Budget and cost contingency CART models for power plant projects
Abstract
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. https://doi.org/10.3846/jcem.2022.16944
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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. https://doi.org/10.1080/15623599.2021.1894632
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. https://doi.org/10.1530/EJE-14-0355
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. https://doi.org/10.1016/j.rser.2016.05.006
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. https://doi.org/10.1177/8756972819848250
Barraza, G. A., Asce, M., & Bueno, R. A. (2007). Cost contingency management. Journal of Management in Engineering, 23(3), 140–146. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:3(140)
Bangladesh Bureau of Statistics. (2020). http://www.bbs.gov.bd/site/page/dc2bc6ce-7080-48b3-9a04-73cec782d0df/-.bbs.gov.bd
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. https://doi.org/10.1111/mice.12279
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. https://doi.org/10.1016/j.aei.2019.101013
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. https://doi.org/10.5703/1288284313134
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. https://doi.org/10.1016/j.aei.2020.101201
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001207
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. https://doi.org/10.1057/jors.1987.44
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001334
Dursun, O., & Stoy, C. (2016). Conceptual estimation of construction costs using the multistep ahead approach. Journal Construction Engineering and Management, 142(9), 04016038. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001150
Elfahham, Y. (2019). Estimation and prediction of construction cost index using neural networks, time series, and regression. Alexandria Engineering Journal, 58(2), 499–506. https://doi.org/10.1016/j.aej.2019.05.002
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678
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. https://doi.org/10.1109/TEM.2020.2972078
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000382
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. https://doi.org/10.1109/3ICT51146.2020.9311963
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. https://doi.org/10.1016/j.enpol.2016.04.001
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. https://doi.org/10.1016/j.conbuildmat.2018.09.017
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. https://doi.org/10.1016/j.ijproman.2004.04.002
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. https://doi.org/10.3846/13923730.2014.890657
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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000447
Haque, M. A. (2020). Bangladesh power sector: An appraisal from a multi-dimensional perspective (Issue 03 September). https://www.arx.cfa/~/media/AD0129173C34401196A0DA 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. https://doi.org/10.1007/s00521-017-3175-5
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)
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001842
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. https://doi.org/10.1016/j.eswa.2010.07.061
International Monetary Fund. (2020). Bangladesh’s GDP and inflation rate. https://www.imf.org/en/Countries/BGD
Islam, M. S., & Nepal, M. (2016). A Fuzzy-Bayesian Model for risk assessment in power plant projects. Procedia Computer Science, 100, 963–970. https://doi.org/10.1016/j.procs.2016.09.259
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001593
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. https://doi.org/10.1016/j.eswa.2019.06.030
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. https://doi.org/10.1016/j.autcon.2020.103519
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. https://doi.org/10.1007/s12205-015-0184-8
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. https://doi.org/10.1108/JFMPC-10-2016-0047
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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000510
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000408
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. https://doi.org/10.1080/13669877.2016.1264451
Loh, W. Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329–348. https://doi.org/10.1111/insr.12016
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. https://doi.org/10.1027/1614-2241.1.3.86
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. https://doi.org/10.1016/j.pnucene.2018.11.011
Mawlana, M., & Hammad, A. (2015). Joint probability for evaluating the schedule and cost of stochastic simulation models. Advanced Engineering Informatics, 29(3), 380–395. https://doi.org/10.1016/j.aei.2015.01.005
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. https://doi.org/10.1016/j.asej.2020.04.009
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. https://doi.org/10.1108/JFMPC-06-2014-0008
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. https://doi.org/10.1016/S0167-8655(00)00098-2
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. https://doi.org/10.1007/s10021-005-0054-1
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. https://doi.org/10.1016/j.eswa.2005.01.006
Salah, A. (2015). Fuzzy set-based risk management for construction projects. Concordia University. https://spectrum.library.concordia.ca/980339/
Salah, A., & Moselhi, O. (2015). Contingency modelling for construction projects using fuzzy-set theory. Engineering, Construction and Architectural Management, 22(2), 214–241. https://doi.org/10.1108/ECAM-03-2014-0039
Shaaban, K., & Pande, A. (2016). Classification tree analysis of factors affecting parking choices in Qatar. Case Studies on Transport Policy, 4(2), 88–95. https://doi.org/10.1016/j.cstp.2015.11.002
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. https://doi.org/10.1061/9780784479827.062
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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000504
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. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:1(35)
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. https://doi.org/10.1016/j.erss.2014.07.016
Steinberg, D. (2009). CART: Classification and regression trees. In The top ten algorithms in data mining (pp. 193–216). Chapman and Hall/CRC. https://doi.org/10.1201/9781420089653.ch10
Strobl, C., Malley, J., & Tutz, G. (2009). Characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0016973
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000227
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. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:3(280)
Uzzafer, M. (2013). A contingency estimation model for software projects. International Journal of Project Management, 31(7), 981–993. https://doi.org/10.1016/j.ijproman.2012.12.002
Williams, T. P., & Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43, 23–29. https://doi.org/10.1016/j.autcon.2014.02.014
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. https://doi.org/10.1108/JEDT-05-2015-0033
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. https://doi.org/10.3390/en12020249
Amadi, A. I. (2021). Towards methodological adventure in cost overrun research: linking process and product. International Journal of Construction Management. https://doi.org/10.1080/15623599.2021.1894632
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. https://doi.org/10.1530/EJE-14-0355
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. https://doi.org/10.1016/j.rser.2016.05.006
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. https://doi.org/10.1177/8756972819848250
Barraza, G. A., Asce, M., & Bueno, R. A. (2007). Cost contingency management. Journal of Management in Engineering, 23(3), 140–146. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:3(140)
Bangladesh Bureau of Statistics. (2020). http://www.bbs.gov.bd/site/page/dc2bc6ce-7080-48b3-9a04-73cec782d0df/-.bbs.gov.bd
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. https://doi.org/10.1111/mice.12279
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. https://doi.org/10.1016/j.aei.2019.101013
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. https://doi.org/10.5703/1288284313134
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. https://doi.org/10.1016/j.aei.2020.101201
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001207
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. https://doi.org/10.1057/jors.1987.44
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001334
Dursun, O., & Stoy, C. (2016). Conceptual estimation of construction costs using the multistep ahead approach. Journal Construction Engineering and Management, 142(9), 04016038. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001150
Elfahham, Y. (2019). Estimation and prediction of construction cost index using neural networks, time series, and regression. Alexandria Engineering Journal, 58(2), 499–506. https://doi.org/10.1016/j.aej.2019.05.002
Elith, J., Leathwick, J. R., & Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology, 77(4), 802–813. https://doi.org/10.1111/j.1365-2656.2008.01390.x
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001678
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. https://doi.org/10.1109/TEM.2020.2972078
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000382
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. https://doi.org/10.1109/3ICT51146.2020.9311963
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. https://doi.org/10.1016/j.enpol.2016.04.001
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. https://doi.org/10.1016/j.conbuildmat.2018.09.017
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. https://doi.org/10.1016/j.ijproman.2004.04.002
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. https://doi.org/10.3846/13923730.2014.890657
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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000447
Haque, M. A. (2020). Bangladesh power sector: An appraisal from a multi-dimensional perspective (Issue 03 September). https://www.arx.cfa/~/media/AD0129173C34401196A0DA 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. https://doi.org/10.1007/s00521-017-3175-5
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)
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001842
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. https://doi.org/10.1016/j.eswa.2010.07.061
International Monetary Fund. (2020). Bangladesh’s GDP and inflation rate. https://www.imf.org/en/Countries/BGD
Islam, M. S., & Nepal, M. (2016). A Fuzzy-Bayesian Model for risk assessment in power plant projects. Procedia Computer Science, 100, 963–970. https://doi.org/10.1016/j.procs.2016.09.259
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001593
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. https://doi.org/10.1016/j.eswa.2019.06.030
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. https://doi.org/10.1016/j.autcon.2020.103519
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. https://doi.org/10.1007/s12205-015-0184-8
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. https://doi.org/10.1108/JFMPC-10-2016-0047
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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000510
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000408
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. https://doi.org/10.1080/13669877.2016.1264451
Loh, W. Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329–348. https://doi.org/10.1111/insr.12016
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. https://doi.org/10.1027/1614-2241.1.3.86
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. https://doi.org/10.1016/j.pnucene.2018.11.011
Mawlana, M., & Hammad, A. (2015). Joint probability for evaluating the schedule and cost of stochastic simulation models. Advanced Engineering Informatics, 29(3), 380–395. https://doi.org/10.1016/j.aei.2015.01.005
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. https://doi.org/10.1016/j.asej.2020.04.009
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. https://doi.org/10.1108/JFMPC-06-2014-0008
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. https://doi.org/10.1016/S0167-8655(00)00098-2
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. https://doi.org/10.1007/s10021-005-0054-1
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. https://doi.org/10.1016/j.eswa.2005.01.006
Salah, A. (2015). Fuzzy set-based risk management for construction projects. Concordia University. https://spectrum.library.concordia.ca/980339/
Salah, A., & Moselhi, O. (2015). Contingency modelling for construction projects using fuzzy-set theory. Engineering, Construction and Architectural Management, 22(2), 214–241. https://doi.org/10.1108/ECAM-03-2014-0039
Shaaban, K., & Pande, A. (2016). Classification tree analysis of factors affecting parking choices in Qatar. Case Studies on Transport Policy, 4(2), 88–95. https://doi.org/10.1016/j.cstp.2015.11.002
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. https://doi.org/10.1061/9780784479827.062
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. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000504
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. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:1(35)
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. https://doi.org/10.1016/j.erss.2014.07.016
Steinberg, D. (2009). CART: Classification and regression trees. In The top ten algorithms in data mining (pp. 193–216). Chapman and Hall/CRC. https://doi.org/10.1201/9781420089653.ch10
Strobl, C., Malley, J., & Tutz, G. (2009). Characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0016973
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. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000227
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. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:3(280)
Uzzafer, M. (2013). A contingency estimation model for software projects. International Journal of Project Management, 31(7), 981–993. https://doi.org/10.1016/j.ijproman.2012.12.002
Williams, T. P., & Gong, J. (2014). Predicting construction cost overruns using text mining, numerical data and ensemble classifiers. Automation in Construction, 43, 23–29. https://doi.org/10.1016/j.autcon.2014.02.014
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. https://doi.org/10.1108/JEDT-05-2015-0033
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. https://doi.org/10.3390/en12020249