Research for the Cost Forecasting of Construction Project

Abstract:

Traditional approach to cost forecasting uses a single model for the entire construction period. However, different stages in a construction project require different financial obligations. A better approach to improve the accuracy of cost forecasting is to break the duration of the entire construction project into three stages. It is the attempt of this research to improve the traditional GM (1, 1) Grey Prediction Model by defining the proper  in place of 0.5. The new technique adopts the Golden Section Method to analyze the optimization in defining and build the cost forecasting model for each phase. The results show that the GM (1,1) with proper  can more accurately forecast the expenditure for each month.

• Periodical: Applied Mechanics and Materials (Volumes 256-259)
• Pages: 3038-3042
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.256-259.3038
• Online since: December 2012
• Authors: Ying Mei Cheng
• Keywords: Cost Forecast, Golden Section Method, Grey Prediction, S-Curve

References

[1] Peer, S., Application of cost-flow forecasting models, Journal of the Construction Division, Proceedings of the American Society of Civil Engineers, Vol. 108, no. CO2 (1982), pp.226-232.

[2] Kenley, R., Wilson, O, A construction project cash flow model – an idiographic approach, Construction Management and Economics, Vol. 4 (3) (1986), pp.213-232.

DOI: 10.1080/01446198600000017

[3] Navon, R., Cash flow forecasting and updating for building projects, Project Management Journal, Vol. 27(2) (1996), p.14–23.

[4] Skitmore, M., A method for forecasting owner monthly construction project expenditure flow, International Journal of Forecasting, Vol. 14(1) (1998), pp.17-34.

DOI: 10.1016/s0169-2070(97)00042-3

[5] Kaka, A. P., John, L., Development of a company-level dynamic cash flow forecasting model, Construction Management and Economic, Vol. 21(7) (2003), pp.693-705.

DOI: 10.1080/0144619032000116561

[6] Hsiang, Y. L., The GM(1, 1) and its applications in the environmental system, Grey System Research & Development, Hwa Chung University of Science & Technology (1996).

[7] Hsin, J. Y., Tsai, Y. P, The study on value adjustment method of grey prediction Z(1)(k), Grey System Theory Seminar (2000), pp.305-308.

[8] Pan, C. L., Huang, Y. F., Lin, G., The study on algorithm of grey prediction Z(1)(k) with iterative method as basis, Grey System Theory Seminar (2002), p. I-27-I-31.

[9] Li, J., Yang, A., Dai, W., New approach of building GM(1, 1) background value and its application, Proceedings of the IEEE International Conference on Automation and Logistics August 18 – 21 (2007) , Jinan, China, pp.16-19.

DOI: 10.1109/ical.2007.4338522

[10] Zhang, C., Dai, W., Improvement of background value and its application in Non-equidistance GM(1, 1) modeling, Proceedings of the 7th World Congress on Intelligent Control and Automation, June 25 - 27 (2008), Chongqing, China, pp.209-212.

DOI: 10.1109/wcica.2008.4594429

[11] Lin, Y. H., Lee, P. C., Chang, T. P., Adaptive and high-precision grey forecasting model, Expert Systems with Applications, Vol. 36(6) (2009), p.9658–9662.

DOI: 10.1016/j.eswa.2008.12.009

[12] Lewis, C. D., Industrial and Business Forecasting Methods, Butterworths, (1982).

[13] Gates, M., Scarpa, A., Preliminary cumulative cash flow analysis, Cost Engineering, Vol. 21(6) (1979), pp.243-249.

[14] Cheng, Y. M., Short-Interval dynamic forecasting for actual S-curve in the construction phase, Journal of Construction Engineering and Management, Vol. 137(11) (2011), pp.933-941.

DOI: 10.1061/(asce)co.1943-7862.0000358

[15] Yu, C.H., Research for the Cost Forecasting of Each Phase of Construction Project, Department of Civil Engineering and Hazard Mitigation Design, China University of Technology (2004).