Data Preprocessing–Based Parametric Cost Model for Building Projects: Case Studies of Korean Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 136, Issue 8
Abstract
For construction to progress smoothly, effective cost estimation is vital, particularly in the conceptual and schematic design stages. In these early phases, despite the fact that initial estimates are highly sensitive to changes in project scope, owners require accurate forecasts which reflect their supplying information. Thus, cost estimators need reliable estimation strategies. In practice, parametric cost estimation, which utilizes historical cost data, is the most commonly used method in these initial phases. Therefore, compilation of historical data pertaining to appropriate cost variance governing parameters is a prime requirement. However, data mining (data preprocessing) for denoising internal errors or abnormal values must be performed before this compilation. To address this issue, this research proposes a statistical methodology for data preprocessing. Moreover, a statistically preprocessed data–based parametric (SPBP) cost model is developed based on multiple regression equations. Case studies of Korean construction projects verify that the model enhances cost estimate accuracy and reliability than conventional cost models.
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Acknowledgments
This research was supported by a grant (Grant No. UNSPECIFIEDR&D06CIT-A03) from the Innovative Construction Cost Engineering Research Center and a grant (Grant No. UNSPECIFIED05CIT-01) from the Construction Technology Innovation Program funded by the Ministry of Land, Transport, and Marine Affairs (Government of Korea).UNSPECIFIED
References
Akintoye, A. (2000). “Analysis of factor influencing project cost estimating practice.” Constr. Manage. Econom., 18, 77–89.
Ashworth, A. (1986). “Cost models—Their history development and appraisal.” Technical information service no. 64, CIOB, London.
Bajaj, A., et al. (2002). “Parametric estimating for design costs.” AACE International Transactions, 2002, EST.08.1–EST.08.6.
Chan, L. S., and Park, M. (2005). “Project cost estimation using principle component regression.” Constr. Manage. Econom., 23, 295–304.
Cheng, M. Y., et al. (2008). “Web-based conceptual cost estimates for construction projects using evolutionary fuzzy neural inference model.” Autom. Constr., 18(2), 164–172.
Cresse, R., and Li, L. (1995). “Cost estimation of timber bridge using neural networks.” Cost Eng., 37(5), 17–22.
Dysert, L. R. (2001). “Developing a parametric model for estimating process control costs.” Cost Eng., 43(2), 31–34.
Ellsworth, R. K. (1998). “Cost-to-capacity analysis for estimating waste-to-energy facility costs.” Cost Eng., 40(6), 27–30.
Graza, J., and Rouhana, K. (1995). “Neural network versus parameter based application.” Cost Eng., 37(2), 14–18.
Han, J., and Kamber, M. (2003). Data mining-concept and techniques, Korean version, Freedom Academy Publishing Co., Seoul, 147–160.
Hegazy, T., and Ayed, A. (1998). “Neural network model for parametric cost estimating of highway projects.” Constr. Manage. Econom., 124(3), 210–218.
Hendrickson, C. (2000). Project management for construction 2, 2nd Ed., Carnegie Mellon Univ., Pittsburgh, ⟨http://pmbook.ce.cmu.edu/05_Cost_Estimation.html⟩ (February 2008).
Jarde, A., and Alkass, S. (2007). “Computer-integrated system for estimating the costs of building project.” J. Constr. Eng. Manage., 13(4), 205–223.
Karshenas, S. (2005). “Cost estimating in the age of 3D CAD software and object database.” Proc., Construction Research Congress 2005, ASCE, Reston, Va.
Kirkham, R. J. (2007). Ferry & Brandon’s cost planning of buildings, 8th Ed., Blackwell Science, Oxford, U.K.
Kwan, S. H. (2008). Multivariate data analysis and application, Freedom Academy Publishing Co., Seoul, 72.
Meyer, E. R., and Burns, T. J. (1999). “Facility parametric cost estimating.” AACE International Transaction, EST.02.1–EST.02.6.
Ministry of Land, Transport, and Marine Affairs of Korea. (2007). “Announcement of historical cost indices for public apartment project under price cap regulation.” Rep. No. 2007-79, Seoul.
Oberlender, G. D., and Trost, S. M. (2001). “Predicting accuracy of early cost estimates based on estimate quality.” J. Constr. Eng. Manage., 127(3), 173–182.
Park, S., et al. (2004). SPSS ver. 12k, SPSS Academy Publishing, Seoul, 225.
Seeley, I. H. (1997). Quantity surveying practice, 2nd Ed., Antony Rowe Ltd., London.
Sharma, S. (1996). Applied multivariate techniques, Wiley, New York.
Smith, A. J. (1995). Estimating, tendering and bidding for construction, Macmillan, London.
Soutos, M., and Lowe, D. J. (2005). “Procost—Towards a powerful early stage cost estimating tool.” Proc., Int. Conf. on Computing in Civil Engineering, Computing in Civil Engineering.
Teicholz, P. (1993). “Forecasting final cost and budget of construction projects.” J. Comput. Civ. Eng., 7(4), 511–529.
Trost, S. M., and Oberlender, G. D. (2003). “Predicting accuracy of early cost estimates using factor analysis and multivariate regression.” J. Constr. Eng. Manage., 129(2), 198–204.
Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 136 • Issue 8 • August 2010
Pages: 844 - 853
Copyright
© 2010 ASCE.
History
Received: Feb 18, 2009
Accepted: Jan 15, 2010
Published online: Jan 28, 2010
Published in print: Aug 2010
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