Fuzzy Cognitive Map Approach to Analyze Causes of Change Orders in Construction Projects
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 2
Abstract
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.
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Data Availability Statement
Data generated and analyzed during the study are available from the corresponding author by request. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
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Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 2 • February 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 27, 2017
Accepted: Aug 3, 2017
Published online: Dec 6, 2017
Published in print: Feb 1, 2018
Discussion open until: May 6, 2018
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