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

被引:8
|
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] Auditing Construction Cost from an In-Process Perspective Based on a Bayesian Predictive Model
    Wang, Peipei
    Wang, Kun
    Huang, Yunhan
    Fenn, Peter
    Stewart, Ian
    JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2022, 148 (04)
  • [3] Bayesian Belief Network Model for Decision Making in Highway Maintenance: Case Studies
    Bayraktar, Mehmet Emre
    Hastak, Makarand
    JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE, 2009, 135 (12): : 1357 - 1369
  • [4] Prioritizing Risks in Last Mile Delivery: A Bayesian Belief Network Approach
    Mismar, Hajed
    Shamayleh, Abdulrahim
    Qazi, Abroon
    IEEE ACCESS, 2022, 10 : 118551 - 118562
  • [5] A new method for flood disaster resilience evaluation: A hidden markov model based on Bayesian belief network optimization
    Sun, Tianyu
    Liu, Deping
    Liu, Dong
    Zhang, Liangliang
    Li, Mo
    Khan, Muhammad Imran
    Li, Tianxiao
    Cui, Song
    JOURNAL OF CLEANER PRODUCTION, 2023, 412
  • [6] A Bayesian belief network model and tool to evaluate risk and impact in software development projects
    Hui, AKT
    Liu, DB
    ANNUAL RELIABILITY AND MAINTAINABILITY SYMPOSIUM, 2004 PROCEEDINGS, 2004, : 297 - 301
  • [7] Development of a time-variant causal model of human error in construction with dynamic Bayesian network
    Ma, Zhangming
    Chong, Heap-Yih
    Liao, Pin-Chao
    ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT, 2021, 28 (01) : 291 - 307
  • [8] Bayesian Belief Network Approach for Supply Risk Modelling
    Jindal, Anil
    Sharma, Satyendra Kumar
    Routroy, Srikanta
    INTERNATIONAL JOURNAL OF INFORMATION SYSTEMS AND SUPPLY CHAIN MANAGEMENT, 2022, 15 (01)
  • [9] Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model
    Stojadinovic, Alexander
    Bilchik, Anton
    Smith, David
    Eberhardt, John S.
    Ben Ward, Elizabeth
    Nissan, Aviram
    Johnson, Eric K.
    Protic, Mladjan
    Peoples, George E.
    Avital, Itzhak
    Steele, Scott R.
    ANNALS OF SURGICAL ONCOLOGY, 2013, 20 (01) : 161 - 174
  • [10] Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model
    Yaseen, Zaher Mundher
    Ali, Zainab Hasan
    Salih, Sinan Q.
    Al-Ansari, Nadhir
    SUSTAINABILITY, 2020, 12 (04)