Machine-learning tool for human factors evaluation - application to lion air Boeing 737-8 max accident

Morais, C. and Yung, K. and Patelli, E.; Papadrakakis, M. and Papadopoulos, V. and Stefanou, G., eds. (2019) Machine-learning tool for human factors evaluation - application to lion air Boeing 737-8 max accident. In: Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. National Technical University of Athens, GRC, pp. 498-508. ISBN 9786188284494 (https://doi.org/10.7712/120219.6355.18709)

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Abstract

The capability of learning from accidents as quickly as possible allows preventing repeated mistakes to happen. This has been shown by the small time interval between two accidents with the same aircraft model: the Boeing 737-8 MAX. However, learning from major accidents and subsequently update the developed accident models has been proved to be a cumbersome process. This is because safety specialists use to take a long period of time to read and digest the information, as the accident reports are usually very detailed, long and sometimes with a difficult language and structure. A strategy to automatically extract relevant information from report accidents and update model parameters is investigated. A machine-learning tool has been developed and trained on previous expert opinion on several accident reports. The intention is that for each new accident report that is issued, the machine can quickly identify the more relevant features in seconds-instead of waiting for some days for the expert opinion. This way, the model can be more quickly and dynamically updated. An application to the preliminary accident report of the 2018 Lion Air accident is provided to show the feasibility of the machine-learning proposed approach.

ORCID iDs

Morais, C., Yung, K. and Patelli, E. ORCID logoORCID: https://orcid.org/0000-0002-5007-7247; Papadrakakis, M., Papadopoulos, V. and Stefanou, G.
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