Efficacy of Estimation Methods in Forecasting Building Projects’ Costs
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 11
Abstract
This paper investigates the adequacy of some nontraditional approaches to produce realistic forecasts of the final costs of building projects and compare them with forecasts produced by three traditional methods, the unit area costs (UAC), client detailed costs (UPA), and contract sums (CS). As a case study, data of the actual final costs and forecasts produced using the three traditional methods for 420 finished public building projects carried out in Turkey were collected. Based on 75% of the collected data (i.e., 316 projects), three cost models based on UAC, UPA, and CS were established. The remaining 25% of the data (104 projects) were used as control data for testing the accuracies of forecasts produced by the different approaches. Cost forecasts using five nontraditional approaches, namely multilayer perceptron (MLP), radial basis function (RBF), grid partitioning algorithm (GPA), reference class forecasting (RCF), and regression analysis (RA) were then produced and compared with the actual final costs of projects of the control data. The forecasts were compared using four standard error measures: root-mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and overall index of model performance (OI). Results of the analysis have shown that although the efficacy of each method to produce forecasts closer to the actual final costs varied depending on the model used, in general however, RCF and RA seem to produce more accurate and realistic forecasts than the other methods. As such the work described in the paper represents valuable contributions to knowledge and practice, firstly because RCF has not been used or tested before for cost forecasting of building projects. Secondly such comparison among the various methods and approaches used has not been found in published literature. Finally the paper provides guidance as to what approach to use at what stage of the project as well as how realistic the expected forecasts are.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors would like to thank the Scientific and Technical Research Council of Turkey (TUBITAK) for the contribution to the corresponding author with 2219-International Postdoctoral Research Scholarship Programme.
References
Arslan, O., Kurt, O., and Konak, H. (2007). “Suggestions on geodesy applications of artificial neural networks (Yapay sinir ağlarının jeodezide uygulamaları üzerine öneriler).” 11th Turkey Geodesy Science and Technical Congress, Chamber of Survey and Cadastre Engineers, Union of Chambers of Turkish Engineers and Architects, Ankara, Turkey.
Baalousha, Y., and Çelik, T. (2011). “An integrated web-based data warehouse and artificial neural networks system for unit price analysis with inflation adjustment.” J. Civ. Eng. Manage., 17(2), 157–167.
Başyiğit, C., Kaçar Akkaş, A., and Kurtarıcı, M. N. (2012). “Betonların radyasyon zırh kalınlıklarının yapay sinir ağları ve çoklu regresyon metotları ile tahmini [Prediction of radiation shielding thickness of concretes by artificial neural networks and multiple regression methods].” Suleyman Demirel University J. Nat. Appl. Sci., 16(1), 77–81.
Bayram, S. (2013). “Türkiye kamu inşaat projelerindeki maliyet ve süre sapmalarinin yapay zekâ yöntemleri ile kiyaslamali analizi [Comparative analysis of cost and schedule variances in public construction projects with artificial intelligence approaches: The case of Turkey].” Ph.D. thesis, Erciyes Univ., Kayseri, Turkey.
Bayram, S., Öcal, M. E., and Laptalı Oral, E. (2012). “Analysis of cost and schedule variances in construction works with artificial intelligence approaches: The case of Turkey.” Int. Students’ Conf. of Civil Engineering (ISCCE 2012), Epoka Univ., Tirana, Albania.
Bayram, S., Öcal, M. E., Laptalı Oral, E., and Atiş, C. D. (2015). “Comparison of multi-layer perceptron (MLP) and radial basis function (RBF) for construction cost estimation: The case of Turkey.” J. Civ. Eng. Manage., in press.
Berthouex, P. M., and Brown, L. C. (2002). Statistics for environmental engineers, 2nd Ed., Lewis Publishers, Boca Raton, FL.
Bisen, Ö., and Dikmen, S. Ü. (2012). “Üstyapı projelerinin maliyet tahmin çalışmalarında belirsizliklerin yapay zeka teknikleriyle analizi [Analysis of uncertainties for the cost estimation of building projects using artificial intelligence techniques].” e-J. New World Sci. Acad. (NWSA), 7(2), 394–403.
Chen, S., Cowan, C. F. N., and Grant, P. M. (1991). “Orthogonal least squares learning algorithm for radial basis function networks.” IEEE Trans. Neural Netw., 2(2), 302–309.
Civelekoğlu, G. (2006). “Arıtma proseslerinin yapay zekâ ve çoklu istatistiksel yöntemler ile modellenmesi [The modeling of treatment processes with artificial intelligence and multistatistical methods].” Ph.D. thesis, Suleyman Demirel Univ., Isparta, Turkey.
Çıtakoğlu, H. (2015). “Comparison of artificial intelligence techniques via empirical equations for prediction of solar radiation.” Comput. Electron. Agric., 118, 28–37.
Doğan, S., Arditi, D., and Günaydın, H. M. (2008). “Using decision trees for determining attribute weights in a case-based model of early cost prediction.” J. Constr. Eng. Manage., 146–152.
Erdiş, E. (2013). “The effect of current public procurement law on duration and cost of construction projects in Turkey.” J. Civ. Eng. Manage., 19(1), 121–135.
Eythorsdottir, E. O. (2012). “Reference class forecasting method used in Icelandic transportation infrastructure projects.” M.Sc. thesis, Reykjavík Univ., Reykjavík, Iceland.
Flyvbjerg, B. (2004). “Procedures for dealing with optimism bias in transport planning.”, British Dept. of Transport, U.K.
Flyvbjerg, B. (2011). “Over budget, over time, over and over again managing major projects.” Oxford handbook of project management, P. W. Morris, J. K. Pinto, and J. Söderlund, eds., Oxford University Press, U.K., 321–344.
Göktürk, İ. (2007). “İnşaat sektöründe fizibilite aşamasinda maliyet tahmini yapmakta karşilaşilan zorluklar ve çözüm önerileri üzerine bir değerlendirme [An evaluation about difficulties and solutions in the step of cost estimating for feasibility process in the construction sector].” M.Sc. thesis, İstanbul Technical Univ., Istanbul, Turkey.
Gülçiçek, U., Özkan, O., Gündüz, M., and Demir, I. H. (2013). “Cost assessment of construction projects through neural networks.” Can. J. Civ. Eng., 40(6), 574–579.
Hacıoğlu, A. (2006). “Hızlı evrimsel eniyileme için yapay sinir ağı kullanılması [Using artificial neural network for rapid evolutionary optimization].” J. Aerosp. Technol., 2(3), 1–8.
HM Treasury. (2003). The green book: Appraisal and evaluation in central government, treasury guidance, TSO, London.
Kahneman, D. (2002). “Maps of bounded rationality: A perspective on intuitive judgment and choice.” The Sveriges Riksbank prize in economic sciences in Memory of Alfred Nobel, Stockholm Univ., Sweden.
Kahneman, D., and Frederick, S. (2002). “Representativeness revisited: Attribute substitution in intuitive judgment.” Heuristics and biases: The psychology of intuitive judgment, T. Gilovich, D. Griffin, and D. Kahneman, eds., Cambridge University Press, New York.
Karunanithi, N., Grenney, W. J., Whitley, D., and Bovee, K. (1994). “Neural networks for river flow prediction.” J. Comput. Civ. Eng., 201–220.
Kayadelen, C., Taşkıran, T., Günaydın, O., and Fener, M. (2009). “Adaptive neuro-fuzzy modeling for the swelling potential of compacted soils.” Environ. Earth Sci., 59(1), 109–115.
Kaynar, O., Taştan, S., and Demirkoparan, F. (2010). “Ham petrol fiyatlarının yapay sinir ağları ile tahmini [Crude oil price forecasting with artificial neural networks].” Ege Acad. Rev., 10(2), 559–573.
Kim, G. H., Seo, D. S., and Kang, K. I. (2005). “Hybrid models of neural networks and genetic algorithms for predicting preliminary cost estimates.” J. Comput. Civ. Eng., 208–211.
Kuşan, H., Aytekin, O., and Özdemir, İ. (2009). “Comparison of fuzzy logic, artificial neural network and multiple regression analysis methods in the determination of selling prices of residences.” 5th Int. Conf. on Construction in the 21st Century (CITC-V), Division of Construction Engineering and Management, Dept. of Civil Engineering, Middle East Technical Univ., Ankara, Turkey.
Lovallo, D., and Kahneman, D. (2003). “Delusions of success: How optimism undermines executives’ decisions.” Harv. Bus. Rev., 81(7), 56–63.
Love, P. E. D., Edwards, D. J., and Irani, Z. (2012). “Moving beyond optimism bias and strategic misrepresentation: An explanation for social infrastructure project cost overruns.” IEEE Trans. Eng. Manage., 59(4), 560–571.
MATLAB [Computer software]. MathWorks, Natick, MA.
Minitab Version 16.1 [Computer software]. Software for Quality Improvement, U.K.
Montgomery, D. C., Peck, E. A., and Vining, G. G. (2012). Introduction to linear regression analysis, 5th Ed., Wiley, NJ.
Oral, M., Laptalı Oral, E., and Aydın, A. (2012). “Supervised vs. unsupervised learning for construction crew productivity prediction.” Autom. Constr., 22, 271–276.
Öztemel, E. (2012). Yapay sinir ağlari [Artificial neural networks], Papatya Publishing, Istanbul, Turkey.
Polat, G. (2012). “ANN approach to determine cost contingency in international construction project.” J. Appl. Manage. Investments, 1(2), 195–201.
Prudêncio, R. B. C., and Ludermir, T. B. (2003). “Neural network hybrid learning: Genetic algorithms & Levenberg-Marquard.” Studies in classification, data analysis, and knowledge organization, M. Schader, et al., Springer, Berlin, 464–472.
Salling, K. B., and Leleur, S. (2012). “Modelling of transport project uncertainties: Feasibility risk assessment and scenario analysis.” Eur. J. Transp. Infrastruct. Res. (EJTIR), 12(1), 21–38.
Sönmez, R. (2011). “Range estimation of construction costs using neural networks with bootstrap prediction intervals.” Exp. Syst. Appl., 38(8), 9913–9917.
Turhan, N. (2006). “Kamu ihale sistemindeki değişikliğin inşaat yatirimlarinin süre ve maliyetlerine yansimalari [Reflections of changing public tender system on time and cost of construction investments].” M.Sc. thesis, Cukurova Univ., Adana, Turkey.
Uğur, L. O. (2007). “Analysis of construction costs with artificial neural networks (Yapı maliyetinin yapay sinir aği ile analizi).” Ph.D. thesis, Gazi Univ., Ankara, Turkey.
Information & Authors
Information
Published In
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Dec 2, 2015
Accepted: Mar 22, 2016
Published online: May 12, 2016
Discussion open until: Oct 12, 2016
Published in print: Nov 1, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.