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        Chapter Tree-Network Overrun Model Associated with Pilots’ Actions and Flight Operational Procedures

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        Author(s)
        Ramos Martins, Marcelo
        Ribeiro Correia, Anderson
        Carvalho Galvão Silva Pinto Bandeira, Michelle
        Language
        English
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        Abstract
        The runway excursions are defined as the exit of an aircraft from the surface of the runway. These excursions can take place at takeoff or at landing and consist of two types of events: veer off and overrun. This last one, which occurs when the aircraft exceeds the limits at the end of the runway, is the event of interest in the current study. This chapter aims to present an accident model with a new approach in aeronautical systems, based on the tasks of the pilots related to the operational procedures necessary for the approach and landing, in order to obtain the chain of events that lead to this type of accident. Thus, the tree-network overrun model (TNO model) was proposed, unlike most traditional models, which consider only the hardware failures or which do not satisfactorily explain the interrelationship between the factors influencing the operator. The proposed model is developed in a fault tree and transformed into a Bayesian network up to the level of the basic elements. The results showed the qualitative model of the main tasks performed by the pilots and their relation to the accident. It has also been suggested how to find and estimate the probability of factors that can impact on each of the tasks.
        URI
        https://library.oapen.org/handle/20.500.12657/49319
        Keywords
        overrun, TNO model, fault tree, Bayesian networks, safety, aviation
        DOI
        10.5772/intechopen.81663
        Publisher
        InTechOpen
        Publisher website
        https://www.intechopen.com/
        Publication date and place
        2019
        Classification
        Astronautics
        Rights
        https://creativecommons.org/licenses/by/3.0/
        • Imported or submitted locally

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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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