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
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© 2010 ASCE.
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Received: Feb 18, 2009
Accepted: Jan 15, 2010
Published online: Jan 28, 2010
Published in print: Aug 2010
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