A Bayesian belief network predictive model for construction delay avoidance in the UK

被引:7
|
作者
Wang, Peipei [1 ]
Fenn, Peter [2 ]
Wang, Kun [2 ]
Huang, Yunhan [1 ]
机构
[1] Jiangsu Ocean Univ, Sch Civil & Ocean Engn, Lianyungang, Peoples R China
[2] Univ Manchester, Sch Mech Aerosp & Civil Engn, Manchester, Lancs, England
关键词
Novel model; Construction; Project management; Questionnaire survey; Risk management; CRITICAL SUCCESS FACTORS; PROJECTS; COST; RISK; IDENTIFICATION; TIME;
D O I
10.1108/ECAM-10-2020-0873
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Purpose The purpose of this research is to advise on UK construction delay strategies. Critical delay factors were identified and their interrelationships were explored; in addition, a predictive model was established upon the factors and interrelationships to calculate delay potentials. Design/methodology/approach The critical causes were identified by a literature review, verified by an open-ended questionnaire survey and then analysed with 299 samples returned from structured questionnaire surveys. The model consisted of factors screened out by Pearson product-moment correlational coefficient, constructed by a logical reasoning process and then quantified by conducting Bayesian belief networks parameter learning. Findings The technical aspect of construction project management was less critical while the managerial aspect became more emphasised. Project factors and client factors present relatively weak impact on construction delay, while contractor factors, contractual arrangement factors and distinctively interaction factors present relatively strong impact. Research limitations/implications This research does not differentiate delay types, such as excusable vs non-excusable ones and compensable vs non-compensable ones. The model nodes have been tested to be critical to construction delay, but the model structure is mostly based on previous literature and logical deduction. Further research could be done to accommodate delay types and test the relationships. Originality/value This research updates critical delay factor list for the UK construction projects, suggesting general rules for resource allocation concerning delay avoidance. Besides, this research establishes a predictive model, assisting delay avoidance strategies on a case-by-case basis.
引用
收藏
页码:2011 / 2026
页数:16
相关论文
共 50 条
  • [1] Weighted Bayesian Belief Network for diabetics: a predictive model
    Kharya, Shweta
    Soni, Sunita
    Pati, Abhilash
    Panigrahi, Amrutanshu
    Giri, Jayant
    Qin, Hong
    Mallik, Saurav
    Nayak, Debasish Swapnesh Kumar
    Swarnkar, Tripti
    FRONTIERS IN ARTIFICIAL INTELLIGENCE, 2024, 7
  • [2] Aids to Bayesian belief network construction
    Rajabally, E
    Sen, P
    Whittle, S
    Dalton, J
    2004 2ND INTERNATIONAL IEEE CONFERENCE INTELLIGENT SYSTEMS, VOLS 1 AND 2, PROCEEDINGS, 2004, : 457 - 461
  • [3] A Bayesian belief network model of bridge deterioration
    Attoh-Okine, N. O.
    Bowers, S.
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-BRIDGE ENGINEERING, 2006, 159 (02) : 69 - 76
  • [4] A Bayesian Belief Network model for software risk analysis
    Hu, Yong
    Chen, Juhua
    Huang, Jiaxing
    Xiao, Jinghua
    Xie, Kang
    Tang, Junbiao
    PROGRESS IN INTELLIGENCE COMPUTATION AND APPLICATIONS, PROCEEDINGS, 2007, : 609 - 613
  • [5] A Bayesian belief network model of a virtual learning community
    Daniel, Ben K.
    Schwier, Richard A.
    International Journal of Web Based Communities, 2007, 3 (02) : 151 - 169
  • [6] A Bayesian Belief Network Model for Breast Cancer Diagnosis
    Wongthanavasu, S.
    OPERATIONS RESEARCH PROCEEDINGS 2010, 2011, : 3 - 8
  • [7] A Bayesian Belief Network model of organizational factors for improving safe work behaviors in Thai construction industry
    Jitwasinkul, Bhanupong
    Hadikusumo, Bonaventura H. W.
    Memon, Abdul Qayoom
    SAFETY SCIENCE, 2016, 82 : 264 - 273
  • [8] Application of multiple linear regression and Bayesian belief network approaches to model life risk to beach users in the UK
    Stokes, Christopher
    Masselink, Gerhard
    Revie, Matthew
    Scott, Timothy
    Purves, David
    Walters, Thomas
    OCEAN & COASTAL MANAGEMENT, 2017, 139 : 12 - 23
  • [9] Using Bayesian belief network and time-series model to conduct prescriptive and predictive analytics for computer industries
    Wang, Chih-Hsuan
    Cheng, Hou-Yu
    Deng, Yu-Ting
    COMPUTERS & INDUSTRIAL ENGINEERING, 2018, 115 : 486 - 494
  • [10] Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients: Using a Bayesian belief network to categorize LOS
    Michalowski W.
    Wilk S.
    Thijssen A.
    Li M.
    Health Care Management Science, 2006, 9 (4) : 341 - 348