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El-Tayeh, A and Gil, N (2007) Using Digital Socialization to Support Geographically Dispersed AEC Project Teams. Journal of Construction Engineering and Management, 133(06), 462–73.

Gransberg, D D, Lopez del Puerto, C and Humphrey, D (2007)  Relating Cost Growth from the Initial Estimate to Design Fee for Transportation Projects. Journal of Construction Engineering and Management, 133(06), 404–8.

Han, S H, Kim, D Y and Kim, H (2007) Predicting Profit Performance for Selecting Candidate International Construction Projects. Journal of Construction Engineering and Management, 133(06), 425–36.

Hastak, M, Gokhale, S, Goyani, K, Hong, T and Safi, B (2007) Project Manager’s Decision Aid for a Radical Project Cycle Reduction. Journal of Construction Engineering and Management, 133(06), 437–46.

Ipsilandis, P G (2007) Multiobjective Linear Programming Model for Scheduling Linear Repetitive Projects. Journal of Construction Engineering and Management, 133(06), 417–24.

Kang, J H, Anderson, S D and Clayton, M J (2007) Empirical Study on the Merit of Web-Based 4D Visualization in Collaborative Construction Planning and Scheduling. Journal of Construction Engineering and Management, 133(06), 447–61.

Lo, W, Lin, C L and Yan, M R (2007) Contractor’s Opportunistic Bidding Behavior and Equilibrium Price Level in the Construction Market. Journal of Construction Engineering and Management, 133(06), 409–16.

Wong, P S P, On Cheung, S and Hardcastle, C (2007) Embodying Learning Effect in Performance Prediction. Journal of Construction Engineering and Management, 133(06), 474–82.

  • Type: Journal Article
  • Keywords: Predictions; Performance characteristics; Engineering education; Neural networks; Parameters;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/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.