Random fields and Bayesian methods for the uncertainty quantification of hull girder ultimate strength and their impact on the reliability of ship structures in ultimate limit state

Γεωργιάδης, Δημήτρης; Georgiadis, Dimitris

dc.contributor.author	Γεωργιάδης, Δημήτρης	el
dc.contributor.author	Georgiadis, Dimitris	en
dc.date.accessioned	2022-10-03T07:52:38Z
dc.date.available	2022-10-03T07:52:38Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/55824
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.23522
dc.rights	Default License
dc.subject	Bayesian analysis	en
dc.subject	Random fields	el
dc.subject	Ultimate limit state	el
dc.subject	Reliability analysis	el
dc.subject	Ship structures	el
dc.title	Random fields and Bayesian methods for the uncertainty quantification of hull girder ultimate strength and their impact on the reliability of ship structures in ultimate limit state	en
dc.contributor.department	Marine Structures	el
heal.type	doctoralThesis
heal.classification	Marine Structures	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2022-05-02
heal.abstract	The structural reliability assessment of ships is a field of particular interest and importance. Since the 1980s where the foundations of structural reliability analysis and probabilistic-based formats for ship structural design were laid, there have been significant advancements. Nowadays, reliability analysis provides a robust tool for the development of design codes and the assessment of existing vessels. Typically, the safety level and the design of ship structures is governed by their performance in extreme or ultimate limit state. The rational treatment and quantification of the uncertainties associated with the load and resistance models is meaningful for a proper reliability analysis. In this thesis, the focus is placed on developing new methods for the management of uncertainties associated with the hull girder ultimate strength assessment of ocean-going vessels. Broadly speaking, this topic is addressed here by: (i) introducing the tools for a better representation of input uncertain parameters, (ii) propagating the relative uncertainties through the model effectively using Monte Carlo simulation and surrogate modelling techniques (neural networks), and (iii) using observed data to reduce the uncertainties considered in the model by means of a Bayesian analysis. Random field theory is used to describe spatial variability on geometric characteristics of hull structures. Ships are subject to manufacturing procedures and in-service deterioration processes which are generally random in space and time. A new stochastic imperfection model is introduced for the representation of the imperfect geometry of steel plates. In addition, the impact of uneven thickness distribution of stiffened plate panels on the ultimate strength calculation is investigated here for the first time. Bayesian methods offer a formal way to combine systematically different types of information and update model predictions as soon as new observations come into light. In the context of this thesis, Bayesian techniques are developed for: (i) the reduction of uncertainties related to modelling aspects arising from the assumptions and methods of analysis used to calculate the hull girder ultimate strength of ships, and (ii) the improvement of corrosion predictions on a vessel-specific basis by incorporating the information acquired from inspections data into existing global-based corrosion models. The impact of the aforementioned novelties on the structural reliability of oil tankers and container ships in ultimate limit state condition is finally examined. In particular, the reliability assessment and updating of an oil tanker is examined conditional on inspections data. Moreover, the reliability of two container ships at a given point in time is evaluated using the proposed modifications on the model uncertainty factor associated with hull girder ultimate strength prediction.	en
heal.advisorName	Σαμουηλίδης, Εμμανουήλ	en
heal.advisorName	Samuelides, Manolis	en
heal.committeeMemberName	Papadrakakis, Manolis
heal.committeeMemberName	Papadopoulos, Vissarion
heal.committeeMemberName	Anyfantis, Konstantinos
heal.committeeMemberName	Straub, Daniel
heal.committeeMemberName	Ventikos, Nikos
heal.committeeMemberName	Tsouvalis, Nikos
heal.committeeMemberName	Samuelides, Manolis
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	248
heal.fullTextAvailability	false