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Aibinu, A A, Ofori, G and Ling, F Y (2008) Explaining Cooperative Behavior in Building and Civil Engineering Projects’ Claims Process: Interactive Effects of Outcome Favorability and Procedural Fairness. Journal of Construction Engineering and Management, 134(09), 681–91.

Chan, E H and Au, M C (2008) Relationship between Organizational Sizes and Contractors’ Risk Pricing Behaviors for Weather Risk under Different Project Values and Durations. Journal of Construction Engineering and Management, 134(09), 673–80.

Lucko, G (2008) Productivity Scheduling Method Compared to Linear and Repetitive Project Scheduling Methods. Journal of Construction Engineering and Management, 134(09), 711–20.

Ndekugri, I, Braimah, N and Gameson, R (2008) Delay Analysis within Construction Contracting Organizations. Journal of Construction Engineering and Management, 134(09), 692–700.

Ng, S T and Zhang, Y (2008) Optimizing Construction Time and Cost Using Ant Colony Optimization Approach. Journal of Construction Engineering and Management, 134(09), 721–8.

Peña-Mora, F, Han, S, Lee, S and Park, M (2008) Strategic-Operational Construction Management: Hybrid System Dynamics and Discrete Event Approach. Journal of Construction Engineering and Management, 134(09), 701–10.

Sohail, M and Cavill, S (2008) Accountability to Prevent Corruption in Construction Projects. Journal of Construction Engineering and Management, 134(09), 729–38.

Wong, J M W, Chan, A P C and Chiang, Y H (2008) Modeling and Forecasting Construction Labor Demand: Multivariate Analysis. Journal of Construction Engineering and Management, 134(09), 664–72.

  • Type: Journal Article
  • Keywords: Forecasting; Labor; Regression models; Project management; Hong Kong;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:9(664)
  • Abstract:
    This paper presents the development of advanced labor demand forecasting models at project level. A total of 11 manpower demand forecasting models were developed for the total project labor and ten essential trades. Data were collected from a sample of 54 construction projects. These data were analyzed through a series of multiple linear regression analyses that help establish the estimation models. The results indicate that project labor demand depends not only on a single factor, but a cluster of variables related to the project characteristics, including construction cost, project complexity attributes, physical site condition, and project type. The derived regression models were tested and validated using four out-of-sample projects and various diagnostic tests. It is concluded that the models are robust and reliable, which merit for contractors and HKSAR government to predict the labor required for a new construction project and facilitate human resources planning and budgeting, and that the methodology used may be applied to develop equally useful models in other subsectors, and in other countries.

Zhang, H and Wang, J Y (2008) Particle Swarm Optimization for Construction Site Unequal-Area Layout. Journal of Construction Engineering and Management, 134(09), 739–48.