Simulating Cost Risks for Prefabricated Construction in Developing Countries Using Bayesian Networks
Publication: Journal of Construction Engineering and Management
Volume 149, Issue 6
Abstract
Prefabricated construction has been widely recognized as a primary method for sustainable construction, improving workflow continuity, increasing resource consumption efficiencies, and minimizing construction waste. While the benefits have been explored, its adoption constraints remain unexplored. Very few studies investigated the complex interrelationships between cost risk factors. The literature lacks a methodology that does not fall short of considering these interrelationships. Due to various interactive constraints, the development of prefabricated construction remains in its infancy. Therefore, the study aims to conduct cost risk identification and assessment methodology to investigate the interrelationships among barriers. In achieving this endeavor, a literature review was conducted first, followed by questionnaire surveys. Lastly, a risk assessment tool was developed by leveraging simulation models of Bayesian networks. The results showed that machinery and technology, direct costs, scheduling and planning, safety hazards, and site layout are the top five significant factors in implementing prefabricated construction with an assessment of 0.817, 0.777, 0.711, 0.687, and 0.666, respectively. The attention is increasing on the economic, social, and environmental dimensions of sustainability. The Bayesian model shows that the three most influencing risk factors are a lack of machinery and technology, inadequate experience, and modifications to existing policies that directly influence at least three factors. The three most influenced risk factors are poor understanding, safety hazards, and scheduling and planning which are directly influenced by at least three factors. The study proposed the following mitigation strategies: (1) apply control measures for critical cost risks, (2) control influencing risks, and lastly (3) monitor highly influenced risks. The uniqueness of the study methodology is that it adopts a simulation model that not only simulates the interrelations of the factors but also updates the model when new evidence is revealed. Hence, when conducting a risk control and risk reassessment, the model will provide a prescriptive analysis to proactively control risks. The research builds upon existing knowledge and bridges a gap by investigating cost risks and their interrelationships. Study findings are informative to policymakers, providing a better grasp on prefabricated construction.
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Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The authors wish to give their gratitude to the industry professionals who have participated in face-to-face interviews to validate the reduced 25 risk factor list. The authors also express their thanks to the 149 industry experts who participated in the questionnaire survey and to those who distributed them. Finally, the authors declare no unreported conflict of interest associated with this publication. The authors report financial support and travel were provided by SELINA INSAAT VE GENEL TICARET LIMITED SIRKETI.
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Information & Authors
Information
Published In
Journal of Construction Engineering and Management
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Oct 18, 2022
Accepted: Feb 8, 2023
Published online: Apr 7, 2023
Published in print: Jun 1, 2023
Discussion open until: Sep 7, 2023
ASCE Technical Topics:
- Analysis (by type)
- Bayesian analysis
- Benefit cost ratios
- Business management
- Construction engineering
- Construction industry
- Construction management
- Construction methods
- Construction wastes
- Disaster risk management
- Engineering fundamentals
- Environmental engineering
- Financial management
- Models (by type)
- Offsite construction
- Pollutants
- Practice and Profession
- Risk management
- Simulation models
- Solid wastes
- Statistical analysis (by type)
- Sustainable development
- Wastes
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Cited by
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