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A Fuzzy Hybrid Intelligent Model for Project Competencies and Performance Evaluation and Prediction in the Construction Industry

  • Author / Creator
    Omar, Moataz N
  • In contemporary construction environments, construction companies measure their performance against a set of predefined performance indicators. These performance indicators are governed by the ability of the company to maintain necessary sets of “competencies” that empower the successful execution of construction projects. Competencies in general are difficult to define and measure due to the multidimensional and subjective nature of their assessment. Additionally, there is little consensus on the performance indicators that capture the different critical aspects of how well a construction project is performing. This thesis expands the body of knowledge on project competencies and performance by demonstrating the power of fuzzy logic combined with other artificial intelligence modeling (i.e., neural networks) in developing a model capable of identifying the relationship between the different project competencies and project performance on construction projects. First, this research identifies 41 project competencies with a total of 248 criteria for evaluating the different project competencies. Appropriate measurement scales are developed for the different project competencies’ evaluation criteria. This research also identifies seven performance categories with 46 key project performance indicators. Second, a systematic framework and methodology are developed to measure project competencies and project key performance indicators on construction projects. Finally, several state of the art techniques are developed and applied to model the relationship between project competencies and project performance namely: 1) a new prioritized aggregation method, 2) a dimensionality reduction technique, and 3) a fuzzy hybrid intelligent model incorporating fuzzy logic and artificial neural networks. The new prioritized aggregation method is developed in this research to consider the prioritized relationship between criteria pertaining to the different project competencies. This prioritized aggregation method is developed for both crisp and fuzzy environments. Then, a dimensionality reduction technique, through the application of feature extraction, is applied to reduce the dimensionality of the model input (i.e., project competencies) and enhance its capability in providing more accurate outputs (i.e., key project performance indicators). Finally, granular AND/OR fuzzy neural networks are constructed using fuzzy logic and artificial neural networks to identify and map the relationship between the different project competencies and project key performance indicators. Data collected from seven construction projects are used to train and test the developed granular AND/OR fuzzy neural networks. This thesis contributes to the current body of knowledge in project competencies and performance by establishing a standardized framework and methodology for evaluating the impact of construction project competencies on key project performance indicators. Furthermore, this thesis applies advanced modeling techniques through the application of fuzzy logic and artificial neural networks to identify and model the relationship between project competencies and project key performance indicators.

  • Subjects / Keywords
  • Graduation date
    2016-06
  • Type of Item
    Thesis
  • Degree
    Doctor of Philosophy
  • DOI
    https://doi.org/10.7939/R3TB0Z59X
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Doctoral
  • Department
    • Department of Civil and Environmental Engineering
  • Specialization
    • Construction Engineering and Management
  • Supervisor / co-supervisor and their department(s)
    • Aminah Robinson Fayek (Civil and Environmental Engineering)
  • Examining committee members and their departments
    • Yasser Mohammed (Civil and Environmental Engineering)
    • Steve Thomas (Civil Engineering, University of Texas)
    • Amy Kim (Transportation)
    • Marek Reformat (Electrical Engineering)