Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach

Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and...

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Main Authors: Yusi Cheng, Jingfeng Yuan, Lei Zhu, Wei Li
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Complexity
Online Access:http://dx.doi.org/10.1155/2020/8854609
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spelling doaj-a44f13a2a5714761a4ede2041a5efda62020-12-21T11:41:27ZengHindawi-WileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88546098854609Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network ApproachYusi Cheng0Jingfeng Yuan1Lei Zhu2Wei Li3School of Civil Engineering, Southeast University, Nanjing 210096, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, ChinaConstruction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node (AON) networks. A model of risk propagation is then built based on a susceptible-infected (SI) model considering the effects of the nodal characteristics on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks.http://dx.doi.org/10.1155/2020/8854609
collection DOAJ
language English
format Article
sources DOAJ
author Yusi Cheng
Jingfeng Yuan
Lei Zhu
Wei Li
spellingShingle Yusi Cheng
Jingfeng Yuan
Lei Zhu
Wei Li
Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach
Complexity
author_facet Yusi Cheng
Jingfeng Yuan
Lei Zhu
Wei Li
author_sort Yusi Cheng
title Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach
title_short Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach
title_full Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach
title_fullStr Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach
title_full_unstemmed Risk Propagation Model and Simulation of Schedule Change in Construction Projects: A Complex Network Approach
title_sort risk propagation model and simulation of schedule change in construction projects: a complex network approach
publisher Hindawi-Wiley
series Complexity
issn 1076-2787
1099-0526
publishDate 2020-01-01
description Construction schedules play an important role in construction project management. However, during construction activities, risks may arise due to unexpected schedule changes, resulting in the ineffective delivery of projects. This study aims to reveal the law of schedule change risk propagation and to analyze the effects on the risk propagation through numerical simulations. First, construction projects are represented by activity-on-node (AON) networks. A model of risk propagation is then built based on a susceptible-infected (SI) model considering the effects of the nodal characteristics on the propagation process. Next, the model is tested on a real-world project to examine cascading failures with varying parameters. The experimental results demonstrate that the model is effective in identifying the activities most capable of affecting a project schedule and evaluating the impact of schedule change risk propagation. This study will provide a basis for enhancing the robustness of AON networks and controlling the propagation of schedule change risks.
url http://dx.doi.org/10.1155/2020/8854609
work_keys_str_mv AT yusicheng riskpropagationmodelandsimulationofschedulechangeinconstructionprojectsacomplexnetworkapproach
AT jingfengyuan riskpropagationmodelandsimulationofschedulechangeinconstructionprojectsacomplexnetworkapproach
AT leizhu riskpropagationmodelandsimulationofschedulechangeinconstructionprojectsacomplexnetworkapproach
AT weili riskpropagationmodelandsimulationofschedulechangeinconstructionprojectsacomplexnetworkapproach
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