Maximum likelihood estimation of probabilistically described loads in beam structures

Τσιώτας-Νιαχοπέτρος, Ανδρέας; Tsiotas-Niachopetros, Andreas

dc.contributor.author	Τσιώτας-Νιαχοπέτρος, Ανδρέας	el
dc.contributor.author	Tsiotas-Niachopetros, Andreas	en
dc.date.accessioned	2024-09-06T08:06:49Z
dc.date.available	2024-09-06T08:06:49Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/60161
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.27857
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Genetic algorithms	en
dc.subject	Parameter Estimation	en
dc.subject	Structural Health Monitoring	en
dc.subject	Probabilistic Inversion	en
dc.subject	Maximum Likelihood Estimation	en
dc.subject	Γενετικοί Αλγόριθμοι	el
dc.subject	Εκτίμηση Παραμέτρων	el
dc.subject	Πιθανολογική Αντιστροφή	el
dc.subject	Παρακολούθηση δομικής ακεραιότητας κατασκευών	el
dc.subject	Εκτίμηση Μέγιστης Πιθανοφάνειας	el
dc.title	Maximum likelihood estimation of probabilistically described loads in beam structures	en
heal.type	bachelorThesis
heal.classification	Probabilistic engineering	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-09-19
heal.abstract	In recent years, the maritime market has shifted its focus on improving the reliability of marine structures. In naval engineering, structures frequently are submitted to loads of probabilistic nature and a concentrated effort has been invested into the creation of frameworks capable to identify the corresponding variability. A successful effort towards this direction could greatly contribute towards the field of predictive maintenance, so the current study could be placed in the wider field of Structural Health Monitoring (SHM). Unfortunately, this field contains a multitude of applications where the inferences regarding loading input’s aleatory variability cannot be made directly, since for the majority of real life systems the loading conditions are unknown and unobservable. To the above phenomenon an extra layer of difficulty is added if one considers the epistemic uncertainty governing the potential system, that further complicates the estimation of the unknown parameters. Researchers have to address the aforementioned issues by processing obtainable response measurements. In the current thesis measurements from a C-channel beam are examined both for an intact beam and one after the introduction of a circular discontinuity. The purpose is to identify the parameters [μ,σ] of four normal distributions governing the magnitudes and application points of two applied loads. The load identification process is performed by maximizing a marginalized version of the Likelihood function using Genetic Algorithms.	en
heal.advisorName	Anyfantis, Konstantinos	en
heal.committeeMemberName	Tsouvalis, Nicholas	en
heal.committeeMemberName	Ventikos, Nikolaos	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Θαλάσσιων Κατασκευών	el
heal.academicPublisherID	ntua
heal.numberOfPages	95 σ.	el
heal.fullTextAvailability	false