PORTO @ 
Archivio Istituzionale della Ricerca
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishAccidents in aviation are rare events. From them, aviation safety management systems take fast and effective remedy actions by performing the analysis of the root causes of accidents, most of them are proved to be human factors. Since the current standard relies on the manual classification performed by trained staff, there are no technical standards already defined for automated human factors identification. This paper considers this issue, proposing machine learning techniques by leveraging on the state-of-the-art technologies of Natural Language Processing. The techniques are then adapted to the SHEL standard accident causality model and tested on a set of real accidents. Computational results show the accuracy and effectiveness of the proposed methodology, which leads to a possible reduction of time and costs up to 30%.
Natural Language Processing for the Identification of Human Factors in Aviation Accidents Causes: An Application to the SHEL Methodology / Perboli, Guido; Gajetti, Marco; Fedorov, Stanislav; Lo Giudice, Simona. - ELETTRONICO. - cirrelt-2020-36(2020), pp. 1-16.
| Titolo: | Natural Language Processing for the Identification of Human Factors in Aviation Accidents Causes: An Application to the SHEL Methodology | |
| Autori: | ||
| Data di pubblicazione: | 2020 | |
| Abstract: | Accidents in aviation are rare events. From them, aviation safety management systems take fast and effective remedy actions by performing the analysis of the root causes of accidents, most of them are proved to be human factors. Since the current standard relies on the manual classification performed by trained staff, there are no technical standards already defined for automated human factors identification. This paper considers this issue, proposing machine learning techniques by leveraging on the state-of-the-art technologies of Natural Language Processing. The techniques are then adapted to the SHEL standard accident causality model and tested on a set of real accidents. Computational results show the accuracy and effectiveness of the proposed methodology, which leads to a possible reduction of time and costs up to 30%. | |
| Appare nelle tipologie: | 5.16 Working paper |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| CIRRELT-2020-36.pdf | Final version of the paper | 1. Preprint / Submitted Version |  | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2846436
Copyright © 2021