Fuzzy Bayesian schedule risk network for offshore wind turbine installation

Abstract Offshore Wind Turbine installation schedule risk analysis is a complex task especially in Taiwan due to the fact that involves huge amount of uncertainties such as typhoons, high winds etc. which have high dependency on the project activities. As a result of this, there is insufficient data for analysis and thus leading to a delay in the successful execution of the project. This study developed the Fuzzy Bayesian Network-Monte Carlo Simulation (FBN-MCS) to model uncertainties having impact on the project duration of offshore wind turbine installation and also to find the correlation between the risks and project duration. Fuzzy Sets Theory (FST) were used to define the membership functions for each risk with the help of experts’ survey. The Bayesian Network (BN) was applied to find the dependency relationship between the risk factors affecting the installation identified through literatures and experts. Monte Carlo Simulation (MCS) model then evaluated the dependent posterior probabilities generated from the BN as independent variables to find their correlation and determine the total project. The proposed model was tested on Taipower Offshore Wind Farm Phase 1 Project, Taiwan, to assess its applicability and the results proved to address the study objectives.

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