Fuzzy Cognitive Map Approach to Analyze Causes of Change Orders in Construction Projects

被引:31
|
作者
Khanzadi, Mostafa [1 ]
Nasirzadeh, Farnad [2 ,3 ]
Dashti, Mohammad Saleh [1 ]
机构
[1] Iran Univ Sci & Technol, Sch Civil Engn, POB 16846-13114, Tehran, Iran
[2] Deakin Univ, Sch Architecture & Built Environm, Geelong, Vic 3220, Australia
[3] Payame Noor Univ, Fac Engn, Dept Civil Engn, POB 19395-3697, Tehran, Iran
关键词
Change orders; Root causes; Fuzzy cognitive maps (FCMs); Analyzing; Underpass construction projects; Contracting; CHANGE MANAGEMENT; LABOR PRODUCTIVITY; MODEL; IMPACT; DESIGN; RISK; VALIDATION; SINGAPORE; SYSTEMS; COST;
D O I
10.1061/(ASCE)CO.1943-7862.0001430
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The negative impacts of change orders on construction projects are well documented in the literature. Effective management of change orders requires a comprehensive approach to identify and analyze their causes. A change order in construction projects is usually an outcome of combining several interrelated causes, rather than a single cause. Most of the previous studies have considered the causes of change orders as independent causes. However, the causes of change orders have a complicated causal structure, which makes it difficult to independently analyze and prioritize them. Therefore, this study contributes to the body of knowledge in change order management by proposing a fuzzy cognitive map (FCM) approach, which has the capability to analyze the causes of change orders considering their entire causal interactions. The root causes of change orders, as well as the direct ones, can be identified and prioritized by the proposed approach. The manageability of the causes can also be analyzed by the FCM approach. The performance of the proposed approach is evaluated by implementing it in a real construction project. A modeling-validating approach is used to construct and validate the FCM model. It is believed that the proposed FCM approach presents a powerful tool for analyzing the causes of change orders. In addition, this research contributes to knowledge in the area of FCMs by proposing a new inference law.
引用
收藏
页数:12
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