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Aramali, V, Gibson, G E, El Asmar, M and Sanboskani, H (2023) Novel earned value management system maturity framework and its relation to project performance. Journal of Construction Engineering and Management, 149(06).
Arora, J, Kar, S and Jha, K N (2023) Assessment of the significance of identified attributes affecting the rebar-fixing productivity using multiple regression. Journal of Construction Engineering and Management, 149(06).
Asadi, R, Rotimi, J O B and Wilkinson, S (2023) Analyzing underlying factors of rework in generating contractual claims in construction projects. Journal of Construction Engineering and Management, 149(06).
Hsu, C L, Wang, J T and Huang, Y H (2023) A functional element-naming approach for BIM elements in building envelope systems. Journal of Construction Engineering and Management, 149(06).
Karamoozian, A, Wu, D and Luo, C (2023) Green supplier selection in the construction industry using a novel fuzzy decision-making approach. Journal of Construction Engineering and Management, 149(06).
Long, W, Wang, X, Liang, Y and Ye, K (2023) Characteristics of collusive practices in bidding: Mixed methods study in China. Journal of Construction Engineering and Management, 149(06).
Nguyen, T Q, Yeoh, J K W and Angelia, N (2023) Predicting percent plan complete through time series analysis. Journal of Construction Engineering and Management, 149(06).
Rathnayake, A and Middleton, C (2023) Systematic review of the literature on construction productivity. Journal of Construction Engineering and Management, 149(06).
Tao, Y, Hu, H, Xu, F and Zhang, Z (2023) Ergonomic risk assessment of construction workers and projects based on fuzzy Bayesian network and D-S evidence theory. Journal of Construction Engineering and Management, 149(06).
Tarekegn Gurmu, A (2023) Preconstruction phase management practices enhancing labor productivity in multistory building projects. Journal of Construction Engineering and Management, 149(06).
Tatari, A (2023) Simulating cost risks for prefabricated construction in developing countries using Bayesian networks. Journal of Construction Engineering and Management, 149(06).
- Type: Journal Article
- Keywords: artificial neural networks; Bayesian networks; conditional gan; construction projects; data analysis; offsite construction; prefabricated construction; risk assessment; risk identification
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13259
- 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.
Wang, Y, Chen, L and Zhuang, J (2023) Pricing of credit default swaps from the perspective of credit enhancement in PPP projects. Journal of Construction Engineering and Management, 149(06).
Zhang, W, Zhang, H and Yu, L (2023) Collaborative planning for stacking and installation of prefabricated building components regarding crane-collision avoidance. Journal of Construction Engineering and Management, 149(06).