Μοντελοποίηση ατυχημάτων σύγκρουσης (collision) στις θαλάσσιες μεταφορές με χρήση της μεθόδου Bayesian Networks - Παναγιωτόπουλος, Φαίδων-Αιμίλιος-Νίκος (Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Μελέτης Πλοίου και Θαλάσσιων Μεταφορών, 2015)
Τhis diploma thesis concerns an integrated study of ship collisions based on the influence of the human element and its consisting factors in the accident. Aim of this dissertation is the collision of ships probability estimation and the human factors leading to it. For this reason, a literature review concerning appropriate methods for accident modeling (and other industries) is presented which is the main theoretical part of this thesis. In the English part, we refer to the importance of the human factor in maritime accidents and the need to design human based systems. Firstly, the principles of Human Centered Design for ships are thoroughly examined, in order to elicit the elements that effect human performance. Afterwards the Bayesian Networks method is selected and various versions are created in both a generic and a more detailed level. Then, we begin to build the model in 2 stages: First we make the frame of the model and then we optimize it, adding elements about the human factors from TRACEr method. All the above are described in detail. After the construction of the defining evidence node wherever we want, we see the change of the final probability of collision. Once the construction of the BN, we define the probability tables of all nodes. Five case studies are extracted from database, in order to run the model, extract the results and even improve it. . Each case study is presented, evidences are selected from their narrative and running the BN model based on these evidences conclude in several probability results for performance and collision. A result presentation and a sensitivity analysis follow for all target nodes. In conclusion, factors that have a strong influence on human performance and collision are selected and highlighted for further research for human performance improvement and collision avoidance.