Damage identification in simplified geometries utilising machine learning methods: a computational study

Katsoudas, Anastasios; Κατσούδας, Αναστάσιος

dc.contributor.author	Katsoudas, Anastasios	en
dc.contributor.author	Κατσούδας, Αναστάσιος	el
dc.date.accessioned	2022-12-14T11:33:38Z
dc.date.available	2022-12-14T11:33:38Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/56452
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.24150
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Structural Health Monitoring	en
dc.subject	Machine Learning	en
dc.subject	Statistical Pattern Recognition	en
dc.subject	Corrosion Detection	en
dc.subject	Artificial Neural Network	en
dc.subject	Στατιστική Κατηγοριοποίηση	el
dc.subject	Παρακολούθηση υγείας κατασκευών	el
dc.subject	Μηχανική μάθηση	el
dc.subject	Τεχνητό νευρωνικό δίκτυο	el
dc.subject	Εντοπισμός διάβρωσης	el
dc.title	Damage identification in simplified geometries utilising machine learning methods: a computational study	en
dc.title	Αναγνώριση βλαβών σε απλοποιημένες γεωμετρίες με χρήση μεθόδων υπολογιστικής μάθησης: μία υπολογιστική έρευνα	el
heal.type	bachelorThesis
heal.classification	Structural Health Monitoring	en
heal.classification	Παρακολούθηση Υγείας Κατασκευών	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2022-09-20
heal.abstract	In the highly corrosive environment where marine structures operate, the current industry practice is to overcompensate for corrosion induced thickness loss (CITL) and replace any parts that score below specified allowable limits during scheduled maintenance. Damage detection and evaluation are conducted on a planned maintenance scheme. Hence, there would be immediate benefits from the implementation of a structural health monitoring (SHM) system that would allow for predictive, condition-based maintenance. The objective of this study is to investigate and assess the effectiveness of state-of-the-art statistical pattern recognition (SPR) and machine learning (ML) methods in association with alternative sensor grid architectures as a SHM scheme for the detection of CITL. For this purpose, a simple rectangular plate at different corrosion levels (uniform and pitting) was considered as a reference structural element, which was subjected to a stochastic pressure load. Strain response data were produced using Finite Element (FE) simulations and were treated under a probabilistic framework. Elements from detection theory and ML were taken under consideration in order to construct alternative detectors and assess their performance.	en
heal.advisorName	Anyfantis, Konstantinos	en
heal.committeeMemberName	Tsouvalis, Nicholas	en
heal.committeeMemberName	Kaiktsis, Lambros	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών. Τομέας Θαλάσσιων Κατασκευών	el
heal.academicPublisherID	ntua
heal.numberOfPages	84 σ.	el
heal.fullTextAvailability	false