Hybrid Fuzzy System Dynamics Model for Risk Analysis and Contingency Determination

• Author / Creator

  Siraj, Nasir B

• The unique nature of construction projects and the uncertainties encountered during project execution make construction a highly-risk prone industry. Risks on construction projects (especially large-scale projects) are extremely complex and highly dynamic, and substantial interrelationships exist among risks throughout the lifecycle of the project. The dynamic nature of risk and opportunity events and the causal interactions and dependencies between them have a considerable effect on risk analysis and contingency determination; their lack of consideration can lead to overestimation or underestimation of contingency.
  System dynamics (SD) is a viable option for modeling and analyzing construction risks to determine work package and project contingency, as it is capable of handling such characteristics. However, conventional SD models do not effectively account for the subjective uncertainties associated with system variables, the imprecise nature of factors that influence the variables, and the vague interdependencies between variables. Therefore, a hybrid fuzzy system dynamics (FSD) model that combines the strengths of both SD and fuzzy logic is developed in this research to analyze the severity of interrelated and interacting risk and opportunity events on work package cost and determine work package and project contingencies.
  A systematic review and detailed content analysis of selected articles was conducted to identify, categorize, and rank potential risk and opportunity events affecting construction projects. A fuzzy-based risk assessment procedure, which assesses the probability and impact of both risk and opportunity events and considers the experts’ expertise level, was employed to assess and prioritize risk and opportunity events. Linguistic scales, represented by fuzzy numbers, were used to allow experts to use natural language to assess the probability and impact of risk and opportunity events and the causal relationships between them. The alpha-cut method and the extension principle based on drastic t-norm were implemented in the FSD model to carry out fuzzy arithmetic operations whenever a fuzzy variable was involved in a given mathematical equation to determine an intermediary or final output. The comparison of project contingency fuzzy numbers obtained based on the two fuzzy arithmetic methods indicates that the accumulation of fuzziness and overestimation of uncertainty encountered in the FSD model was significantly reduced by using the drastic t-norm instead of the α-cut method. Structural and behavioral validation tests were performed to validate the FSD model. Moreover, the performance of the FSD model was evaluated by implementing it using actual project case study and the results were compared against contingency values obtained from Monte Carlo simulation and Fuzzy Contingency Determinator©.
  This research addresses the lack of a systematic review and content analysis of published articles related to risk identification and provides a useful reference on common potential risks affecting construction projects. Moreover, it provides a systematic risk assessment and prioritization procedure. This research provides both researchers and construction industry practitioners with a hybrid FSD modeling approach for understanding the dynamic causal interactions and dependencies among risk and opportunity events and determining their severity on work package and project cost contingency using subjective evaluation and experience. It also provides a structured and systematic method of defining causal relationships among risk and opportunity events and constructing causal loop diagrams in the qualitative FSD model. Additionally, this research provides a basis for the implementation of fuzzy arithmetic methods (both alpha-cut and extension principle) and defuzzification methods in FSD modeling for risk analysis and contingency determination.

• Subjects / Keywords

  • Construction
  • Contingency
  • Risk analysis
  • System dynamics
  • Fuzzy DEMATEL
  • Fuzzy logic
  • Risk identification

• Graduation date

  Fall 2019

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-7q1y-9r95

• License

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