Budget and cost contingency CART models for power plant projects

    Md Arifuzzaman   Affiliation
    ; Uneb Gazder Affiliation
    ; Muhammad Saiful Islam   Affiliation
    ; Martin Skitmore Affiliation


Cost overruns are a ubiquitous feature of construction projects, and realistic budgeting at the development stage plays a significant role in their control. However, the application of existing models to budgeting for power plant projects is restricted by the limited amount of project-specific cost data available. This study overcomes this by using a Classification and Regression Tree (CART) approach involving mixed methods of website visits, document study, and expert opinion to predict the amount of project cost (PC) and cost contingency (CC) needed to cover probable cost increases by the use of models containing readily available project attributes and national economic parameters at the project development stage. The modeling process is demonstrated and tested with a case study involving 58 Bangladeshi power plant projects – producing average absolute errors ranging from 0.7% to 1.7% and enabling project cost, inflation rate, and GDP to be identified as significant factors affecting PC and CC modeling. The approach can be applied to predict the PC during preliminary budgeting and selecting a project type and location aligned to the country’s economic status and policy-making strategies, thus facilitating further investment decisions.

Keyword : power plant, project cost, cost contingency, prediction, CART

How to Cite
Arifuzzaman, M., Gazder, U., Islam, M. S., & Skitmore, M. (2022). Budget and cost contingency CART models for power plant projects. Journal of Civil Engineering and Management, 28(8), 680–695.
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Oct 27, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.


Ajay, S., & Micah, B. (2014). Sampling techniques & determination of sample size in applied statistics research: An overview. International Journal of Economics, Commerce and Management, 2(11), 1–22.

Amadi, A. I. (2021). Towards methodological adventure in cost overrun research: linking process and product. International Journal of Construction Management.

Aragonés-Beltrán, P., Chaparro-Gonzalez, F., Pastor-Ferrando, J.-P., & Pla-Rubio, A. (2014). An AHP/ANP-based multi-criteria decision approach for the selection of solar thermal power plant investment projects. Energy, 66, 222–238.

Awojobi, O., & Jenkins, G. P. (2016). Managing the cost overrun risks of hydroelectric dams: An application of reference class forecasting techniques. Renewable and Sustainable Energy Reviews, 63, 19–32.

Ayub, B., Thaheem, M. J., & Ullah, F. (2019). Contingency release during project execution: The contractor’s decision-making dilemma. Project Management Journal, 50(6), 734–748.

Barraza, G. A., Asce, M., & Bueno, R. A. (2007). Cost contingency management. Journal of Management in Engineering, 23(3), 140–146.

Bangladesh Bureau of Statistics. (2020).

Bhargava, A., Labi, S., Chen, S., Saeed, T. U., & Sinha, K. C. (2017). Predicting cost escalation pathways and deviation severities of infrastructure projects using risk-based econometric models and Monte Carlo simulation. Computer-Aided Civil and Infrastructure Engineering, 32(8), 620–640.

Bilal, M., & Oyedele, L. O. (2020). Guidelines for applied machine learning in construction industry – A case of profit margins estimation. Advanced Engineering Informatics, 43, 101013.

Bordat, C., McCullouch, B. G., Labi, S., & Sinha, K. (2004). An analysis of cost overruns and time delays of INDOT projects (Publication FHWA/IN/JTRP-2004/07). Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette, Indiana.

Chakraborty, D., Elhegazy, H., Elzarka, H., & Gutierrez, L. (2020). A novel construction cost prediction model using hybrid natural and light gradient boosting. Advanced Engineering Informatics, 46, 101201.

Chang, C. Y., & Ko, J. W. (2017). New approach to estimating the standard deviations of lognormal cost variables in the Monte Carlo analysis of construction risks. Journal of Construction Engineering and Management, 143(1), 06016006.

Curram, S. P., & Mingers, H. (2017). Neural networks, decision tree induction and discriminant analysis: an empirical comparision. Journal of the Operational Research Society, 45(4), 440–450.

Diab, M. F., Varma, A., & Panthi, K. (2017). Modeling the construction risk ratings to estimate the contingency in highway projects. Journal of Construction Engineering and Management, 143(8), 04017041.

Dursun, O., & Stoy, C. (2016). Conceptual estimation of construction costs using the multistep ahead approach. Journal Construction Engineering and Management, 142(9), 04016038.

Elfahham, Y. (2019). Estimation and prediction of construction cost index using neural networks, time series, and regression. Alexandria Engineering Journal, 58(2), 499–506.

Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813.

Elmousalami, H. H. (2020a). Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review. Journal of Construction Engineering and Management, 146(1), 03119008.

Elmousalami, H. H. (2020b). Comparison of artificial intelligence techniques for project conceptual cost prediction: A case study and comparative analysis. IEEE Transactions on Engineering Management, 68(1), 183–196.

Eybpoosh, M., Dikmen, I., & Birgonul, M. T. (2011). Identification of risk paths in international construction projects using structural equation modeling. Journal of Construction Engineering and Management, 137(12), 1164–1175.

Gazder, U., Islam, M. S., & Arifuzzaman, M. (2020). Parametric modeling of the cost of power plant projects. In 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT 2020), Sakheer, Bahrain.

Gilbert, A., Sovacool, B. K., Johnstone, P., & Stirling, A. (2017). Cost overruns and financial risk in the construction of nuclear power reactors: A critical appraisal. Energy Policy, 102, 644–649.

Gong, H., Sun, Y., Shu, X., & Huang, B. (2018). Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890–897.

Günaydin, H. M., & Doǧan, 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.

Gunduz, M., & Sahin, H. B. (2015). An early cost estimation model for hydroelectric power plant projects using neural networks and multiple regression analysis. Journal of Civil Engineering and Management, 21(4), 470–477.

Hammad, M. W., Abbasi, A., & Ryan, M. J. (2016). Allocation and management of cost contingency in projects. Journal of Management in Engineering, 32(6), 04016014.

Haque, M. A. (2020). Bangladesh power sector: An appraisal from a multi-dimensional perspective (Issue 03 September). 6F7C338035.ashx

Hashemi, S. T., Ebadati, O. M., & Kaur, H. (2019). A hybrid conceptual cost estimating model using ANN and GA for power plant projects. Neural Computing and Applications, 31(7), 2143–2154.

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.

Hoseini, E., Bosch-Rekveldt, M., & Hertogh, M. (2020). Cost contingency and cost evolvement of construction projects in the preconstruction phase. Journal of Construction Engineering and Management, 146(6), 05020006.

Idrus, A., Nuruddin, M. F., & Rohman, M. A. (2011). Development of project cost contingency estimation model using risk analysis and fuzzy expert system. Expert Systems with Applications, 38(3), 1501–1508.

International Monetary Fund. (2020). Bangladesh’s GDP and inflation rate.

Islam, M. S., & Nepal, M. (2016). A Fuzzy-Bayesian Model for risk assessment in power plant projects. Procedia Computer Science, 100, 963–970.

Islam, M. S., Nepal, M. P., & Skitmore, M. (2018). Modified Fuzzy Group Decision Making Approach to the cost overrun risk assessment of power plant projects. Journal of Construction Engineering and Management, 145(2), 04018126-1–04018126-15.

Islam, M. S., Nepal, M. P., Skitmore, M., & Kabir, G. (2019). A knowledge-based expert system to assess power plant project cost overrun risks. Expert Systems with Applications, 136, 12–32.

Islam, R., Ahmad Bashawir, A. G., Mahyudin, E., & Manickam, N. (2017). Determinants of factors that affecting inflation in Malaysia. International Journal of Economics and Financial Issues, 7(2), 355–364.

Islam, M. S., Nepal, M. P., Skitmore, M., & Drogemuller, R. (2021). Risk induced contingency cost modeling for power plant projects. Automation in Construction, 123, 103519.

Jung, J. H., Kim, D. Y., & Lee, H. K. (2016). The computer-based contingency estimation through analysis cost overrun risk of public construction project. KSCE Journal of Civil Engineering, 20(4), 1119–1130.

Lam, T. Y. M., & Siwingwa, N. (2017). Risk management and contingency sum of construction projects. Journal of Financial Management of Property and Construction, 22(3), 237–251.

Lee, K. P., Lee, H. S., Park, M., Kim, D. Y., & Jung, M. (2017). Management-reserve estimation for international construction projects based on risk-informed k-NN. Journal of Management in Engineering, 33(4), 04017002.

Lhee, S. C., Issa, R. R. A., & Flood, I. (2012). Prediction of financial contingency for asphalt resurfacing projects using artificial neural networks. Journal of Construction Engineering and Management, 138(1), 22–30.

Li, Y., & Wang, X. (2018). Risk assessment for public–private partnership projects: using a fuzzy analytic hierarchical process method and expert opinion in China. Journal of Risk Research, 21(8), 952–973.

Loh, W. Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329–348.

Maas, C. J., & Hox, J. J. (2005). Sufficient sample sizes for multilevel modeling. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 1(3), 86–92.

Maronati, G., & Petrovic, B. (2019). Estimating cost uncertainties in nuclear power plant construction through Monte Carlo sampled correlated random variables. Progress in Nuclear Energy, 111, 211–222.

Mawlana, M., & Hammad, A. (2015). Joint probability for evaluating the schedule and cost of stochastic simulation models. Advanced Engineering Informatics, 29(3), 380–395.

Moisen, G. G. (2008). Classification and regression trees. In S. E. Jør­gensen, & B. D. Fath (Eds.), Encyclopedia of ecology (Vol. 1, pp. 582–588). Elsevier.

Musarat, M. A., Alaloul, W. S., & Liew, M. S. (2021). Impact of inflation rate on construction projects budget: A review. Ain Shams Engineering Journal, 12(1), 407–414.

Olaniran, O. J. (2015). The effects of cost-based contractor selection on construction project performance. Journal of Financial Management of Property and Construction, 20(3), 235–251.

Perner, P., Zscherpel, U., & Jacobsen, C. (2001). A comparison between neural networks and decision trees based on data from industrial radiographic testing. Pattern Recognition Letters, 22(1), 47–54.

Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181–199.

Razi, M. A., & Athappilly, K. (2005). A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Systems with Applications, 29(1), 65–74.

Salah, A. (2015). Fuzzy set-based risk management for construction projects. Concordia University.

Salah, A., & Moselhi, O. (2015). Contingency modelling for construction projects using fuzzy-set theory. Engineering, Construction and Architectural Management, 22(2), 214–241.

Shaaban, K., & Pande, A. (2016). Classification tree analysis of factors affecting parking choices in Qatar. Case Studies on Transport Policy, 4(2), 88–95.

Shahtaheri, M., Haas, C. T., & Salimi, T. (2016). A stochastic simulation approach for the integration of risk and uncertainty into megaproject cost and schedule estimates. Construction Research Congress, 4, 1669–1679.

Shahtaheri, M., Haas, C. T., & Rashedi, R. (2017). Applying very large scale integration reliability theory for understanding the impacts of type II risks on megaprojects. Journal of Management in Engineering, 33(4), 04017003.

Singh, A. S., & Masuku, M. B. (2013). Fundamentals of applied research and sampling techniques. International Journal of Medical and Applied Sciences, 2(4), 124–132.

Sonmez, R., Ergin, A., & Birgonul, M. T. (2007). Quantitative methodology for determination of cost contingency in international projects. Journal of Management in Engineering, 23(1), 35–39.

Sovacool, B. K., Gilbert, A., & Nugent, D. (2014). An international comparative assessment of construction cost overruns for electricity infrastructure. Energy Research & Social Science, 3, 152–160.

Steinberg, D. (2009). CART: Classification and regression trees. In The top ten algorithms in data mining (pp. 193–216). Chapman and Hall/CRC.

Strobl, C., Malley, J., & Tutz, G. (2009). Characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348.

Thal, A. E., Cook, J. J., & Iii, E. D. W. (2010). Estimation of cost contingency for air force construction projects. Journal of Construction Engineering and Management, 136(11), 1181–1188.

Timofeev, R. (2004). Classification and regression trees (CART) theory and applications [Master thesis]. Center of Applied Statistics and Economics, Humboldt University, Berlin.

Touran, A. (2003). Probabilistic model for cost contingency. Journal of Construction Engineering and Management, 129(3), 280–284.

Uzzafer, M. (2013). A contingency estimation model for software projects. International Journal of Project Management, 31(7), 981–993.

Williams, T. P., & Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43, 23–29.

Xia, N., Wang, X., Wang, Y., Yang, Q., & Liu, X. (2017). Lifecycle cost risk analysis for infrastructure projects with modified Bayesian networks. Journal of Engineering, Design and Technology, 15(1), 79–103.

Zhao, Y., Xiang, J., Xu, J., Li, J., & Zhang, N. (2019). Study on the comprehensive benefit evaluation of transnational power networking projects based on multi-project stakeholder perspectives. Energies, 12(2), 249.