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.

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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