A regression-based model for parametric cost estimation of industrial steel structures
Abstract
Construction industry is considered one of the most versatile industries characterized by uncertainties and risk. Estimating the steel structure cost of industrial buildings is a challenging task compared with traditional buildings due to the uniqueness of this class of projects. This paper aims to introduce an effective and accurate parametric model for construction cost estimation of industrial steel structures. The paper proposes a regression-based model for estimating the cost of a critical construction component: the industrial steel structure where the is not enough historical data is available. The factors that affect the construction cost of industrial steel structures are initially identified based on the literature and interviews with local experts. The correlation between input factors and model’s output is then investigated. In addition, sensitivity analysis is performed to examine the relative importance of the regression model’s inputs. The model is validated using actual data on industrial steel structure costs in Saudi Arabia. The model adequately predicted the construction costs of actual projects with an accuracy of more than 88%. This indicates that the model is capable of accurately predicting the cost of such structures. The proposed model can be of great assistance to investors and decision-makers looking to invest in the industrial sector.
First published online 10 December 2024
Keyword : construction, industrial steel structures, parametric cost estimation, multiple linear regression
How to Cite
Alshibani, A., Almuhtaseb, O., Mohammed, A., & Ghaithan, A. M. (2024). A regression-based model for parametric cost estimation of industrial steel structures. Journal of Civil Engineering and Management, 1-14. https://doi.org/10.3846/jcem.2024.22472
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Sonmez, R. (2004). Conceptual cost estimation of building projects with regression analysis and neural networks. Canadian Journal of Civil Engineering, 31(4), 677–683. https://doi.org/10.1139/l04-029
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Alshamrani, O. S. (2017). Construction cost prediction model for conventional and sustainable college buildings in North America. Journal of Taibah University for Science, 11(2), 315–323. https://doi.org/10.1016/j.jtusci.2016.01.004
Alshibani, A., & Alshamrani, O. S. (2017). ANN/BIM-based model for predicting the energy cost of residential buildings in Saudi Arabia. Journal of Taibah University for Science, 11(6), 1317–1329. https://doi.org/10.1016/j.jtusci.2017.06.003
Arafah, 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
Badawy, M. (2020). A hybrid approach for a cost estimate of residential buildings in Egypt at the early stage. Asian Journal of Civil Engineering, 21, 763–774. https://doi.org/10.1007/s42107-020-00237-z
Badra, I., Badawy, M., & Attabi, M. (2020). Conceptual cost estimate of buildings using regression analysis in Egypt. IOSR Journal of Mechanical and Civil Engineering, 17(5), 29–35.
Cho, H.-G., Kim, K.-G., Kim, J.-Y., & Kim, G.-H. (2013). A comparison of construction cost estimation using multiple regression analysis and neural network in elementary school project. Journal of the Korea Institute of Building Construction, 13(1), 66–74. https://doi.org/10.5345/JKIBC.2013.13.1.066
Chopra, P., Sharma, R. K., & Kumar, M. (2014). Regression models for the prediction of compressive strength of concrete with and without fly ash. International Journal of Latest Trends in Engineering and Technology, 3(4), 400–406.
Chro, A. (2021). Early cost estimation models based on multiple regression analysis for road and railway tunnel projects. Arabian Journal of Geosciences, 14, Article 972. https://doi.org/10.1007/s12517-021-07359-x
Dang, C. N., & Le-Hoai, L. (2018). Revisiting storey enclosure method for early estimation of structural building construction cost. Engineering, Construction and Architectural Management, 25(7), 877–895. https://doi.org/10.1108/ECAM-07-2015-0111
Dharwadkar, N. V., & Arage, S. S. (2018). Prediction and estimation of civil construction cost using linear regression and neural network. International Journal of Intelligent Systems Design and Computing, 2(1), 28–44. https://doi.org/10.1504/IJISDC.2018.092554
El-Sawah, H., & Moselhi, O. (2014). Comparative study in the use of neural networks for order of magnitude cost estimating in construction. ITcon, 19, 462–473.
Fragkakis, N., Marinelli, M., & Lambropoulos, S. (2015). Preliminary cost estimate model for culverts. Procedia Engineering, 123, 153–161. https://doi.org/10.1016/j.proeng.2015.10.072
Gujarati, D. N. (2003). Basic econometrics (4th ed.). McGraw-Hill.
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., Ugur, L. O., & Ozturk, E. (2011). Parametric cost estimation system for light rail transit and metro trackworks. Expert Systems with Applications, 38(3) 2873–2877. https://doi.org/10.1016/j.eswa.2010.08.080
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
Hegazy, T., & Ayed, A. (1998). Neural network model for parametric cost estimation of highway projects. Journal of Construction Engineering and Management, 124(3), 210–218. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:3(210)
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer. https://doi.org/10.1007/978-1-4614-7138-7
Kamarthi, S., Sanvido, V., & Kumara, S. (1992). Neuroform—Neural network system for vertical formwork selection. Journal of Computing in Civil Engineering, 6(2), 178–199. https://doi.org/10.1061/(ASCE)0887-3801(1992)6:2(178)
Kim, G. H., An, S. H., & Kang, K. I. (2004). Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning. Building and Environment, 39(10), 1235–1242. https://doi.org/10.1016/j.buildenv.2004.02.013
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, 1–17. https://doi.org/10.4236/jbcpr.2013.11001
Latief, Y., Wibowo, A., & Isvara, W. (2013). Preliminary cost estimation using regression analysis incorporated with adaptive neuro fuzzy inference system. International Journal of Technology, 4(1), 63–72. https://doi.org/10.14716/ijtech.v4i1.102
Lowe, D. J., Emsley, M. W., & Harding, A. H. (2006). Predicting construction cost using multiple regression techniques. Journal of Construction Engineering and Management, 132(7), 750–758. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:7(750)
Lu, M., AbouRizk, S. M., & Hermann, U. H. (2000). Estimating labor productivity using probability inference neural network. Journal of Computing in Civil Engineering, 14(4), 241–248. https://doi.org/10.1061/(ASCE)0887-3801(2000)14:4(241)
Mahamid, I. (2011). Early cost estimating for road construction projects using multiple regression techniques. Australasian Journal of Construction Economics and Building, 11(4), 87–101. https://doi.org/10.5130/AJCEB.v11i4.2195
Mahamid, I. (2013). Conceptual cost estimate of road construction projects in Saudi Arabia. Jordan Journal of Civil Engineering, 7(3), 285–294.
Mahamid, I., & Bruland, A. (2010). Preliminary cost estimating models for road construction activities. In FIG Congress 2010. Facing the Challenges – Building the Capacity, Sydney, Australia.
Moselhi, O., & Siqueira, I. (1998). Neural networks for cost estimating of structural steel buildings. In Proceedings of the AACE International Transactions, IT/IM.06. American Association of Cost Engineers (AACE), Morgantown, WV.
Ofori-Boadu, A. N. (2015). Exploring regression models for forecasting early cost estimates for high-rise buildings. The Journal of Technology, Management, and Applied Engineering, 31(5).
Petroutsatou, C., Lambropoulos, S., & Pantouvakis, J.-P. (2006). Road tunnel early cost estimates using multiple regression analysis. Operational Research, 6, 311–322. https://doi.org/10.1007/BF02941259
Roxas, C. L. C., & Ongpeng, J. M. C. (2014). An artificial neural network approach to structural cost estimation of building projects in the Philippines. In DLSU Research Congress 2014, Manila, Philippines.
Sanni-Anibire, M. O., Zin, R. M., & Olatunji, S. O. (2021). Developing a preliminary cost estimation model for tall buildings based on machine learning. International Journal of Management Science and Engineering Management, 16(2), 134–142. https://doi.org/10.1080/17509653.2021.1905568
Shin, Y. (2015). Application of boosting regression trees to preliminary cost estimation in building construction projects. Computational Intelligence and Neuroscience, 2015(4), Article 149702. https://doi.org/10.1155/2015/149702
Siqueira, I. (1999). Neural network-based cost estimating [Master thesis]. Concordia University, Montreal, Quebec, Canada.
Sonmez, R. (2004). Conceptual cost estimation of building projects with regression analysis and neural networks. Canadian Journal of Civil Engineering, 31(4), 677–683. https://doi.org/10.1139/l04-029
Sonmez, R., & Ontepeli, B. (2009). Predesign cost estimation of urban railway projects with parametric modeling. Journal of Civil Engineering and Construction Management, 15, 405–409. https://doi.org/10.3846/1392-3730.2009.15.405-409
U.S. Department of Defense. (1995). Parametric cost estimating handbook. Department of Defense, Arlington, VA, USA.
Wang, X.-Z., Duan, X.-c., & Liu, J.-y. (2010). Application of neural network in the cost estimation of highway engineering. Journal of Computers, 5(11), 1762–1766. https://doi.org/10.4304/jcp.5.11.1762-1766
Xue, X., Jia, Y., & Tang, Y. (2020). Expressway project cost estimation with a convolutional neural network model. IEEE Access, 8, 217848–217866. https://doi.org/10.1109/ACCESS.2020.3042329