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Identification of Factors Driving Crew Production Rate : Methodology and Application  

Huh Youngki (미국 텍사스대, 건설경영학)
Publication Information
Korean Journal of Construction Engineering and Management / v.5, no.5, 2004 , pp. 93-100 More about this Journal
Abstract
For accurate construction contract time estimation, few parameters are more significant than crew production rates and factors affecting the rates. However, statistical analysis techniques for finding such factors are not always simple mainly because there are many factors and the interaction between factors is not well quantitatively understood. This paper presents methodology of identifying factors driving crew production rates. The methodology is further demonstrated with representative data collected by the author from 13 on-going highway constructions. Three factors were identified as statistically significant drivers of Cap crew production rate: 'Cap Size (m3/ea)'; 'Cap Length (m)'; and 'Cap Shape (Rectangle vs. Inverted 'T')'. It was also found that the production rates are best explained by a multiple regression model with two of the drivers; 'Cap Size' and 'Cap Shape'.
Keywords
Factor; Quantitative Analysis; Crew Reduction Rate; Time Determination;
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  • Reference
1 Albright et al, 'Data Analysis and Decision Making', 2nd edition, Thomas Learning, Inc., CA, USA, 2003
2 Hendrickson, C. et al., Expert System for Construction Planning, Journal of Computing in Civil Engineering, Vol. 1, No. 4, pp. 253-269, 1987   DOI   ScienceOn
3 Huh, Y. K., 'Highway Bridge Construction Production Rates for Contract Time Estimation.' Dissertation, Dept. of Civil Eng., University of Texas at Austin, Austin, Texas, USA, 2004
4 Sonmez, R. and Rowings, J. E., Construction Labor Productivity Modeling with Neural Networks, Journal of Construction Engineering and Management, Vol. 124, No 6, pp.498-504, 1998
5 Stevens, J., 'Applied Multivariate Statistics for the Social Sciences,' eBook (http://www.netlibrary.com). Mahwah, N.J. Lawrence Erlbaurn Associates, Inc., Mahwah, New Jersey, 2002
6 Thomas, H. R., Smith, G. R., and Sanders, S. R., An Exploratory Study of Productivity Forecasting Using the Factor Model for Masonry, PTI Rep. 9005, Pennsylvania Transportation Institute, Pennsylvania State University, University Park, Pa,1989
7 Ming, Lu et. al, Estimating Labor Productivity Using Probability Inference Neural Network, Journal of Computing in Civil Engineering, Vol. 14, No.4, pp.241-248, 2000   DOI   ScienceOn
8 Sakamoto, A. J., 'SOCL. STAT: LIN. MOD./STRC. EQ. SYS.,' Class (SOC 385L) Note, University of Texas at Austin, Texas, USA, 2002
9 Portas, J. and AbouRizk, S., Neural Network Model for Estimating Construction Productivity, Journal of Construction Engineering and Management, Vol. 123, No.4, December 1997, pp.399-410, 1997
10 Thomas, H. R. and Raynar, K. A., Scheduled Overtime and Labor Productivity: Quantitative Analysis, Journal of Construction Engineering and Management, Vol 123, No.2, pp.181-188, 1997
11 Sonmez, R., 'Construction Labor Productivity Modeling with Neural Networks and Regression Analysis,' PhD thesis, Dept of Civil Eng., Iowa State University, Iowa, USA, 1996
12 Smith, S. D., Earthmoving Productivity Estimation Using Linear Regression Techniques, Journal of Construction Engineering and Management, Vol. 125, No.3, pp. 133-141, 1999   DOI   ScienceOn
13 Thomas, S. R., 'Quantitative Methods for Project Analysis,' Class (CE 395R.6) Note, Dept. of Civil Eng., University of Texas at Austin, TX, USA, 2003
14 Herbsman, Z. J. and Ellis, R., 'Synthesis of Highway Practice 215: Determination of Contract Time for Highway Construction Projects,' Transportation Research Board, National Cooperative Highway Research Program, National Academy Press, Washington, D.C., 1995
15 Sanders, S. R.,Thomas, H. R.,and Smith, G. R., An Analysis of Factors Affecting Labor Productivity in Masonry Construction, PTI Rep. 9003, Pennsylvania Transportation Institute, Pennsylvania State University, University Park, Pa,1989
16 Thomas, H. R. and Yiakoumis, I., Factor Model of Construction Productivity, Journal of Construction Engineering and Management, Vol. 113, No.4, pp. 623-639, 1987   DOI   ScienceOn
17 Wonnacott, T.H. and Wonnacott, R.J., 'Regression: A Second Course in Statistics,' Krieger Publishing Company, Malabar, Florida, USA, 1986
18 Christian, J. and Hachey, D., Effects of Delay Times on Production Rates in Construction, Journal of Construction Engineering and Management, Vol. 121, No.1, pp.20-26, 1995   DOI   ScienceOn
19 Chao, L. C., and Skibniewski, M. J., Estimating Construction Productivity: Neural-Network-based Approach, Journal of Computing. in Civil Engineering, Vol. 8, No.2, 234-251, 1994
20 Sanders, S. R. and Thomas, H. R., Masonry Productivity Forecasting Model, Journal of Construction Engineering and Management, Vol. 119, No.1, pp.163-179, 1993   DOI
21 Hanna, A. S., Peterson P., and Lee, M., Benchmarking Productivity Indicators for Electrical/mechanical Projects, Journal of Construction Engineering and Management, Vol. 128, No.4, pp.331-337, 2002   DOI   ScienceOn