Optimization of sensor network topology for Structural Health Monitoring applications

Λιάγγου, Θεοδώρα; Liangou, Theodora

dc.contributor.author	Λιάγγου, Θεοδώρα	el
dc.contributor.author	Liangou, Theodora	en
dc.date.accessioned	2022-10-13T09:15:19Z
dc.date.available	2022-10-13T09:15:19Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/55912
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.23610
dc.description	Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Ναυτική και Θαλάσσια Τεχνολογία και Επιστήμη”	el
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	Optimal sensor placement	en
dc.subject	Statistical Pattern recognition	en
dc.subject	Detection Theory	en
dc.subject	Genetic Algorithm	en
dc.subject	Corrosion	en
dc.subject	Βέλτιστη τοποθέτηση αισθητήρων	el
dc.subject	Στατιστική αναγνώριση προτύπων	el
dc.subject	Θεωρία ανίχνευσης	el
dc.subject	Διάβρωση	el
dc.subject	Γενετικός αλγόριθμος	el
dc.title	Optimization of sensor network topology for Structural Health Monitoring applications	en
heal.type	masterThesis
heal.classification	Structural Health Monitoring	en
heal.language	en
heal.access	campus
heal.recordProvider	ntua	el
heal.publicationDate	2022-06-06
heal.abstract	A Structural Health Monitoring (SHM) architecture involves the processing of sensor measurements and their translation to decisions about the structure’s condition. Given a specific SHM approach, the sensor topology, the feature selection, and the employed detector are the main elements that control the detection performance. The present thesis, first, provides an exploratory analysis of the statistical response patterns that govern a structure subjected to var-iable loads and then methodically arrives at an optimal sensor topology, that maximizes the detection performance. For demonstration purposes, a thin square plate subjected to probabilistically described loads is considered. The damage of interest corresponds to a uniform thickness loss, the detection of which is evaluated at different damage levels (from 1% to a 90% unrealistic upper bound). The damage is to be identified indirectly, through strain sensing. The problem is numerically approached (Finite Elements and Monte Carlo Simulations). The generalized Gaussian likelihood ratio test is employed for setting up the detector. The effect of the feature vector arrangement to the detection performance is assessed through estimations of the probability of detection and false alarm, under the Neyman-Pearson framework. The optimal feature vector has been derived through case-based informal (selective process) or formal (Genetic Algorithms) optimization.	en
heal.advisorName	Ανυφαντής, Κωνσταντίνος	el
heal.advisorName	Anyfantis, Konstantinos	el
heal.committeeMemberName	Samouelides, Manolis
heal.committeeMemberName	Papalambrou, George
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ναυπηγών Μηχανολόγων Μηχανικών	el
heal.academicPublisherID	ntua
heal.numberOfPages	78 p.
heal.fullTextAvailability	false