IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0252138.html
   My bibliography  Save this article

Analysis on the hidden cost of prefabricated buildings based on FISM-BN

Author

Listed:
  • Junlong Peng
  • Jing Zhou
  • Fanyi Meng
  • Yan Yu

Abstract

Facing the pressure of environment, sustainable development is the demand of the current construction industry development. Prefabricated construction technologies has been actively promoted in China. Cost has always been one of the important factors in the development of prefabricated buildings. The hidden cost of prefabricated buildings has a great impact on the total cost of the project, and it exists in the whole process of building construction. In this paper innovatively studies the cost of prefabricated buildings from the perspective of hidden cost. In order to analysis the hidden cost of prefabricated buildings, the influencing factor index system in terms of design, management, technology, policy and environment has been established, which includes 13 factors in total. And the hidden cost analysis model has been proposed based on FISM-BN, this model combines fuzzy interpretive structure model(FISM) with Bayesian network(BN). This model can comprehensively analyze the hidden cost through the combination of qualitative and quantitative methods. And the analysis process is dynamic, not fixed at a certain point in time to analyze the cost. We can get the internal logical relationship among the influencing factors of the hidden cost, and present it in the form of intuitive chart by FISM-BN. Furthermore the model could not only predict the probability of the hidden cost of prefabricated buildings and realize in-time control through causal reasoning, but also predict the posterior probability of other influencing factors through diagnostic reasoning when the hidden cost occurs and find out the key factors that lead to the hidden cost. Then the final influencing factors are determined after one by one check. Finally, the model is demonstrated on the hidden cost analysis of prefabricated buildings the probability of recessive cost is 26%. In the analysis and control of the hidden cost of prefabricated buildings, scientific and effective decision-making and reference opinions are provided for managers.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0252138
    DOI: 10.1371/journal.pone.0252138
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0252138
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0252138&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0252138?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Xiaoyan Jiang & Kun Lu & Bo Xia & Yong Liu & Caiyun Cui, 2019. "Identifying Significant Risks and Analyzing Risk Relationship for Construction PPP Projects in China Using Integrated FISM-MICMAC Approach," Sustainability, MDPI, vol. 11(19), pages 1-31, September.
    2. 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.
    3. Huazan Liu & Yukang He & Qichao Hu & Jianfei Guo & Lan Luo, 2020. "Risk management system and intelligent decision-making for prefabricated building project under deep learning modified teaching-learning-based optimization," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-15, July.
    4. Yadegaridehkordi, Elaheh & Hourmand, Mehdi & Nilashi, Mehrbakhsh & Shuib, Liyana & Ahani, Ali & Ibrahim, Othman, 2018. "Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 199-210.
    5. Ehsan Lotfi & M. Darini & M. R. Karimi-T., 2016. "Cost estimation using ANFIS," The Engineering Economist, Taylor & Francis Journals, vol. 61(2), pages 144-154, April.
    6. Chee Kian Leong, 2016. "Credit Risk Scoring with Bayesian Network Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(3), pages 423-446, 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. 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.

    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. Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    2. Sachan, Swati & Almaghrabi, Fatima & Yang, Jian-Bo & Xu, Dong-Ling, 2024. "Human-AI collaboration to mitigate decision noise in financial underwriting: A study on FinTech innovation in a lending firm," International Review of Financial Analysis, Elsevier, vol. 93(C).
    3. Michail Tsagris, 2021. "A New Scalable Bayesian Network Learning Algorithm with Applications to Economics," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 341-367, January.
    4. Shtub, Avraham & Versano, Ronen, 1999. "Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 201-207, September.
    5. Fredrick Betuel Sawe & Anil Kumar & Jose Arturo Garza‐Reyes & Rohit Agrawal, 2021. "Assessing people‐driven factors for circular economy practices in small and medium‐sized enterprise supply chains: Business strategies and environmental perspectives," Business Strategy and the Environment, Wiley Blackwell, vol. 30(7), pages 2951-2965, November.
    6. Gupta, Shivam & Justy, Théo & Kamboj, Shampy & Kumar, Ajay & Kristoffersen, Eivind, 2021. "Big data and firm marketing performance: Findings from knowledge-based view," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    7. André Luiz Romano & Luís Miguel D. F. Ferreira & Sandra Sofia F. S. Caeiro, 2021. "Modelling Sustainability Risk in the Brazilian Cosmetics Industry," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    8. Wang, Qing, 2007. "Artificial neural networks as cost engineering methods in a collaborative manufacturing environment," International Journal of Production Economics, Elsevier, vol. 109(1-2), pages 53-64, September.
    9. Badreddine Benyacoub & Souad ElBernoussi & Abdelhak Zoglat & Mohamed Ouzineb, 2022. "Credit Scoring Model Based on HMM/Baum-Welch Method," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1135-1154, March.
    10. Dan Su & Yi-Sheng Liu & Xin-Tong Li & Xiao-Yan Chen & Dong-Han Li, 2020. "Management Path of Concrete Beam Bridge in China from the Perspective of Sustainable Development," Sustainability, MDPI, vol. 12(17), pages 1-22, September.
    11. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    12. Libiao Bai & Kanyin Zheng & Zhiguo Wang & Jiale Liu, 2022. "Service provider portfolio selection for project management using a BP neural network," Annals of Operations Research, Springer, vol. 308(1), pages 41-62, January.
    13. Cavalieri, Sergio & Maccarrone, Paolo & Pinto, Roberto, 2004. "Parametric vs. neural network models for the estimation of production costs: A case study in the automotive industry," International Journal of Production Economics, Elsevier, vol. 91(2), pages 165-177, September.
    14. Kusi-Sarpong, Simonov & Orji, Ifeyinwa Juliet & Gupta, Himanshu & Kunc, Martin, 2021. "Risks associated with the implementation of big data analytics in sustainable supply chains," Omega, Elsevier, vol. 105(C).
    15. Wen-Kuo Chen & Ching-Torng Lin, 2021. "Interrelationship among CE Adoption Obstacles of Supply Chain in the Textile Sector: Based on the DEMATEL-ISM Approach," Mathematics, MDPI, vol. 9(12), pages 1-24, June.
    16. Manel Elmsalmi & Wafik Hachicha & Awad M. Aljuaid, 2021. "Prioritization of the Best Sustainable Supply Chain Risk Management Practices Using a Structural Analysis Based-Approach," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    17. Guoxian Cao & Chaoyang Guo & Hezhong Li, 2022. "Risk Analysis of Public–Private Partnership Waste-to-Energy Incineration Projects from the Perspective of Rural Revitalization," Sustainability, MDPI, vol. 14(13), pages 1-19, July.
    18. Asadi, Shahla & Nilashi, Mehrbakhsh & Iranmanesh, Mohammad & Hyun, Sunghyup Sean & Rezvani, Azadeh, 2022. "Effect of internet of things on manufacturing performance: A hybrid multi-criteria decision-making and neuro-fuzzy approach," Technovation, Elsevier, vol. 118(C).
    19. Jafari-Sadeghi, Vahid & Amoozad Mahdiraji, Hannan & Busso, Donatella & Yahiaoui, Dorra, 2022. "Towards agility in international high-tech SMEs: Exploring key drivers and main outcomes of dynamic capabilities," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    20. Gabriel Castelblanco & Jose Guevara & Harrison Mesa & Diego Flores, 2020. "Risk Allocation in Unsolicited and Solicited Road Public-Private Partnerships: Sustainability and Management Implications," Sustainability, MDPI, vol. 12(11), pages 1-28, June.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0252138. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.