Embodying Learning Effect in Performance Prediction
Publication: Journal of Construction Engineering and Management
Volume 133, Issue 6
Abstract
Predicting performance of contractors is of interest to both academics and practitioners. The physical execution of a project is critical to the overall success of the development. Having a competent contractor that can deliver is most desirable. In this aspect, a significant number of performance prediction models have been developed. Multiple regression and neural networks are typically used as the analytical tools in these prediction models. This paper reports a study that employs a learning curve approach to perform the prediction task. It is suggested that this approach can accommodate the changes in performance as experience accumulates. Thus a performance pattern is projected in addition to the project final outcome. A two-step approach suggested by Everett and Farghal was adopted for this study. First, the learning curve model that best represents a contractors’ performance was explored using the least-square curve fitting analysis. Second, prediction analysis was performed by comparing the actual performance data with their respective prediction results obtained from extrapolation on the selected learning curve. The three-parameter hyperbolic model was found to provide the most reliable prediction on performance in this study.
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Acknowledgments
The work described in this paper is fully supported by a grant from the City University of Hong Kong (Project No. UNSPECIFIED7001686). The authors are grateful to the Hong Kong Housing Department for providing the data for the study.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 133 • Issue 6 • June 2007
Pages: 474 - 482
Copyright
© 2007 ASCE.
History
Received: May 17, 2006
Accepted: Jan 22, 2007
Published online: Jun 1, 2007
Published in print: Jun 2007
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