A Coupled Genetic Programming Monte Carlo Simulation–Based Model for Cost Overrun Prediction of Thermal Power Plant Projects
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 8
Abstract
Globally, power projects are prone to cost overrun projects. Within the body of knowledge, previous studies have paid less attention to predicting the cost overruns to assist contingency cost planning. Particularly, in thermal power plant projects (TPPPs), the enormous risks involved in their delivery undermine the accuracy of cost overrun prediction. To prevent cost overrun in thermal power plant projects, these risks need to be accounted for by employing sophisticated cost overrun prediction techniques. This study aims to develop a hybrid predictive-probabilistic-based model (HPPM) that integrates a genetic programming technique with Monte Carlo simulation (MCS). The HPPM was proposed based on the data collected from TPPPs in Bangladesh. Also, the sensitivity of the HPPM was examined to identify the critical risks in cost overruns simulation. The simulation outcomes show that 40.48% of a project’s initial estimated budget was the most probable to cost overrun, while the maximum cost overrun will not exceed 75% with 90% confidence. Practically, the analysis will sensitize project managers to emphasize thermal plants’ budget accuracy not only at the initial project delivery phase but throughout the project life cycle. Theoretically, the HPPM could be employed for cost overrun prediction in other types of power plant projects.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
References
Afzal, F., S. Yunfei, D. Junaid, and M. S. Hanif. 2020. “Cost-risk contingency framework for managing cost overrun in metropolitan projects: Using fuzzy-AHP and simulation.” Int. J. Managing Projects Bus. 13 (5): 1121–1139. https://doi.org/10.1108/IJMPB-07-2019-0175.
Ahvanooey, M. T., Q. Li, M. Wu, and S. Wang. 2019. “A survey of genetic programming and its applications.” KSII Trans. Internet Inf. Syst. 13 (4): 1765–1794. https://doi.org/10.3837/tiis.2019.04.002.
Alavi, A. H., and A. H. Gandomi. 2011. “A robust data mining approach for formulation of geotechnical engineering systems.” Eng. Comput. 28 (3): 242–274. https://doi.org/10.1108/02644401111118132.
Alsharif, S., and A. Karatas. 2016. “A framework for identifying causal factors of delay in nuclear power plant projects.” Procedia Eng. 145 (248): 1486–1492. https://doi.org/10.1016/j.proeng.2016.04.187.
Ansar, A., B. Flyvbjerg, A. Budzier, and D. Lunn. 2014. “Should we build more large dams? The actual costs of hydropower megaproject development.” Energy Policy 69 (Jun): 43–56. https://doi.org/10.1016/j.enpol.2013.10.069.
Arnold, U., and Ö. Yildiz. 2015. “Economic risk analysis of decentralized renewable energy infrastructures: A Monte Carlo simulation approach.” Renewable Energy 77 (May): 227–239. https://doi.org/10.1016/j.renene.2014.11.059.
Asmar, M. E., A. S. Hanna, and G. C. Whited. 2011. “New approach to developing conceptual cost estimates for highway projects.” J. Constr. Eng. Manage. 137 (11): 942–949. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000355.
Attalla, M., and T. Hegazy. 2003. “Predicting cost deviation in reconstruction projects: Artificial neural networks versus regression.” J. Constr. Eng. Manage. 129 (4): 405–411. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(405).
Ayub, B., M. J. Thaheem, and F. Ullah. 2019. “Contingency release during project execution: The contractor’s decision-making dilemma.” Project Manage. J. 50 (6): 734–748. https://doi.org/10.1177/8756972819848250.
Bangladesh Country Commercial Guide. 2017. “Bangladesh: Power & energy.” Accessed October 5, 2017. https://www.export.gov/article ?id=Bangladesh-Power-and-energy.
Barraza, G. A., and R. A. Bueno. 2007. “Cost contingency management.” J. Manage. Eng. 23 (3): 140–146. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:3(140).
Bouayed, Z. 2016. “Using Monte Carlo simulation to mitigate the risk of project cost overruns.” Int. J. Saf. Security Eng. 6 (2): 293–300. https://doi.org/10.2495/SAFE-V6-N2-293-300.
BPDB (Bangladesh Power Development Board). 2017. “Annual report (2015–16).” Accessed October 5, 2017. http://www.bpdb.gov.bd /download/annual_report/Annual_Report_2015_16.pdf.
Car-Pusic, D., S. Petruseva, V. Zileska Pancovska, and Z. Zafirovski. 2020. “Neural network-based model for predicting preliminary construction cost as part of cost predicting system.” Adv. Civ. Eng. 2020 (Dec): 1–13. https://doi.org/10.1155/2020/8886170.
Castelli, M., L. Trujillo, and L. Vanneschi. 2015. “Energy consumption forecasting using semantic-based genetic programming with local search optimizer.” In Computational intelligence and neuroscience, 2015. London: Hindawi.
Chan, K. Y., C. K. Kwong, and H. Jiang. 2021. “Analyzing imbalanced online consumer review data in product design using geometric semantic genetic programming.” Eng. Appl. Artif. Intell. 105 (Oct): 104442. https://doi.org/10.1016/j.engappai.2021.104442.
Chang, C.-Y., and J.-W. Ko. 2017. “New approach to estimating the standard deviations of lognormal cost variables in the Monte Carlo analysis of construction risks.” J. Constr. Eng. Manage. 143 (1): 06016006. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001207.
Chavoya, A., C. Lopez-Martin, I. R. Andalon-Garcia, and M. E. Meda-Campaña. 2012. “Genetic programming as alternative for predicting development effort of individual software projects.” PLoS One 7 (11): e50531. https://doi.org/10.1371/journal.pone.0050531.
Dahiru, A. T., C. W. Tan, S. Salisu, and K. Y. Lau. 2021. “Multi-configurational sizing and analysis in a nanogrid using nested integer linear programming.” J. Cleaner Prod. 323 (Nov): 129159. https://doi.org/10.1016/j.jclepro.2021.129159.
De Marco, A., C. Rafele, and M. J. Thaheem. 2016. “Dynamic management of risk contingency in complex design-build projects.” J. Constr. Eng. Manage. 142 (2): 04015080. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001052.
Diab, M. F., A. Varma, and K. Panthi. 2017. “Modeling the construction risk ratings to estimate the contingency in highway projects.” J. Constr. Eng. Manage. 143 (8): 04017041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001334.
Elmousalami, H. H. 2020a. “Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review.” J. Constr. Eng. Manage. 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 Trans. Eng. Manage. 68 (1): 183–196. https://doi.org/10.1109/TEM.2020.2972078.
Emigdio, Z., M. Abatal, A. Bassam, L. Trujillo, P. Juárez-Smith, and Y. El Hamzaoui. 2017. “Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming.” J. Cleaner Prod. 161 (Sep): 860–870. https://doi.org/10.1016/j.jclepro.2017.05.192.
Fallahpour, A., K. Y. Wong, S. Rajoo, and G. Tian. 2021. “An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming.” J. Cleaner Prod. 283 (Feb): 125287. https://doi.org/10.1016/j.jclepro.2020.125287.
Flyvbjerg, B., et al. 2018. “Five things you should know about cost overrun.” Transp. Res. Part A Policy Pract. 118 (Dec): 174–190. https://doi.org/10.1016/j.tra.2018.07.013.
Flyvbjerg, B., M. S. Holm, and S. Buhl. 2002. “Underestimating costs in public works, error or lie?” Am. Plann. Assoc. J. 68 (3): 279–295. https://doi.org/10.1080/01944360208976273.
Gandomi, A. H., A. H. Alavi, and C. Ryan. 2015. Handbook of genetic programming applications. Cham, Switzerland: Springer.
Garg, A., and J. S. L. Lam. 2015. “Improving environmental sustainability by formulation of generalized power consumption models using an ensemble based multi-gene genetic programming approach.” J. Cleaner Prod. 102 (Sep): 246–263. https://doi.org/10.1016/j.jclepro.2015.04.068.
Gilbert, A., B. K. Sovacool, P. Johnstone, and A. Stirling. 2017. “Cost overruns and financial risk in the construction of nuclear power reactors: A critical appraisal.” Energy Policy 102 (Mar): 644–649. https://doi.org/10.1016/j.enpol.2016.04.001.
Gurgun, A. P., Y. Zhang, and A. Touran. 2013. “Schedule contingency analysis for transit projects using a simulation approach.” J. Civ. Eng. Manage. 19 (4): 465–475. https://doi.org/10.3846/13923730.2013.768542.
Hadikusumo, B. H. W., and S. Tobgay. 2015. “Construction claim types and causes for a large-scale hydropower project in Bhutan.” J. Constr. Dev. Countries 20 (1): 49–63.
Hashemi, S. T., O. M. Ebadati, and H. Kaur. 2019. “A hybrid conceptual cost estimating model using ANN and GA for power plant projects.” Neural Comput. Appl. 31 (7): 2143–2154. https://doi.org/10.1007/s00521-017-3175-5.
Hollmann, J. K. 2007. “The Monte-Carlo challenge: A better approach.” AACE Int. Trans. 3: 1–7.
Hossen, M. M., S. Kang, and J. Kim. 2015. “Construction schedule delay risk assessment by using combined AHP-RII methodology for an international NPP project.” Nuclear Eng. Technol. 47 (3): 362–379. https://doi.org/10.1016/j.net.2014.12.019.
Idrus, A., M. F. Nuruddin, and M. A. Rohman. 2011. “Development of project cost contingency estimation model using risk analysis and fuzzy expert system.” Expert Syst. Appl. 38 (3): 1501–1508. https://doi.org/10.1016/j.eswa.2010.07.061.
IHS. 2014. “Cost and technology indexes.” Accessed September 25, 2015. https://www.ihs.com/info/cera/ihsindexes/index.html.
Islam, M. S., M. Nepal, M. Skitmore, and G. Kabir. 2019. “A knowledge-based expert system to assess power plant project cost overrun risks.” Expert Syst. Appl. 136 (Dec): 12–32. https://doi.org/10.1016/j.eswa.2019.06.030.
Islam, M. S., M. P. Nepal, and M. Skitmore. 2018. “Modified fuzzy group decision-making approach to cost overrun risk assessment of power plant projects.” J. Constr. Eng. Manage. 145 (2): 04018126. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001593.
Islam, M. S., M. P. Nepal, M. Skitmore, and R. Drogemuller. 2021. “Risk induced contingency cost modeling for power plant projects.” Autom. Constr. 123 (Mar): 103519. https://doi.org/10.1016/j.autcon.2020.103519.
Islam, S., and M. Z. R. Khan. 2017. “A review of energy sector of Bangladesh.” Energy Procedia 110 (Mar): 611–618. https://doi.org/10.1016/j.egypro.2017.03.193.
Kim, M., I. Lee, and Y. Jung. 2017. “International project risk management for nuclear power plant (NPP) construction: Featuring comparative analysis with fossil and gas power plants.” Sustainability 9 (3): 469. https://doi.org/10.3390/su9030469.
Koza, J. R. 1994. Genetic programming II: Automatic discovery of reusable subprograms. Cambridge, MA: MIT Press.
Kucukali, S. 2016. “Risk scorecard concept in wind energy projects: An integrated approach.” Renewable Sustainable Energy Rev. 56 (Apr): 975–987. https://doi.org/10.1016/j.rser.2015.12.017.
Li, Y., and X. Wang. 2016. “Risk assessment for public–private partnership projects: Using a fuzzy analytic hierarchical process method and expert opinion in China.” J. Risk Res. 21 (8): 952–973. https://doi.org/10.1080/13669877.2016.1264451.
Lin, J., L. Zhu, and K. Gao. 2020. “A genetic programming hyper-heuristic approach for the multi-skill resource constrained project scheduling problem.” Expert Syst. Appl. 140 (Feb): 112915. https://doi.org/10.1016/j.eswa.2019.112915.
Love, P. E. D., D. D. Ahiaga-Dagbui, and Z. Irani. 2016. “Cost overruns in transportation infrastructure projects: Sowing the seeds for a probabilistic theory of causation.” Transp. Res. Part A Policy Pract. 92 (Oct): 184–194. https://doi.org/10.1016/j.tra.2016.08.007.
Love, P. E. D., C.-P. Sing, B. Carey, and J. T. Kim. 2015. “Estimating construction contingency: Accommodating the potential for cost overruns in road construction projects.” J. Infrastruct. Syst. 21 (2): 04014035. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000221.
Love, P. E. D., C.-P. Sing, X. Wang, Z. Irani, and D. W. Thwala. 2014. “Overruns in transportation infrastructure projects.” Struct. Infrastruct. Eng. 10 (2): 141–159. https://doi.org/10.1080/15732479.2012.715173.
Love, P. E. D., X. Wang, C. Sing, and R. L. K. Tiong. 2013. “Determining the probability of project cost overruns.” J. Constr. Eng. Manage. 139 (3): 321–330. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000575.
Mahdiyar, A., S. Tabatabaee, A. Abdullah, and A. Marto. 2018. “Identifying and assessing the critical criteria affecting decision-making for green roof type selection.” Sustainable Cities Soc. 39 (May): 772–783. https://doi.org/10.1016/j.scs.2018.03.007.
Mahdiyar, A., S. Tabatabaee, K. Yahya, and S. R. Mohandes. 2021. “A probabilistic financial feasibility study on green roof installation from the private and social perspectives.” Urban For. Urban Greening 58 (Mar): 126893. https://doi.org/10.1016/j.ufug.2020.126893.
Maronati, G., and B. Petrovic. 2019. “Estimating cost uncertainties in nuclear power plant construction through Monte Carlo sampled correlated random variables.” Prog. Nucl. Energy 111 (Mar): 211–222. https://doi.org/10.1016/j.pnucene.2018.11.011.
Matel, E., F. Vahdatikhaki, S. Hosseinyalamdary, T. Evers, and H. Voordijk. 2019. “An artificial neural network approach for cost estimation of engineering services.” Int. J. Constr. Manage. 22 (7): 1–14. https://doi.org/10.1080/15623599.2019.1692400.
Mohandes, S. R., and X. Zhang. 2021. “Developing a holistic occupational health and safety risk assessment model: An application to a case of sustainable construction project.” J. Cleaner Prod. 291 (Apr): 125934. https://doi.org/10.1016/j.jclepro.2021.125934.
Mostafavi, E. S., S. I. Mostafavi, A. Jaafari, and F. Hosseinpour. 2013. “A novel machine learning approach for estimation of electricity demand: An empirical evidence from Thailand.” Energy Convers. Manage. 74 (Oct): 548–555. https://doi.org/10.1016/j.enconman.2013.06.031.
Nevada DOT. 2012. Risk management and risk-based cost estimation guidelines. Carson, NV: Nevada DOT.
Olague, G., and L. Trujillo. 2011. “Evolutionary-computer-assisted design of image operators that detect interest points using genetic programming.” Image Vis. Comput. 29 (7): 484–498. https://doi.org/10.1016/j.imavis.2011.03.004.
Queensland Government. 2017. Project cost estimating manual. 7th ed. Brisbane, QLD, Australia: Queensland Government, Dept. of Transport and Main Road.
Quintero-Domínguez, L. A., C. Morell, and S. Ventura. 2021. “A propositionalization method of multi-relational data based on grammar-guided genetic programming.” Expert Syst. Appl. 168 (Apr): 114263. https://doi.org/10.1016/j.eswa.2020.114263.
Sadeghi, N., A. R. Fayek, and W. Pedrycz. 2010. “Fuzzy Monte Carlo simulation and risk assessment in construction.” Comput.-Aided Civ. Infrastruct. Eng. 25 (4): 238–252. https://doi.org/10.1111/j.1467-8667.2009.00632.x.
Sadrossadat, E., B. Ghorbani, R. Oskooei, and M. Kaboutari. 2018. “Use of adaptive neuro-fuzzy inference system and gene expression programming methods for estimation of the bearing capacity of rock foundations.” Eng. Comput. 35 (5): 2078–2106. https://doi.org/10.1108/EC-07-2017-0258.
Salah, A., and O. Moselhi. 2014. “Contingency modelling for construction projects using fuzzy-set theory.” Eng. Constr. Archit. Manage. 22 (2): 214–241. https://doi.org/10.1108/ECAM-03-2014-0039.
Salah, A., and O. Moselhi. 2015. “Contingency modelling for construction projects using fuzzy-set theory.” Eng. Constr. Archit. Manage. 22 (2): 214–241. https://doi.org/10.1108/ECAM-03-2014-0039.
Shahrara, N., T. Çelik, and A. H. Gandomi. 2017. “Gene expression programming approach to cost estimation formulation for utility projects.” J. Civ. Eng. Manage. 23 (1): 85–95. https://doi.org/10.3846/13923730.2016.1210214.
Shahtaheri, M., C. T. Haas, and R. Rashedi. 2017. “Applying very large scale integration reliability theory for understanding the impacts of Type II risks on megaprojects.” J. Manage. Eng. 33 (4): 04017003. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000504.
Shahtaheri, M., C. T. Haas, and T. Salimi. 2016. “A stochastic simulation approach for the integration of risk and uncertainty into megaproject cost and schedule estimates.” In Proc., Construction Research Congress, 1669–1679. Reston, VA: ASCE.
Singer, L. E., and D. Peterson. 2017. International energy outlook 2017. Washington, DC: US Energy Information Administration.
Sovacool, B. K., A. Gilbert, and D. Nugent. 2014a. “An international comparative assessment of construction cost overruns for electricity infrastructure.” Energy Res. Social Sci. 3 (Sep): 152–160. https://doi.org/10.1016/j.erss.2014.07.016.
Sovacool, B. K., A. Gilbert, and D. Nugent. 2014b. “Risk, innovation, electricity infrastructure and construction cost overruns: Testing six hypotheses.” Energy 74 (Sep): 906–917. https://doi.org/10.1016/j.energy.2014.07.070.
Sudirman, W. B., and S. Hardjomuljadi. 2011. “Project risk management in hydropower plant projects: A case study from the state-owned electricity company of Indonesia.” J. Infrastruct. Dev. 3 (2): 171–186. https://doi.org/10.1177/097493061100300205.
Tabatabaee, S., A. Mahdiyar, and S. Ismail. 2021. “Towards the success of building information modelling implementation: A fuzzy-based MCDM risk assessment tool.” J. Build. Eng. 43 (Nov): 103117. https://doi.org/10.1016/j.jobe.2021.103117.
Thal, A. E., Jr., J. J. Cook, and E. D. White III. 2010. “Estimation of cost contingency for Air Force construction projects.” J. Constr. Eng. Manage. 136 (11): 1181–1188. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000227.
Tiago Filho, G. L., I. F. S. dos Santos, and R. M. Barros. 2017. “Cost estimate of small hydroelectric power plants based on the aspect factor.” Renewable Sustainable Energy Rev. 77 (Sep): 229–238. https://doi.org/10.1016/j.rser.2017.03.134.
Tijanić, K., D. Car-Pušić, and M. Šperac. 2020. “Cost estimation in road construction using artificial neural network.” Neural Comput. Appl. 32 (13): 9343–9355. https://doi.org/10.1007/s00521-019-04443-y.
Touran, A., and R. Lopez. 2006. “Modeling cost escalation in large infrastructure projects.” J. Constr. Eng. Manage. 132 (8): 853–860. https://doi.org/10.1061/(ASCE)0733-9364(2006)132:8(853).
Traynor, B. A., and M. Mahmoodian. 2019. “Time and cost contingency management using Monte Carlo simulation.” Aust. J. Civ. Eng. 17 (1): 11–18. https://doi.org/10.1080/14488353.2019.1606499.
USDOT. 2015. “Oversight procedure 40c—Risk andcontingency review.” Accessed September 1, 2015. https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/regulations-and-guidance/58901/op40c-risk-and-contingency-review-sept-2015.pdf.
Wang, S. Q., and L. K. Tiong. 2000. “Case study of government initiatives for PRC’s BOT power plant project.” Int. J. Project Manage. 18 (1): 69–78. https://doi.org/10.1016/S0263-7863(98)00072-6.
Williams, T. P., and J. Gong. 2014. “Predicting construction cost overruns using text mining, numerical data and ensemble classifiers.” Autom. Constr. 43 (Jul): 23–29. https://doi.org/10.1016/j.autcon.2014.02.014.
World Energy Council. 2016. “World energy resources 2016.” Accessed September 1, 2021. https://www.worldenergy.org/assets/images/imported/2016/10/World-Energy-Resources-Full-report-2016.10.03.pdf.
Yamashita, G. H., F. S. Fogliatto, M. J. Anzanello, and G. L. Tortorella. 2022. “Customized prediction of attendance to soccer matches based on symbolic regression and genetic programming.” Expert Syst. Appl. 187 (Jan): 115912. https://doi.org/10.1016/j.eswa.2021.115912.
Yip, H. L., H. Fan, and Y. H. Chiang. 2014. “Predicting the maintenance cost of construction equipment: Comparison between general regression neural network and Box-Jenkins time series models.” Autom. Constr. 38 (Mar): 30–38. https://doi.org/10.1016/j.autcon.2013.10.024.
Yong, W., J. Zhou, D. Jahed Armaghani, M. M. Tahir, R. Tarinejad, B. T. Pham, and V. van Huynh. 2021. “A new hybrid simulated annealing-based genetic programming technique to predict the ultimate bearing capacity of piles.” Eng. Comput. 37 (3): 2111–2127. https://doi.org/10.1007/s00366-019-00932-9.
Yoo, W. S., J. Yang, S. Kang, and S. Lee. 2017. “Development of a computerized risk management system for international NPP EPC projects.” KSCE J. Civ. Eng. 21 (1): 11–26. https://doi.org/10.1007/s12205-016-0784-y.
Yun, L., W. Li, A. Garg, S. Maddila, L. Gao, Z. Fan, P. Buragohain, and C.-T. Wang. 2019. “Maximization of extraction of Cadmium and Zinc during recycling of spent battery mix: An application of combined genetic programming and simulated annealing approach.” J. Cleaner Prod. 218 (May): 130–140. https://doi.org/10.1016/j.jclepro.2018.11.226.
Zegordi, S. H., E. Rezaee Nik, and A. Nazari. 2012. “Power plant project risk assessment using a fuzzy-ANP and fuzzy-TOPSIS method.” Int. J. Eng. Trans. B 25 (2): 107–120. https://doi.org/10.5829/idosi.ije.2012.25.02b.04.
Z-Flores, E., M. Abatal, A. Bassam, L. Trujillo, P. Juárez-Smith, and Y. El Hamzaoui. 2017. “Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming.” J. Cleaner Prod. 161 (Sep): 860–870. https://doi.org/10.1016/j.jclepro.2017.05.192.
Information & Authors
Information
Published In
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 25, 2021
Accepted: Apr 6, 2022
Published online: Jun 9, 2022
Published in print: Aug 1, 2022
Discussion open until: Nov 9, 2022
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.
Cited by
- Aymen Nassar, Ahmed Elbisy, Evaluation of Factors Leading to Time Delays and Cost Overruns in Marine Construction Projects, Engineering, Technology & Applied Science Research, 10.48084/etasr.8116, 14, 5, (16095-16102), (2024).
- Hui Sun, Yihan Wang, Ruiyu Li, Huicang Wu, Yuru Wang, Research on the Dynamic Evolution of Social Risks in Community Renewal Projects: Considering the Coupling Effects of Factors, Journal of Management in Engineering, 10.1061/JMENEA.MEENG-6027, 40, 5, (2024).
- Mehdi Rajabi Asadabadi, Ofer Zwikael, Unrealistic Project Goals: Detection and Modification, Journal of Construction Engineering and Management, 10.1061/JCEMD4.COENG-13665, 150, 3, (2024).
- Yaman Saeid Al-Nahhas, Laith A. Hadidi, Muhammad Saiful Islam, Martin Skitmore, Ziyad Abunada, Modified Mamdani-fuzzy inference system for predicting the cost overrun of construction projects, Applied Soft Computing, 10.1016/j.asoc.2023.111152, 151, (111152), (2024).
- Diogo Freitas Rodrigues, Ana Paula Barbosa Sobral, Simulação de Monte Carlo aplicada à projetos de recertificação de 5 anos em equipamentos de superfície para perfuração de poços de petróleo offshore, Revista de Gestão e Projetos, 10.5585/gep.v14i1.23452, 14, 1, (96-132), (2023).
- Muhammad Saiful Islam, Madhav Nepal, Martin Skitmore, Structuring risks for a comprehensive understanding of cost overruns in power plant projects, Construction Innovation, 10.1108/CI-05-2022-0120, (2023).
- Ehsan Aghdam, Saeed Reza Mohandes, Patrick Manu, Clara Cheung, Akilu Yunusa-Kaltungo, Tarek Zayed, Predicting quality parameters of wastewater treatment plants using artificial intelligence techniques, Journal of Cleaner Production, 10.1016/j.jclepro.2023.137019, 405, (137019), (2023).
- Ali Ashrafian, Naser Safaeian Hamzehkolaei, Ngakan Ketut Acwin Dwijendra, Maziar Yazdani, An Evolutionary Neuro-Fuzzy-Based Approach to Estimate the Compressive Strength of Eco-Friendly Concrete Containing Recycled Construction Wastes, Buildings, 10.3390/buildings12081280, 12, 8, (1280), (2022).