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Chinowsky, P, Diekmann, J and Galotti, V (2008) Social Network Model of Construction. Journal of Construction Engineering and Management, 134(10), 804–12.

Hartmann, T, Gao, J and Fischer, M (2008) Areas of Application for 3D and 4D Models on Construction Projects. Journal of Construction Engineering and Management, 134(10), 776–85.

Leu, S and Lin, Y (2008) Project Performance Evaluation Based on Statistical Process Control Techniques. Journal of Construction Engineering and Management, 134(10), 813–9.

Oo, B, Drew, D S and Lo, H (2008) Heterogeneous Approach to Modeling Contractors' Decision-to-Bid Strategies. Journal of Construction Engineering and Management, 134(10), 766–75.

Rojas, E M (2008) Single versus Multiple Prime Contracting. Journal of Construction Engineering and Management, 134(10), 758–65.

Sellés, M E, Rubio, J A and Mullor, J R (2008) Development of a Quantification Proposal for Hidden Quality Costs: Applied to the Construction Sector. Journal of Construction Engineering and Management, 134(10), 749–57.

Showalter, W E and Halpin, D W (2008) Dynamic Programming Approach to Optimization of Site Remediation. Journal of Construction Engineering and Management, 134(10), 820–7.

Song, L and AbouRizk, S M (2008) Measuring and Modeling Labor Productivity Using Historical Data. Journal of Construction Engineering and Management, 134(10), 786–94.

  • Type: Journal Article
  • Keywords: Productivity; Measurement; Data collection; Simulation; Neural networks; Construction management;
  • ISBN/ISSN: 0733-9364
  • URL: https://doi.org/10.1061/(ASCE)0733-9364(2008)134:10(786)
  • Abstract:
    Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.

Wang, Y, Goodrum, P M, Haas, C T and Glover, R W (2008) Craft Training Issues in American Industrial and Commercial Construction. Journal of Construction Engineering and Management, 134(10), 795–803.