Hdl Handle:
http://hdl.handle.net/10149/58376
Title:
Embodying learning effect in performance prediction 
Authors:
Wong, P. S. P. (Peter); Cheung, S. O. (Sai); Hardcastle, C. (Cliff)
Affiliation:
City University of Hong Kong. Construction Dispute Resolution Research Unit. Department of Building and Construction; University of Teesside.
Citation:
Wong, P. S. P., Cheung, S. O. and Hardcastle, C. (2007) 'Embodying learning effect in performance prediction', Journal of Construction Engineering and Management, 133 (6), pp.474-482.
Publisher:
American Society of Civil Engineers
Journal:
Journal of Construction Engineering and Management
Issue Date:
Jun-2007
URI:
http://hdl.handle.net/10149/58376
DOI:
10.1061/(ASCE)0733-9364(2007)133:6(474)
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.
Type:
Article
Keywords:
performance; prediction; contractors; performance prediction models; Everett and Farghal; learning curve model; hyperbolic model
ISSN:
0733-9364
Rights:
Subject to restrictions, author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 08/01/2010]
Citation Count:
1 [Scopus, 08/01/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorWong, P. S. P. (Peter)-
dc.contributor.authorCheung, S. O. (Sai)-
dc.contributor.authorHardcastle, C. (Cliff)-
dc.date.accessioned2009-04-01T10:50:28Z-
dc.date.available2009-04-01T10:50:28Z-
dc.date.issued2007-06-
dc.identifier.citationJournal of Construction Engineering and Management; 133 (6): 474-482-
dc.identifier.issn0733-9364-
dc.identifier.doi10.1061/(ASCE)0733-9364(2007)133:6(474)-
dc.identifier.urihttp://hdl.handle.net/10149/58376-
dc.description.abstractPredicting 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.-
dc.publisherAmerican Society of Civil Engineers-
dc.rightsSubject to restrictions, author can archive post-print (ie final draft post-refereeing). For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 08/01/2010]-
dc.subjectperformance-
dc.subjectprediction-
dc.subjectcontractors-
dc.subjectperformance prediction models-
dc.subjectEverett and Farghal-
dc.subjectlearning curve model-
dc.subjecthyperbolic model-
dc.titleEmbodying learning effect in performance prediction -
dc.typeArticle-
dc.contributor.departmentCity University of Hong Kong. Construction Dispute Resolution Research Unit. Department of Building and Construction; University of Teesside.-
dc.identifier.journalJournal of Construction Engineering and Management-
ref.assessmentRAE 2008-
ref.citationcount1 [Scopus, 08/01/2010]-
or.citation.harvardWong, P. S. P., Cheung, S. O. and Hardcastle, C. (2007) 'Embodying learning effect in performance prediction', Journal of Construction Engineering and Management, 133 (6), pp.474-482.-
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