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.
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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.
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Journal of Construction Engineering and Management
Volume 148 • Issue 8 • August 2022
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© 2022 American Society of Civil Engineers.
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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
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