway IFC data and automatically generate the feature data and target data according to user requirements. Two data mining models are built to find useful patterns among the data. The performances of the models are compared. A cost sensitive analysis is especially performed to take into consideration of the practical cost of a project manager’s decision. Overall, this research has developed a highly automated approach to discover highway-project knowledge from historical data in an effective and efficient manner, which can help highway-project managers make timely and wise decisions to avoid potential construction delay and the associated costs.

Genre

doctoral dissertations

Subjects--Topics

Computer science
Civil engineering

Degree

Ph.D.

Keywords

BIM
Data Mining
Highway
IFC
KDD

Subject Area

Civil Engineering

Advisor(s)

Kim, Hyunjoo
Wu, Jy

Committee Members

Chen, Don
Lu, Na
Azarbayjani, Mona

Degree Note

Thesis (Ph.D.)--University of North Carolina at Charlotte, 2016.

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Rights Holder Information

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Identifier

Shen_uncc_0694D_11213

Permalink

http://hdl.handle.net/20.500.13093/etd:611