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T.P. Williams and J. Gong

Department of Civil and Environmental Engineering
Rutgers University, New Brunswick, United States of America

doi:10.4203/ccp.102.158

T.P. Williams, J. Gong, "Construction Project Cost Prediction using Text and Data Mining", in , (Editors), "Proceedings of the Fourteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 158, 2013. doi:10.4203/ccp.102.158

Keywords: construction costs, data mining, text mining.

Summary

In this paper, text data from a sample of competitively bid California highway projects has been used to predict the likely level of cost overrun in construction projects. A text description of the project and the text of the five largest project line items were used as input. The text data were converted to numerical attributes using text-mining algorithms and singular value decomposition. Classification rules were produced using the Ridor (ripple down rules) classification algorithm. Results of the modeling effort showed that the text data could be used to gain insight into the likely level of project cost overrun.

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 102 PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING COMPUTING Edited by: Paper 158 Construction Project Cost Prediction using Text and Data Mining T.P. Williams and J. Gong Department of Civil and Environmental Engineering Rutgers University, New Brunswick, United States of America doi:10.4203/ccp.102.158 purchase the full-text of this paper Full Bibliographic Reference for this paper T.P. Williams, J. Gong, "Construction Project Cost Prediction using Text and Data Mining", in , (Editors), "Proceedings of the Fourteenth International Conference on Civil, Structural and Environmental Engineering Computing", Civil-Comp Press, Stirlingshire, UK, Paper 158, 2013. doi:10.4203/ccp.102.158 Keywords: construction costs, data mining, text mining. Summary In this paper, text data from a sample of competitively bid California highway projects has been used to predict the likely level of cost overrun in construction projects. A text description of the project and the text of the five largest project line items were used as input. The text data were converted to numerical attributes using text-mining algorithms and singular value decomposition. Classification rules were produced using the Ridor (ripple down rules) classification algorithm. Results of the modeling effort showed that the text data could be used to gain insight into the likely level of project cost overrun. purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £65 +P&P)
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