Abstract
Owners need clear information about the cost at the early stage, while information about drawings and designs are limited in this stage. The purpose of this research is to propose a hybrid model using artificial neural networks and the regression analysis to estimate the cost of residential buildings in Egypt. Real data were collected from 174 real residential projects in Egypt. The Delphi technique was used to reach a consensus on the key factors affecting early stage cost estimation. Artificial neural network models were developed with various numbers of hidden layers and types of activation functions. The sensitivity analysis showed that the most effective factors in the cost estimate at the early stage are the number of floors and the area of the floors. The multiple linear regression, polynomial regression, gamma regression, and Poisson regression were used. The proposed hybrid model was extracted from the ANN model and the regression models using multiple linear regression. The mean absolute percentage error of the hybrid model was 10.64% which is less than the absolute percentage error of the ANN model and the regression models. The results of the hybrid model indicate that the model was successful in estimating the cost of residential projects and would be useful for decision-makers in the construction industry.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
An-najjar, H. M. M. (2005). Prediction of ultimate shear strength of reinforced concrete deep beam using Artificial Neural Network. Master of the science of civil engineering. The Islamic University of Gaza, Palestine. http://hdl.handle.net/20.500.12358/19178. Accessed 3 May 2018.
Antoniou, F., Konstantinidis, D., Aretoulis, G., & Xenidis, Y. (2018). Preliminary construction cost estimates for motorway underpass bridges. International Journal of Construction Management,18(4), 321–330. https://doi.org/10.1080/15623599.2017.1358076.
Arabzadeh, V., Niaki, S. T. A., & Arabzadeh, V. (2018). Construction cost estimation of spherical storage tanks: Artificial neural networks and hybrid regression- GA algorithms. Journal of Industrial Engineering International,14(4), 747–756. https://doi.org/10.1007/s40092-017-0240-8.
Arafa, M., & Alqedra, M. (2011). Early stage cost estimation of buildings construction projects using artificial neural networks. Journal of Artificial Intelligence,4(1), 63–75. https://doi.org/10.3923/jai.2011.63.75.
Attal, A. (2010). Development of neural network models for prediction of highway construction cost and project duration. Doctoral dissertation, Ohio University. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1282146503.
Barros, L. B., Marcy, M. I., & Carvalho, M. T. (2018). Construction cost estimation of Brazilian highways using artificial neural networks. International Journal of Structural and Civil Engineering Research,7(3), 283–289.
Bazan-Krzywoszanska, A., & Bereta, M. (2018). The use of urban indicators in forecasting a real estate value with the use of deep neural network. Reports on Geodesy and Geoinformatics,106(1), 25–34. https://doi.org/10.2478/rgg-2018-0011.
Binu, V. S., Mayya, S. S., & Dhar, M. (2014). Some basic aspects of statistical methods and sample size determination in health science research. Ayu,35(2), 119–123. https://doi.org/10.4103/0974-8520.146202.
Cheng, M. Y., Tsai, H. C., & Sudjono, E. (2010). Conceptual cost estimates using an evolutionary fuzzy hybrid neural network for projects in the construction industry. Expert Systems with Applications,37(6), 4224–4231. https://doi.org/10.1016/j.eswa.2009.11.080.
Coffie, G. H., Aigbavboa, C. O., & Thwala, W. D. (2019). Modelling construction completion cost in Ghana public sector building projects. Asian Journal of Civil Engineering,20(7), 1063–1070. https://doi.org/10.1007/s42107-019-00165-7.
Dogan, S. Z. (2005). Using machine learning techniques for early cost estimation of a structural system of buildings. IZMIR: Doctor Of Philosophy in Architecture. Izmir Institute of Technology.
Duran, O., Rodriguez, N., & Consalter, L. A. (2009). Neural networks for cost estimation of shell and tube heat exchangers. Expert Systems with Applications,36(4), 7435–7440. https://doi.org/10.1016/j.eswa.2008.09.014.
Elfadaly, F. G., Garthwaite, P. H., & Crawford, J. R. (2016). On point estimation of the abnormality of a Mahalanobis index. Computational Statistics & Data Analysis,99, 115–130. https://doi.org/10.1016/j.csda.2016.01.014.
El-Sawalhi, N. I., & Shehatto, O. (2014). A neural network model for building construction projects cost estimating. Journal of Construction Engineering and Project Management. https://doi.org/10.6106/jcepm.2014.4.4.009.
Feng, G. L., & Li, L. (2013). Application of genetic algorithm and neural network in construction cost estimate. In S. Z. Cai & Q. F. Zhang (Eds.), Advanced materials research (Vol. 756, pp. 3194–3198). Atlantis Press, Paris. https://doi.org/10.4028/www.scientific.net/AMR.756-759.3194.
García, B., Adey, B. T., & Fernando, D. (2017). A hybrid methodology to estimate construction material quantities at an early project phase. International Journal of Construction Management,17(3), 165–196. https://doi.org/10.1080/15623599.2016.1176727.
Günaydın, H. M., & Doğan, S. Z. (2004). A neural network approach for early cost estimation of structural systems of buildings. International Journal of Project Management,22(7), 595–602. https://doi.org/10.1016/j.ijproman.2004.04.002.
Gunduz, M., & Bayram, H. S. (2015). An early cost estimation model for hydroelectric power plant projects using neural network and multiple regression analysis. Journal of Civil Engineering and Management,21(4), 470–477. https://doi.org/10.3846/13923730.2014.890657.
Gupta, B. B., Joshi, R. C., & Misra, M. (2011). Prediction of number of zombies in a DDoS attack using polynomial regression model. Journal of Advances In Information Technology,2(1), 57–62. https://doi.org/10.4304/jait.2.1.57-62.
Haddad, E. (2016). Quantities predictor model (QPM) based on artificial neural networks for Gaza Strip building contractors. Journal of Engineering Research and Technology,3(2), 35–43.
Hashimoto, E. M., Ortega, E. M., Cordeiro, G. M., & Hamedani, G. G. (2017). The log-gamma-logistic regression model: Estimation, sensibility and residual analysis. Journal of Statistical Theory and Applications,16(4), 547–564. https://doi.org/10.2991/jsta.2017.16.4.9.
Hyari, K. H., Al-Daraiseh, A., & El-Mashaleh, M. (2016). Conceptual cost estimation model for engineering services in public construction projects. Journal of Management in Engineering. https://doi.org/10.1061/(asce)me.1943-5479.0000381.
Jusczyk, M. (2017). The challenges of non-parametric cost estimation of construction works with the use of artificial intelligence tools. Procedia Engineering,196, 415–422. https://doi.org/10.1016/j.proeng.2017.07.218.
Juszczyk, M., & Leśniak, A. (2016). Site overhead cost index prediction using RBF Neural Networks. DEStech Transactions on Economics, Business and Management (ICEM). https://doi.org/10.12783/dtem/icem2016/4096.
Kim, G. H., Shin, J. M., Kim, S., & Shin, Y. (2013). Comparison of school building construction costs estimation methods using regression analysis, neural network, and support vector machine. Journal of Building Construction and Planning Research,1(01), 1.
Magdum, S. K., & Adamuthe, A. C. (2017). Construction cost prediction using neural networks. ICTACT Journal on Soft Computing. https://doi.org/10.21917/ijsc.2017.0220.
Nishiyama, M., & Filion, Y. (2013). Review of statistical water main break prediction models. Canadian Journal of Civil Engineering,40(10), 972–979. https://doi.org/10.1139/cjce-2012-0424.
Sanad, A., & Saka, M. P. (2001). Prediction of ultimate shear strength of reinforced-concrete deep beams using neural networks. Journal of Structural Engineering,127(7), 818–828. https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(818).
Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: appropriate use and interpretation. Anesthesia and Analgesia,126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864.
Shehatto, O. (2013). Factors cost estimation for building construction projects in Gaza Strip using Artificial Neural Network (ANN). M.Sc. thesis, The Islamic University of Gaza, Palestine.
Tijanić, K., Car-Pušić, D., & Šperac, M. (2019). Cost estimation in road construction using artificial neural network. Neural Computing and Applications. https://doi.org/10.1007/s00521-019-04443-y.
Tosun, E., Aydin, K., & Bilgili, M. (2016). Comparison of linear regression and artificial neural network model of a diesel engine fueled with biodiesel-alcohol mixtures. Alexandria Engineering Journal,55(4), 3081–3089. https://doi.org/10.1016/j.aej.2016.08.011.
Unsal, M. A., Aydinli, S. & Oral, E. (2017). Cost estimation of reinforced concrete structural system via artificial neural network models. In ICOCEE conference, May 8-10, At Nevşehir /Turkey 2182-2190.
Urquhart, B., Ghonima, M., Nguyen, D. (Andu), Kurtz, B., Chow, C.W., Kleissl, J. (2013). Sky-imaging systems for short-term forecasting. In J. Kleiss (Ed.), Solar energy forecasting and resource assessment (pp. 195–232). Elsevier, UK. https://doi.org/10.1016/b978-0-12-397177-7.00009-7.
Wang, Y. R., Yu, C. Y., & Chan, H. H. (2012). Predicting construction cost and schedule success using artificial neural networks ensemble and support vector machines classification models. International Journal of Project Management,30(4), 470–478. https://doi.org/10.1016/j.ijproman.2011.09.002.
Yong, Y. C., & Mustaffa, N. E. (2012). Analysis of factors critical to construction project success in Malaysia. Engineering, Construction and Architectural Management,19(5), 543–556. https://doi.org/10.1108/09699981211259612.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Badawy, M. A hybrid approach for a cost estimate of residential buildings in Egypt at the early stage. Asian J Civ Eng 21, 763–774 (2020). https://doi.org/10.1007/s42107-020-00237-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42107-020-00237-z