Development of a data mining-based analysis framework for multi-attribute construction project information

Authors:
Seokho Chi;Sung-Joon Suk;Youngcheol Kang;Stephen P. Mulva
Affiliations:
School of Civil Engineering and Built Environment, Science and Engineering Faculty, Queensland University of Technology, QLD 4001, Australia;Construction Industry Institute, University of Texas at Austin, 3925 West Braker Lane (R4500), Austin, TX 78759-5316, USA;OHL School of Construction, Florida International University, Miami, FL 33174, USA;Construction Industry Institute, University of Texas at Austin, 3925 West Braker Lane (R4500), Austin, TX 78759-5316, USA
Venue:
Advanced Engineering Informatics
Year:
2012

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Abstract

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.