IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i3p1864-d743344.html
   My bibliography  Save this article

Analysis on Dynamic Evolution of the Cost Risk of Prefabricated Building Based on DBN

Author

Listed:
  • Mengwei Ye

    (Division of Engineering Management, School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

  • Junwu Wang

    (Division of Engineering Management, School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

  • Xiang Si

    (China Construction Seventh Division South Company, Shenzhen 518000, China)

  • Shiman Zhao

    (Division of Engineering Management, School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

  • Qiyun Huang

    (Division of Engineering Management, School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Prefabricated building constitutes the development trend of the construction industry in the future. However, many uncertainties in the construction process will surely lead to a higher cost. Therefore, it is necessary to study the cost risk evolution and transfer mechanism in the implementation process of this project. A dynamic evolution model for the cost risk of prefabricated buildings has been established in this paper. First of all, a matrix for cost risk of prefabricated buildings was established based on the WSR (Wuli-Shili-Renli) model, and all risk factors in the implementation stage were classified in accordance with the WSR principle. Second, a DBN-based dynamic evolution model was established based on the risk matrix, and the structure and node parameters of the Dynamic Bayesian Network were determined with the aid of the K2 structure learning algorithm and parameter learning method. In view of the probability change process of risks over time, the dynamic evolution path of risks was predicted in different cases through causal reasoning and diagnostic reasoning. Eventually, the model was applied into construction projects. The research results show that: because prefabricated components need to be made by prefabricated component factories, the management systems of prefabricated component factories are usually not perfect, and the probability of management risks is higher. The occurrence of management risks not only has an impact on other risks at the current time node, but also causes other risks to occur in the subsequent transportation and construction phases at the next moment, which eventually leads to the occurrence of risk events.

Suggested Citation

  • Mengwei Ye & Junwu Wang & Xiang Si & Shiman Zhao & Qiyun Huang, 2022. "Analysis on Dynamic Evolution of the Cost Risk of Prefabricated Building Based on DBN," Sustainability, MDPI, vol. 14(3), pages 1-19, February.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1864-:d:743344
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/3/1864/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/3/1864/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cantarelli, C.C. & van Wee, B. & Molin, E.J.E. & Flyvbjerg, B., 2012. "Different cost performance: different determinants?," Transport Policy, Elsevier, vol. 22(C), pages 88-95.
    2. Xiaolin Zhai & Richard Reed & Anthony Mills, 2014. "Factors impeding the offsite production of housing construction in China: an investigation of current practice," Construction Management and Economics, Taylor & Francis Journals, vol. 32(1-2), pages 40-52, February.
    3. Junlong Peng & Jing Zhou & Fanyi Meng & Yan Yu, 2021. "Analysis on the hidden cost of prefabricated buildings based on FISM-BN," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-20, June.
    4. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    5. Unknown, 2016. "Energy for Sustainable Development," Conference Proceedings 253270, Guru Arjan Dev Institute of Development Studies (IDSAsr).
    6. Shtub, Avraham & Zimerman, Yoav, 1993. "A neural-network-based approach for estimating the cost of assembly systems," International Journal of Production Economics, Elsevier, vol. 32(2), pages 189-207, September.
    7. Zhenmin Yuan & Guodong Ni & Linxiu Wang & Yaning Qiao & Chengshuang Sun & Na Xu & Wenshun Wang, 2020. "Research on the Barrier Analysis and Strength Measurement of a Prefabricated Building Design," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    8. He Wang & Yinqi Zhang & Weijun Gao & Soichiro Kuroki, 2020. "Life Cycle Environmental and Cost Performance of Prefabricated Buildings," Sustainability, MDPI, vol. 12(7), pages 1-19, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Merve Anaç & Gulden Gumusburun Ayalp & Kamil Erdayandi, 2023. "Prefabricated Construction Risks: A Holistic Exploration through Advanced Bibliometric Tool and Content Analysis," Sustainability, MDPI, vol. 15(15), pages 1-31, August.
    2. Shengxi Zhang & Zhongfu Li & Shengbin Ma & Long Li & Mengqi Yuan, 2022. "Critical Factors Influencing Interface Management of Prefabricated Building Projects: Evidence from China," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    3. Qiuyu Wang & Zhiqi Gong & Chengkui Liu, 2022. "Risk Network Evaluation of Prefabricated Building Projects in Underdeveloped Areas: A Case Study in Qinghai," Sustainability, MDPI, vol. 14(10), pages 1-26, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sriroop Chaudhuri & Mimi Roy & Louis M. McDonald & Yves Emendack, 2021. "Reflections on farmers’ social networks: a means for sustainable agricultural development?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 2973-3008, March.
    2. Houchao Sun & Yuwei Fang & Minggan Yin & Feiting Shi, 2023. "Research on the Restrictive Factors of Vigorous Promotion of Prefabricated Buildings in Yancheng under the Background of “Double Carbon”," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    3. Zaunbrecher, Barbara S. & Linzenich, Anika & Ziefle, Martina, 2017. "A mast is a mast is a mast…? Comparison of preferences for location-scenarios of electricity pylons and wind power plants using conjoint analysis," Energy Policy, Elsevier, vol. 105(C), pages 429-439.
    4. Jörn Harfst & Jasmin Sandriester & Wolfgang Fischer, 2021. "Industrial Heritage Tourism as a Driver of Sustainable Development? A Case Study of Steirische Eisenstrasse (Austria)," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    5. Rahman, Shaikh Moksadur, 2020. "Relationship between Job Satisfaction and Turnover Intention: Evidence from Bangladesh," Asian Business Review, Asian Business Consortium, vol. 10(2), pages 99-108.
    6. Villanthenkodath, Muhammed Ashiq & Mahalik, Mantu Kumar, 2021. "Does economic growth respond to electricity consumption asymmetrically in Bangladesh? The implication for environmental sustainability," Energy, Elsevier, vol. 233(C).
    7. Shahbaz, Muhammad & Hoang, Thi Hong Van & Mahalik, Mantu Kumar & Roubaud, David, 2017. "Energy consumption, financial development and economic growth in India: New evidence from a nonlinear and asymmetric analysis," Energy Economics, Elsevier, vol. 63(C), pages 199-212.
    8. Schlör, Holger & Venghaus, Sandra & Hake, Jürgen-Friedrich, 2018. "The FEW-Nexus city index – Measuring urban resilience," Applied Energy, Elsevier, vol. 210(C), pages 382-392.
    9. Wang Kai, 2019. "Towards a Taxonomy of Idea Generation Techniques," Foundations of Management, Sciendo, vol. 11(1), pages 65-80, January.
    10. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    11. Mollik, Sazib & Rashid, M.M. & Hasanuzzaman, M. & Karim, M.E. & Hosenuzzaman, M., 2016. "Prospects, progress, policies, and effects of rural electrification in Bangladesh," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 553-567.
    12. Obsatar Sinaga & Mohd Haizam Mohd Saudi & Djoko Roespinoedji & Mohd Shahril Ahmad Razimi, 2019. "The Dynamic Relationship between Natural Gas and Economic Growth: Evidence from Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 388-394.
    13. Asongu, Simplice A. & Odhiambo, Nicholas M., 2021. "Inequality, finance and renewable energy consumption in Sub-Saharan Africa," Renewable Energy, Elsevier, vol. 165(P1), pages 678-688.
    14. Bevilacqua, Maurizio & Ciarapica, Filippo Emanuele, 2018. "Human factor risk management in the process industry: A case study," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 149-159.
    15. Naveena Prakasam & Louisa Huxtable-Thomas, 2021. "Reddit: Affordances as an Enabler for Shifting Loyalties," Information Systems Frontiers, Springer, vol. 23(3), pages 723-751, June.
    16. Colin Jerolmack & Alexandra K. Murphy, 2019. "The Ethical Dilemmas and Social Scientific Trade-offs of Masking in Ethnography," Sociological Methods & Research, , vol. 48(4), pages 801-827, November.
    17. Valeriy Makarov & Albert Bakhtizin, 2014. "The Estimation Of The Regions’ Efficiency Of The Russian Federation Including The Intellectual Capital, The Characteristics Of Readiness For Innovation, Level Of Well-Being, And Quality Of Life," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(4), pages 9-30.
    18. Shirzad, Mohammad & Kazemi Shariat Panahi, Hamed & Dashti, Behrouz B. & Rajaeifar, Mohammad Ali & Aghbashlo, Mortaza & Tabatabaei, Meisam, 2019. "A comprehensive review on electricity generation and GHG emission reduction potentials through anaerobic digestion of agricultural and livestock/slaughterhouse wastes in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 111(C), pages 571-594.
    19. Zhao, Jing & Knoop, Victor L. & Wang, Meng, 2020. "Two-dimensional vehicular movement modelling at intersections based on optimal control," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 1-22.
    20. Teng, Meixuan & Burke, Paul J. & Liao, Hua, 2019. "The demand for coal among China's rural households: Estimates of price and income elasticities," Energy Economics, Elsevier, vol. 80(C), pages 928-936.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:3:p:1864-:d:743344. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.