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Optimized Growth Curve for Estimating Performance Measurement Baseline Depended on Domestic Construction Facility Type

  • Construction Management
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Abstract

This study describes an optimized growth curve for quantitatively estimating performance measurement baseline according to domestic construction facility types. The proposed curves is derived using the progress information of the 19 listed construction companies through the electronic disclosure system provided by the Financial Supervisory. The procedures of this study consisted of the following steps; (1) performing a preliminary review on the outline of a data collection, classification of construction facilities, and growth curve and regression used to derive the proposed curves; (2) presenting data collection, refining and preprocessing procedures; (3) deriving and verifying of the optimized model for domestic construction facility types; and (4) analyzing and discussing the results of considering the inherent characteristics of each facility type. Overall, the proposed curves were statistically significant, and found to be able to explain about 77% or more of the actual progress. The results of this study is expected to be used as an alternative to estimate the performance measurement baseline objectively in the context of domestic construction industry which is difficult to gather reliable data on various indicators to set baseline and calculate the standard progress by trial and error method.

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References

  • Alfredo, H. S. A. and Wilson, H. (2007). Probability Concepts in Engineering: Emphasis on Applications in Civil & Environmental engineering (2nd Edition), Wiley, N. J.

    Google Scholar 

  • Barraza, G. A., Back, W. E., and Mata, F. (2000). “Probabilistic monitoring of project performance using SS-curves,” Journal of Construction Engineering and Management, American Society of Civil Engineer, Vol. 126, No. 2, pp. 142–148, DOI: 10.1061/(ASCE)0733-9364 (2000)126:2(142).

    Google Scholar 

  • Bassioni, H. A., Price, A. D., and Hassan, T. M. (2004). “Performance measurement in construction,” Journal of Management in Engineering, American Society of Civil Engineer, Vol. 20, No. 2, pp. 42–50, DOI: 10.1061/(ASCE)0742-597X(2004)20:2(42).

    Google Scholar 

  • Cha, H. S. and Kim, K. H. (2013). “Development of construction Project Performance Management System (PPMS) considering project characteristics,” Journal of Construction Engineering and Management, Korea Institute of Construction Engineering and Management, Vol. 14, No. 1, pp. 82–90.

    Article  Google Scholar 

  • Cha, H. S. and Kim, T. K. (2008). “Developing measurement system for key performance indicators on building construction projects,” Journal of Construction Engineering and Management, Korea Institute of Construction Engineering and Management, Vol. 9, No. 4, pp. 120–130.

    Google Scholar 

  • Chun, J. Y., So, B. G., Choo, J. S., and Woo, S. K. (2005). “Simulation based productivity analysis for NATM operations,” Journal of The Korean Society of Civil Engineers, Korean Society of Civil Engineers, Vol. 25, No. 3D, pp. 457–462.

    Google Scholar 

  • Han, J. K., Chin, S. Y., and Kim, Y. S. (2003). “An analysis on delay factors of major trades in apartment housing projects,” Journal of the Architectural Institute of Korea, Architectural Institute of Korea, Vol. 19, No. 3, pp. 163–168.

    Google Scholar 

  • Han, S. H., Yun, S. M., and Lee, S. H. (2006). “Exploring delays of the mega construction project: The case of korea high speed railway,” Journal of The Korean Society of Civil Engineers, Korean Society of Civil Engineers, Vol. 26, No. 5D, pp. 839–848.

    Google Scholar 

  • Hwang, J. Y. (1997). “A study on the analysis procedures of nonlinear growth curve models,” Journal of the Korean Society for Quality Management, The Korean Society for Quality Management, Vol. 25, No. 1, pp. 44–55.

    Google Scholar 

  • Hyun, C. T. and Moon, H. S. (2010). “Model for predicting cost and cost range of the public office building at the planning phase,” Journal of the Architectural Institute of Korea (Structure and Construction), Architectural Institute of Korea, Vol. 26, No. 6, pp. 139–148.

    Google Scholar 

  • Kim, C. W., Kim, B. J., Yoo, W., Cho, H., and Kang, K. I. (2013). “Decisionmaking reliability estimation model based on building construction project participants’ experience,” Journal of the Korea Institute of Building Construction, Korea Institute of Building Construction, Vol. 13, No. 2, pp. 148–158, DOI: 10.5345/JKIBC. 2013.13.2.148.

    Article  Google Scholar 

  • Kim, D. S. (2008). Regression: Basic and Application (2nd Edition), Nanam publishing company, Seoul.

    Google Scholar 

  • Kim, J. H. and Kim, K. R. (2007). “Analysis of delay causation by characteristics of construction projects,” Journal of Construction Engineering and Management, Korea Institute of Construction Engineering and Management, Vol. 8, No. 1, pp. 78–86.

    Google Scholar 

  • Kim, S. G. (2010). “Risk performance indexes and measurement systems for mega construction projects,” Journal of Civil Engineering and Management, Taylor and Fancis, Vol. 16, No. 4, pp. 586–594.

    Google Scholar 

  • Korea Institute for Industrial Economics & Trade (2016). Industry Trend Brief, Korea Institute for Industrial Economics & Trade, Sejong

  • Lee, J. S. (2014). “A study on the data mining preprocessing tool for efficient database marketing,” Journal of Digital Convergence, The Society of Digital Policy and Management, Vol. 12, No. 11, pp. 257–264, DOI: 10.14400/JDC.2014.12.11.257.

    Article  Google Scholar 

  • Lee, K. W., Hong, H. U., Park, H. D., and Han, S. H. (2011). “Developing a program performance management framework for mixed-use development in urban regeneration projects,” Journal of Construction Engineering and Management, Korea Institute of Construction Engineering and Management, Vol. 12, No. 1, pp. 141–152.

    Article  Google Scholar 

  • Lee, S. B. (2008). “The study on the method of establishing performance measurement baseline for owner,” Journal of the Architectural Institute of Korea, Architectural Institute of Korea, Vol. 10, No. 2, pp. 307–314.

    Google Scholar 

  • Lee, Y. S. (2014). “A survey of DEA applications in measuring the efficiency performance of construction organizations,” Journal of Construction Engineering and Management, Korea Institute of Construction Engineering and Management, Vol. 15, No. 5, pp. 103–114.

    Article  Google Scholar 

  • Ng, S. Thomas, Cheung, S. O., Skitmore, M., and Wong, T. C. (2004). “An integrated regression analysis and time series model for construction tender price index forecasting,” Construction Management and Economics, Taylor & Francis, Vol. 22, No. 5, pp. 483–493, DOI: 10.1080/0144619042000202799.

    Article  Google Scholar 

  • Roush, W. B. and Branton, S. L. (2005). “A comparison of fitting growth models with a genetic algorithm and nonlinear regression,” Poultry Science, Oxford University Press, Vol. 84, No. 3, pp. 494–502, DOI: 10.1093/ps/84.3.494.

    Article  Google Scholar 

  • Son, C. B. and Kwon, J. S. (2007). “Risk assessment and preventive measures for the civil appeals in building construction sites,” Journal of the Architectural Institute of Korea (Structure and Construction), Architectural Institute of Korea, Vol. 23, No. 11, pp. 185–192.

    Google Scholar 

  • Sunitha, L., BalRaju, M., and Sasikiran, J. (2013). “Data mining: Estimation of missing values using lagrange interpolation technique,” International Journal of Advanced Research in Computer Engineering and Technology (IJARCET), Vol. 2, No. 4, pp. 1579–1582.

    Google Scholar 

  • Vanhoucke, M. (2011). “On the dynamic use of project performance and schedule risk information during project tracking,” Omega, Vol. 39, No. 4, pp. 416–426, DOI: 10.1016/j.omega.2010.09.006.

    Article  Google Scholar 

  • Yoo, W. S. and Hadipriono, F. C. (2007). “An Information-based forecasting model for project progress and completion using bayesian inference,” Journal of Construction Engineering and Management, Korea Institute of Construction Engineering and Management, Vol. 8, No. 4, pp. 203–213.

    Google Scholar 

  • Yoo, W. S. and Kim, W. Y. (2015). Development and Implications of International Project Risk Index (IPRI), Construction & Economy Research Institute of Korea, Seoul.

    Google Scholar 

  • Yu, I., Kim, K., Jung, Y., and Chin, S. (2007). “Comparable performance measurement system for construction companies,” Journal of Management in Engineering, American Society of Civil Engineer, Vol. 23, No. 3, pp. 131–139, DOI: 10.1061/(ASCE)0742-597X(2007)23:3(131).

    Google Scholar 

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Correspondence to Hunhee Cho.

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Kim, CW., Kim, T., Yoo, W.S. et al. Optimized Growth Curve for Estimating Performance Measurement Baseline Depended on Domestic Construction Facility Type. KSCE J Civ Eng 22, 2691–2701 (2018). https://doi.org/10.1007/s12205-017-0180-2

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  • DOI: https://doi.org/10.1007/s12205-017-0180-2

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