A coupled genetic programming Monte Carlo simulation–based model for cost overrun prediction of thermal power plant projects

Muhammad Saiful Islam*, Saeed Reza Mohandes, Amir Mahdiyar, Alireza Fallahpour, Ayokunle Olubunmi Olanipekun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Number of pages14
JournalJournal of Construction Engineering and Management
Issue number8
Early online date9 Jun 2022
Publication statusPublished - 1 Aug 2022


  • Cost overruns
  • Machine Learning
  • Infrastructure
  • Thermal power plant
  • Monte Carlo simulation (MCS)

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