In this work, an assessment of four types of risks is carried out for wind turbines during four phases, namely: transportation, installation, operation and maintenance. This work mainly focuses on onshore type of wind turbines and briefly mentioning the offshore wind turbines. The introduction gives an overview of the main parts and components of wind turbine, in addition to discussing the process of risk assessment and the procedure to be followed in this study. The paper focuses on the following four risks: the risk of transporting large-scale wind turbine parts and components, the risk of workers slipping, tripping and falling during installation and maintenance of wind turbines, the risk of working in confined spaces, and finally the risk of ice accretion and irregular shedding when the wind turbine is in operation phase or even when it is stationary. The last type of risk is highly observed in cold climate regions. The four mentioned types of risks are the main ones out of the many risks that could appear during transporting, installing, operating and maintaining wind turbines. The main aim of this work is to contribute in the proper risk assessment of potential hazards, which enhances the ability to devise passive and active protection measures to reduce the effects of a catastrophic event.

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The encounter of vortices generated by a leading aircraft during takeoff or landing can be a source of hazard to a following aircraft. In spite of airport efforts to keep safe separation distances between aircrafts, a number of them encounter severe vortices each year. It has been challenging to accurately identify those encounters by manual approaches. To mitigate the impact of vortex encounters on an aircraft, it is important that more reliable identification techniques be developed. This research is a contribution towards the automatic identification of vortex encounters using artificial neural networks. Multilayer feedforward neural networks are trained using the back-propagation learning algorithm to classify flight events into either vortex encounters or other events. Using salient inputs such as aircraft roll angle, normal acceleration and lateral acceleration, the neural networks are able to achieve an overall average identification rate of about 88%. These results confirm the authors' earlier assumption on using a reduced set of critical inputs to properly classify aircraft vortex encounters.