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

    Source: Journal of Construction Engineering and Management:;2018:;Volume ( 144 ):;issue: 002
    Author:
    Khanzadi Mostafa;Nasirzadeh Farnad;Dashti Mohammad Saleh
    DOI: 10.1061/(ASCE)CO.1943-7862.0001430
    Publisher: American Society of Civil Engineers
    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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      Fuzzy Cognitive Map Approach to Analyze Causes of Change Orders in Construction Projects

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    https://yetl.yabesh.ir/yetl1/handle/yetl/4250293
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    contributor authorKhanzadi Mostafa;Nasirzadeh Farnad;Dashti Mohammad Saleh
    date accessioned2019-02-26T07:55:19Z
    date available2019-02-26T07:55:19Z
    date issued2018
    identifier other%28ASCE%29CO.1943-7862.0001430.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4250293
    description abstractThe 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.
    publisherAmerican Society of Civil Engineers
    titleFuzzy Cognitive Map Approach to Analyze Causes of Change Orders in Construction Projects
    typeJournal Paper
    journal volume144
    journal issue2
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0001430
    page4017111
    treeJournal of Construction Engineering and Management:;2018:;Volume ( 144 ):;issue: 002
    contenttypeFulltext
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