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

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. Th...

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Autores principales: Junlong Peng, Jing Zhou, Fanyi Meng, Yan Yu
Formato: article
Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/39f9af9d3ff242eb8df456c2899aa358
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Sumario: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.