基于贝叶斯网络的大型公共项目进度延误风险研究-《郑州大学学报(工学版)》

文章信息/Info

Title:: Research on Delay Risk of Large Complex Public Projects ba＜x＞sed on Bayesian Network

Author(s):: Chen Yuan; Jin Rui; Chaya Chuang;; School of Civil Engineering, Zhengzhou University;

Keywords:: Large -scale complex public projects; progress delay risk; Beyes network; interpretation of structural models

摘要:: 目前针对大型公共类项目进度风险的研究主要侧重于识别进度延误风险因素以及分析风险因素之间的相互作用，缺少对风险因素的敏感性、重要性以及风险因素间的影响强度的定量分析。本研究将贝叶斯网络和解释结构模型应用于大型复杂公共项目进度风险的评估，在收集的七个来源于真实案例的延期风险因素体系的基础上提炼出24个主要延期风险因素，基于136份调查问卷构建并验证了贝叶斯网络模型。使用GeNIe对贝叶斯网络模型进行参数学习，获得基于样本数据的进度延误风险水平的概率分布，并利用贝叶斯网络的逆向推理、敏感性分析及影响强度分析，明确了大型复杂公共项目中导致进度延误的关键因素、敏感因素以及各风险因素之间的定量关系，为进度风险管理提供了科学有效的理论依据和控制工具。

Abstract:: The current research on the schedule risk of large public projects mainly focuses on identifying the risk factors of schedule delay and analyzing the interaction between the risk factors, there is a lack of quantitative analysis of the sensitivity and importance of risk factors and the intensity of influence between risk factors. This research applies Bayesian network and interpretive structure model to the assessment of schedule risk of large and complex public projects, ba＜x＞sed on the collection of seven deferred risk factor systems derived from real cases, 24 major deferred risk factors were refined, and the Bayesian network model was constructed and verified ba＜x＞sed on 146 questionnaires. Use GeNIe to learn the parameters of the Bayesian network model to obtain the probability distribution of the schedule delay risk level ba＜x＞sed on the sample data. And use Bayesian network reverse reasoning, sensitivity analysis and influence intensity analysis, clarifies the key factors , sensitive factors and the quantitative relationship between various risk factors that cause schedule delays in large-scale and complex public projects, It provides scientific and effective theoretical basis and control tools for schedule risk management.