Development of a data mining-based analysis framework for multi-attribute construction project information
Chi, Seokho, Suk, Sung-Joon , Kang, Youngcheol , & Mulva, Stephen P. (2012) Development of a data mining-based analysis framework for multi-attribute construction project information. Advanced Engineering Informatics, 26(3), pp. 574-581.
Data mining techniques extract repeated and useful patterns from a large data set that in turn are utilized to predict the outcome of future events. The main purpose of the research presented in this paper is to investigate data mining strategies and develop an efficient framework for multi-attribute project information analysis to predict the performance of construction projects. The research team first reviewed existing data mining algorithms, applied them to systematically analyze a large project data set collected by the survey, and finally proposed a data-mining-based decision support framework for project performance prediction. To evaluate the potential of the framework, a case study was conducted using data collected from 139 capital projects and analyzed the relationship between use of information technology and project cost performance. The study results showed that the proposed framework has potential to promote fast, easy to use, interpretable, and accurate project data analysis.
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|Item Type:||Journal Article|
|Keywords:||Construction data mining, Qualitative project information acquisition, Project performance analysis, Multi-attribute survey|
|Subjects:||Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > ENGINEERING DESIGN (120400)|
|Divisions:||Current > Schools > School of Civil Engineering & Built Environment|
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 Elsevier|
|Copyright Statement:||This is the author’s version of a work that was accepted for publication in <Advanced Engineering Informatics>. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Advanced Engineering Informatics, 26(3), (2012). DOI: 10.1016/j.aei.2012.03.005|
|Deposited On:||12 Jul 2012 08:15|
|Last Modified:||12 Jul 2012 08:15|
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