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Beavers, J E, Moore, J R and Schriver, W R (2009) Steel Erection Fatalities in the Construction Industry. Journal of Construction Engineering and Management, 135(03), 227–34.

Blacud, N A, Bogus, S M, Diekmann, J E and Molenaar, K R (2009) Sensitivity of Construction Activities under Design Uncertainty. Journal of Construction Engineering and Management, 135(03), 199–206.

Chan, E H and Au, M C (2009) Factors Influencing Building Contractors’ Pricing for Time-Related Risks in Tenders. Journal of Construction Engineering and Management, 135(03), 135–45.

Chao, L and Chien, C (2009) Estimating Project S-Curves Using Polynomial Function and Neural Networks. Journal of Construction Engineering and Management, 135(03), 169–77.

Chung, B, Skibniewski, M J and Kwak, Y H (2009) Developing ERP Systems Success Model for the Construction Industry. Journal of Construction Engineering and Management, 135(03), 207–16.

Dai, J, Goodrum, P M and Maloney, W F (2009) Construction Craft Workers’ Perceptions of the Factors Affecting Their Productivity. Journal of Construction Engineering and Management, 135(03), 217–26.

Hwang, B, Thomas, S R, Haas, C T and Caldas, C H (2009) Measuring the Impact of Rework on Construction Cost Performance. Journal of Construction Engineering and Management, 135(03), 187–98.

Kim, B and Reinschmidt, K F (2009) Probabilistic Forecasting of Project Duration Using Bayesian Inference and the Beta Distribution. Journal of Construction Engineering and Management, 135(03), 178–86.

  • Type: Journal Article
  • Keywords: Forecasting; Scheduling; Bayesian analysis; Construction management;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2009)135:3(178)
  • Abstract:
    Reliable forecasting is instrumental in successful project management. In order to ensure the successful completion of a project, the project manager constantly monitors actual performance and updates the current predictions of project duration and cost at completion. This study introduces a new probabilistic forecasting method for schedule performance control and risk management of on-going projects. The Bayesian betaS-curve method (BBM) is based on Bayesian inference and the beta distribution. The BBM provides confidence bounds on predictions, which can be used to determine the range of potential outcomes and the probability of success. Furthermore, it can be applied from the outset of a project by integrating prior performance information (i.e., the original estimate of project duration) with observations of new actual performance. A comparative study reveals that the BBM provides, early in the project, much more accurate forecasts than the earned value method or the earned schedule method and as accurate forecasts as the critical path method without analyzing activity-level technical data.

Menassa, C C, Mora, F P and Pearson, N (2009) Option Pricing Model to Analyze Cost–Benefit Trade-Offs of ADR Investments in AEC Projects. Journal of Construction Engineering and Management, 135(03), 156–68.

Sharma, H, McIntyre, C, Gao, Z and Nguyen, T (2009) Developing a Traffic Closure Integrated Linear Schedule for Highway Rehabilitation Projects. Journal of Construction Engineering and Management, 135(03), 146–55.