Ανάλυση μοντέλου ροής γεγονότων συστήματος για την εκτίμηση του ρίσκου σε επιλεγμένες διαδικασίες σε πλοίο

Παπαβασιλείου, Οδυσσεάς-Ευγένιος; Papavasileiou, Odysseas-Evgenios

dc.contributor.author	Παπαβασιλείου, Οδυσσεάς-Ευγένιος	el
dc.contributor.author	Papavasileiou, Odysseas-Evgenios	en
dc.date.accessioned	2025-04-14T09:31:44Z
dc.date.available	2025-04-14T09:31:44Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/61743
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.29439
dc.rights	Default License
dc.subject	Risk Analysis in Drydocking Accidents,	el
dc.title	Ανάλυση μοντέλου ροής γεγονότων συστήματος για την εκτίμηση του ρίσκου σε επιλεγμένες διαδικασίες σε πλοίο	el
heal.type	bachelorThesis
heal.classification	Marine Safety	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-11-01
heal.abstract	This thesis focuses on risk management during ship drydocking, addressing accident risks falls from heights, and fires/explosions. Drydocking is essential for ship maintenance and safety, allowing repairs and inspections that can't occur while the vessel is operational. Despite rigorous safety protocols, shipyards often see accidents due to the complexity and hazards involved in drydocking tasks. Chapter 3 provides an in-depth look at the drydocking process itself, detailing the stages of a typical drydocking operation, such as that of an LPG (Liquified Petroleum Gas) Carrier. The chapter analyzes specific tasks like hull cleaning, machinery repairs, and safety upgrades, explaining how these are coordinated and managed under tight timelines. Chapter 4 explores risk theory and assessment methodologies in the maritime context, particularly as they apply to drydocking. This chapter highlights methods like Fault Tree Analysis (FTA) and Bayesian Networks (BNs), which allow systematic identification and quantification of accident causes. These tools are critical for understanding the likelihood and severity of potential accidents. Chapter 5 presents the core modeling work. Fault trees for falls and fire/explosion risks are constructed and then converted into Bayesian Networks to integrate conditional probabilities, enabling more dynamic risk assessments. Here, models are tested with both static and probabilistic variables to reflect varying conditions, like worker experience or workload. Sensitivity analyses reveal key risk factors, like supervision quality and safe material storage, which substantially affect accident probabilities. Simulations on these models help evaluate different risk scenarios, enhancing decision-making on safety improvements.	en
heal.advisorName	Βεντίκος, Νικόλαος	el
heal.committeeMemberName	Ηλιοπούλου, Ελευθερία	el
heal.committeeMemberName	Θεμελής, Νικόλαος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Μελέτης Πλοίου και Θαλάσσιων Μεταφορών	el
heal.academicPublisherID	ntua
heal.numberOfPages	71 σ.	el
heal.fullTextAvailability	false