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

Islam, M S; Mohandes, S R; Mahdiyar, A; Fallahpour, A and Olanipekun, A O (2022) A coupled genetic programming Monte Carlo simulation-based model for cost overrun prediction of thermal power plant projects. Journal of Construction Engineering and Management, 148(8): 04022073, ISSN 0733-9364

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.

Item Type: Article
Uncontrolled Keywords: cost overruns; infrastructure; machine learning; Monte Carlo simulation; thermal power plant
Index terms: Monte Carlo simulation, project delivery, cost planning, cost overrun, body of knowledge, thermal power plant, Bangladesh, power plant, project manager, life cycle, programming, accuracy, machine learning
Subjects: Geography, financial and cost management, professional development, energy systems, infrastructure and transport systems, programming, financial management, artificial intelligence, profession, value management, knowledge management, modelling and simulation, project delivery
Topics: Information Management, Engineering Principles, Sustainability, Procurement, Geographical Context, Digital Applications, Research Practice, Cost Management, Project Management, Roles and Professions
Descriptive scope: 3 PCA

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