Conceptual Estimation of Construction Costs Using the Multistep Ahead Approach
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 9
Abstract
Providing accurate forecasts of construction costs at the conceptual phases of building projects is vital since they form an objective benchmark for the subsequent evaluation of project performance. Previous works adopted a conventional approach in which a restricted set of macro project determinants, which are available in the preplanning phase, was employed towards direct estimation of construction costs. Aiming to reduce the prediction error in conceptual estimates, the current study adopts a novel approach from the domain of forecasting. This multistep ahead (MSA) approach relies on the idea of using several cascaded estimations to predict future values. Accordingly, building element quantities were estimated as the first step. In the second step, estimated quantities were combined with the existing set of inputs to achieve a higher accuracy in construction cost prediction. In order to test the hypotheses of interest, 657 building projects from Germany were analyzed using linear regression and artificial neural network methods. Conclusive evidence suggests that the MSA approach significantly outperforms the prediction accuracy of the conventional practice. To the best of authors’ investigation, the current study is the first to offer such a cascaded estimation approach. Therefore, further empirical evidence is necessary prior to generalizing applicability of the MSA approach in construction cost estimation.
Get full access to this article
View all available purchase options and get full access to this article.
References
BKI. (2012). “BKI cost planner—New/existing structures, free facilites [BKI Kostenplaner—Neu-/Altbau, Freianlagen].” Kostenplanner, Baukosteninformationszentrum Deutscher Architektenkammern, 18th Ed., Stuttgart, Germany.
Chatterjee, S., and Hadi, A. S. (2013). Regression analysis by example, Wiley, Hoboken, NJ.
Cheng, M.-Y., and Hoang, N.-D. (2014). “Interval estimation of construction cost at completion using least squares support vector machine.” J. Civ. Eng. Manage., 20(2), 223–236.
Choi, S., Kim, D. Y., Han, S. H., and Kwak, Y. H. (2013). “Conceptual cost-prediction model for public road planning via rough set theory and case-based reasoning.” J. Constr. Eng. Manage., 04013026.
Dong, G., Fataliyev, K., and Wang, L. (2013). “One-step and multi-step ahead stock prediction using backpropagation neural networks.” Proc., IEEE 2013 9th Int. Conf. on Information, Communications and Signal Processing (ICICS), IEEE, NJ, 1–5.
Elfaki, A. O., Alatawi, S., and Abushandi, E. (2014). “Using intelligent techniques in construction project cost estimation: 10-year survey.” Adv. Civ. Eng., 2014, 11.
Emsley, M. W., Lowe, D. J., Duff, A. R., Harding, A., and Hickson, A. (2002). “Data modelling and the application of a neural network approach to the prediction of total construction costs.” Constr. Manage. Econ., 20(6), 465–472.
Fellows, R. F., and Liu, A. M. (2009). Research methods for construction, Wiley, West Sussex, U.K.
Günaydın, M. H., and Doğan, Z. S. (2004). “A neural network approach for early cost estimation of structural systems of buildings.” Int. J. Project Manage., 22(7), 595–602.
Hegazy, T., and Ayed, A. (1998). “Neural network model for parametric cost estimation of highway projects.” J. Constr. Eng. Manage., 210–218.
Jafarzadeh, R., Ingham, J., Wilkinson, S., González, V., and Aghakouchak, A. (2013). “Application of artificial neural network methodology for predicting seismic retrofit construction costs.” J. Constr. Eng. Manage., 04013044.
Ji, S.-H., Park, M., and Lee, H.-S. (2011). “Case adaptation method of case-based reasoning for construction cost estimation in Korea.” J. Constr. Eng. Manage., 43–52.
Karshenas, S. (1984). “Predesign cost estimating method for multistory buildings.” J. Constr. Eng. Manage., 79–86.
Khosrowshahi, F., and Kaka, A. P. (1996). “Estimation of project total cost and duration for housing projects in the UK.” Build. Environ., 31(4), 375–383.
Kim, G.-H., An, S.-H., and Kang, K.-I. (2004). “Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning.” Build. Environ., 39(10), 1235–1242.
Kim, S. (2013). “Hybrid forecasting system based on case-based reasoning and analytic hierarchy process for cost estimation.” J. Civ. Eng. Manage., 19(1), 86–96.
Kouskoulas, V., and Koehn, E. (1974). “Predesign cost-estimation function for buildings.” J. Constr. Div., 100(4), 589–604.
Li, H., Shen, Q., and Love, P. E. (2005). “Cost modelling of office buildings in Hong Kong: An exploratory study.” Facilities, 23(9/10), 438–452.
Lowe, D. J., Emsley, M. W., and Harding, A. (2006). “Predicting construction cost using multiple regression techniques.” J. Constr. Eng. Manage., 750–758.
Mahamid, I. (2011). “Early cost estimating for road construction projects using multiple regression techniques.” Constr. Econ. Build., 11(4), 87–101.
Newton, S. (1991). “An agenda for cost modelling research.” Constr. Manage. Econ., 9(2), 97–112.
Petroutsatou, K., Georgopoulos, E., Lambropoulos, S., and Pantouvakis, J. (2011). “Early cost estimating of road tunnel construction using neural networks.” J. Constr. Eng. Manage., 679–687.
Skitmore, R. M., and Ng, S. T. (2003). “Forecast models for actual construction time and cost.” Build. Environ., 38(8), 1075–1083.
Son, H., Kim, C., and Kim, C. (2012). “Hybrid principal component analysis and support vector machine model for predicting the cost performance of commercial building projects using pre-project planning variables.” Autom. Constr., 27, 60–66.
Sonmez, R. (2004). “Conceptual cost estimation of building projects with regression analysis and neural networks.” Can. J. Civ. Eng., 31(4), 677–683.
Sonmez, R. (2008). “Parametric range estimating of building costs using regression models and bootstrap.” J. Constr. Eng. Management, 1011–1016.
Sonmez, R. (2011). “Range estimation of construction costs using neural networks with bootstrap prediction intervals.” Exp. Syst. Appl., 38(8), 9913–9917.
Specht, D. F. (1991). “A general regression neural network.” IEEE Trans. Neural Netw., 2(6), 568–576.
Stoy, C., Pollalis, S., and Schalcher, H.-R. (2008). “Drivers for cost estimating in early design: Case study of residential construction.” J. Constr. Eng. Manage., 32–39.
Tang, L., Yu, L., Wang, S., Li, J., and Wang, S. (2012). “A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting.” Appl. Energy, 93, 432–443.
Wang, Y.-R., and Gibson, G. E. (2010). “A study of preproject planning and project success using ANNs and regression models.” Autom. Constr., 19(3), 341–346.
Wang, Y.-R., Yu, C.-Y., and Chan, H.-H. (2012). “Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models.” Int. J. Project Manage., 30(4), 470–478.
Xiong, T., Bao, Y., and Hu, Z. (2013). “Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices.” Energy Econ., 40, 405–415.
Yao, B., Yao, J., Zhang, M., and Yu, L. (2014). “Improved support vector machine regression in multi-step-ahead prediction for rock displacement surrounding a tunnel.” Scientia Iranica Trans. A Civ. Eng., 21(4), 1309–1316.
Zhang, X. (1994). “Time series analysis and prediction by neural networks.” Optim. Meth. Software, 4(2), 151–170.
Zhang, X., and Hutchinson, J. (1994). “Simple architectures on fast machines: Practical issues in nonlinear time series prediction.” Time series prediction: forecasting the future and understanding the past, A. S. Weigend and N. A. Gershenfeld, eds., Addison-Wesley, Reading, MA.
Information & Authors
Information
Published In
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 11, 2015
Accepted: Dec 30, 2015
Published online: Mar 15, 2016
Discussion open until: Aug 15, 2016
Published in print: Sep 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.