[1]陈远,金蕊,查亚闯.基于贝叶斯网络的大型公共项目进度延误风险研究[J].郑州大学学报(工学版),2022,43(02):91-97.[doi:10.13705/j.issn.1671-6833.2022.02.002]
　CHEN Yuan,JIN Rui,ZHA Yachuang.Research on Delay Risk of Large Complex Public Projects Based on Bayesian Network[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):91-97.[doi:10.13705/j.issn.1671-6833.2022.02.002]

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基于贝叶斯网络的大型公共项目进度延误风险研究()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

43

期数:

2022年02期

页码:

91-97

栏目:

出版日期:

2022-02-27

文章信息/Info

Title:

Research on Delay Risk of Large Complex Public Projects Based on Bayesian Network

作者:

陈远; 金蕊; 查亚闯

郑州大学土木工程学院;

Author(s):

CHEN Yuan;  JIN Rui;  ZHA Yachuang

School of Civil Engineering, Zhengzhou University, Zhengzhou 450001, China

关键词:

大型复杂公共项目; 进度延误风险; 贝叶斯网络; 解释结构模型

Keywords:

large and complex public projects;  schedule delay risk;  Bayesian network;  interpretative structural model

分类号:

TU712+.4 F294.1

DOI:

10.13705/j.issn.1671-6833.2022.02.002

文献标志码:

A

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 delay risk of large public projects mainly focused on identifying the risk factors of schedule delay and analyzing the interaction between the risk factors, there was a lack of quantitative analysis of the sensitivity and importance of risk factors and the intensity of influence between risk factors. In this study, Bayesian network and interpretive structure model were used in the assessment of schedule risk of large and complex public projects. Based on the collection of 7 deferred risk factor systems derived from real cases, 24 major deferred risk factors were refined, then the explanatory structure model was used to process the risk factors into four different levels, and the hierarchical structure diagram was transformed into a Bayesian network. Finally, according to the established Bayesian network, the risk factors were collected and evaluated based on actual project data. The collected data were imported into GeNIe, the parameters of the Bayesian network model was learnt, and obtain the probability distribution of the risk level of schedule delay based on the sample data.And using Bayesian network reverse reasoning, sensitivity analysis, and influence intensity analysis were used, the key factors and sensitive factors that lead to schedule delays in large and complex public projects are clarified, and a scientific and effective theoretical basis and control tools were offered for schedule risk management.

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