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

 
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2012 (EN)
A neural network approach for compressive strength prediction in cement-based materials through the study of pressure-stimulated electrical signals (EN)

Σταύρακας, Ηλίας (EL)
Αλεξανδρίδης, Αλέξανδρος Π. (EL)
Στεργιόπουλος, Χαράλαμπος Χ. (EL)
Τριάντης, Δήμος Α. (EL)

N/A (EN)

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)

journalArticle

Νευρωνικά δίκτυα (EN)
Τσιμέντο (EN)
Radial basis functions (EN)
Συνάρτηση ακτινικής βάσης (EN)
Nondestructive testing (EN)
Μη καταστροφικές δοκιμές (EN)
Compressive strength (EN)
Neural networks (EN)
Μικρορωγμές (EN)
Cement (EN)
Αντοχή συμπίεσης (EN)
Pressure stimulated currents (EN)
Fuzzy means (EN)
Micro cracks (EN)
Ρεύματα που ενεργοποιούνται με την πίεση (EN)
Ασαφής μέσα (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

Construction and Building Materials (EN)

English

2012-05

DOI: 10.1016/j.conbuildmat.2011.11.036

Elsevier (EN)



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