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Abdelgawad, M A M (2011) Hybrid decision support system for risk criticality assessment and risk analysis, Unpublished PhD Thesis, , University of Alberta (Canada).
- Type: Thesis
- Keywords: expert system; liability; reliability; risk assessment; risk analysis; risk identification; risk management; failure; case studies; fuzzy logic; risk analysis; Monte Carlo
- ISBN/ISSN:
- URL: https://www.proquest.com/docview/848629819
- Abstract:
Risk management is essential for the construction industry to successfully fulfill project objectives. Several studies were conducted in the past decade to support quantitative risk analysis. These studies were based on using some of the commonly used techniques such as risk matrix, decision trees, Monte Carlo, and sensitivity analysis. However, some of these techniques are limited because they either do not support quantitative risk analysis, or are difficult to be utilized due to the required amount of data to support quantitative risk analysis. To address such limitations, a comprehensive framework was developed, based on combining three well-known techniques in reliability engineering, i.e., failure mode and effect analysis, fault trees, and event trees with fuzzy logic. Fuzzy logic and failure mode and effect analysis were first combined to provide an answer to the problem of identifying of critical risk events through the development of a fuzzy expert system software package named Risk Criticality Analyzer. To support quantitative risk analysis in the construction industry, fault tree and event tree were combined, and fuzzy logic is used to solve both of them. Fuzzy arithmetic operations on fuzzy numbers were used to represent logical gates in the fault tree structure, and to conduct event tree analysis. To automate solving both fault trees and event trees, Fuzzy Reliability Analyzer was designed and implemented using Visual Basic.net. Both tools were then validated through case studies. The results indicate that by using the proposed methodology, the risk can be assessed effectively and efficiently. The proposed framework presented in this research provides the contribution of combining fuzzy logic with failure mode and effect analysis, fault trees, and event trees in a comprehensive framework to support risk identification, risk assessment, and risk response. Since the proposed framework is based on using linguistic terms, risk analysts are offered a more convenient and practical framework to conduct risk analysis. The proposed framework was able to address several limitations attributed to the conventional application of failure mode and effect analysis and offered a generic framework that can be adapted to fit any industry or organization.
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