A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Τριάντης, Δήμος Α.	el
dc.contributor.author	Σταύρακας, Ηλίας	el
dc.contributor.author	Στεργιόπουλος, Χαράλαμπος Χ.	el
dc.date.accessioned	2015-05-16T10:24:48Z
dc.date.available	2015-05-16T10:24:48Z
dc.date.issued	2015-05-16
dc.identifier.uri	http://hdl.handle.net/11400/10505
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.elsevier.com/	en
dc.subject	Nondestructive testing
dc.subject	Compressive strength
dc.subject	Cement
dc.subject	Pressure stimulated currents
dc.subject	Micro cracks
dc.subject	Neural networks
dc.subject	Radial basis functions
dc.subject	Fuzzy means
dc.subject	Μη καταστροφικές δοκιμές
dc.subject	Αντοχή συμπίεσης
dc.subject	Τσιμέντο
dc.subject	Ρεύματα που ενεργοποιούνται με την πίεση
dc.subject	Μικρορωγμές
dc.subject	Νευρωνικά δίκτυα
dc.subject	Συνάρτηση ακτινικής βάσης
dc.subject	Ασαφής μέσα
dc.title	A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85092221
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.identifier.secondary	DOI: 10.1016/j.conbuildmat.2011.11.036
heal.language	en
heal.access	campus
heal.publicationDate	2012-05
heal.bibliographicCitation	ALEXANDRIDIS, A.P., TRIANTIS, D.A., STAVRAKAS, I. & STERGIOPOULOS, C.C. (2012). A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals. Construction and Building Materials. [Online] 30. p. 294-300. Available from: http://www.elsevier.com/[Accessed 30/12/2011]	en
heal.abstract	This paper presents a non-destructive method for predicting the compressive strength of cement-based materials by studying the appearance of weak electrical signals at specimens that are under mechanical stress. A series of lab experiments have been conducted in order to record the pressure-stimulated electrical signals in cement mortar specimens. Selected signal characteristics were correlated with the ultimate compressive strength of each specimen through the use of a neural network, employing a special training algorithm that offers increased predictive abilities. Results showed that the ultimate compressive strength can be successfully predicted without destroying the specimen.	en
heal.publisher	Elsevier	en
heal.journalName	Construction and Building Materials	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true