Schedule Risk Analysis of Prefabricated Building Projects Based on DEMATEL-ISM and Bayesian Networks

被引:0
作者
Zhong, Chunling [1 ,2 ]
Zhang, Siyu [1 ]
机构
[1] Jilin Jianzhu Univ, Sch Econ & Management, Changchun 130118, Peoples R China
[2] Jilin Univ Architecture & Technol, Sch Civil Engn, Changchun 130114, Peoples R China
关键词
prefabricated building; DEMATEL-ISM; project schedule management; Bayesian network; risk analysis;
D O I
10.3390/buildings15030508
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The schedule is a critical factor in the development of prefabricated buildings. This paper establishes the schedule risk influencing factors for prefabricated building projects across five dimensions-design, production, transportation, installation, and others-encompassing a total of 14 factors. By integrating DEMATEL and ISM, it constructs a hierarchical network model using expert knowledge and maps it to Bayesian networks (BN), and the node probabilities were calculated using fuzzy set theory combined with the noisy-OR gate model. This DEMATEL-ISM-BN model not only infers the probability of schedule risk occurrence in prefabricated construction projects through causal reasoning and controls the schedule risk of prefabricated construction projects, but it also deduces the posterior probabilities of other influencing factors when a schedule risk occurs through diagnostic reasoning. This approach identifies the key factors contributing to schedule risk and pinpoints the final influencing factors. Research has shown that the three influencing factors of "tower crane worker lifting level", "construction worker component installation technology", and "design changes" significantly affect project progress, providing a new risk assessment tool for prefabricated building project progress, effectively helping enterprises identify potential risks, formulate risk control strategies, improve project success rates, and overall benefits.
引用
收藏
页数:22
相关论文
共 43 条
  • [1] Multi-objective genetic optimization for scheduling a multi-storey building
    Agrama, Fatma A.
    [J]. AUTOMATION IN CONSTRUCTION, 2014, 44 : 119 - 128
  • [2] [Anonymous], 2016, Journal of Construction in Developing Countries, V21, P51, DOI [10.21315/jcdc2016.21.1.4, DOI 10.21315/JCDC2016.21.1.4, 10.21315/jcdc2016.21.1]
  • [3] Arantes A., 2015, P IND ENG SYST MAN I
  • [4] Causes of delay in large construction projects
    Construction Engineering and Management Department, King Fahd University of Petroleum and Minerals, Box # 680, Dhahran, 31261, Saudi Arabia
    [J]. Int. J. Proj. Manage., 2006, 4 (349-357): : 349 - 357
  • [5] Babatunde Oluwaseun A., 2022, Front. Built Environ, V8
  • [6] Discrete Bayesian Network Classifiers: A Survey
    Bielza, Concha
    Larranaga, Pedro
    [J]. ACM COMPUTING SURVEYS, 2014, 47 (01)
  • [7] Analysis on Supply Chain Risk Factors of Prefabricated Buildings Using AHP-DEMATELISM Model
    Cai, Qiang
    Du, Yunchao
    Wang, Renxiang
    [J]. TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2023, 30 (05): : 1379 - 1386
  • [8] Construction Schedule Risk Assessment and Management Strategy for Foreign General Contractors Working in the Ethiopian Construction Industry
    Cheng, Min-Yuan
    Darsa, Mohammadzen Hasan
    [J]. SUSTAINABILITY, 2021, 13 (14)
  • [9] A review of cyber security risk assessment methods for SCADA systems
    Cherdantseva, Yulia
    Burnap, Pete
    Blyth, Andrew
    Eden, Peter
    Jones, Kevin
    Soulsby, Hugh
    Stoddart, Kristan
    [J]. COMPUTERS & SECURITY, 2016, 56 : 1 - 27
  • [10] Francisco Javier D., 1993, Uncertainty in Artificial Intelligence