SBARTAÏ, Zoubir Mehdi; LAURENS, S.; ELACHACHI, Sidi Mohammed; PAYAN, Cedric

doi:10.1016/j.conbuildmat.2012.09.064

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SBARTAÏ, Zoubir Mehdi
GCE

LAURENS, S.
Laboratoire Matériaux et Durabilité des constructions [LMDC]

ELACHACHI, Sidi Mohammed
GCE

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Construction and Building Materials. 2012-12-01, vol. 37, p. 943

Elsevier

Résumé en anglais

Measurements from Non-Destructive Testing (NDT) techniques are affected in different ways by concrete properties such as porosity, complexity of the pore network, water content, strength, etc. Therefore, extracting one concrete property from one NDT measurement appears to result in uncertainties. This highlights the benefit of NDT data fusion to evaluate accurately concrete properties. In this paper, NDT measurements from GPR, electrical resistivity and ultrasonic pulse velocity were combined to predict more accurately concrete properties such as strength and water content. Two techniques of data fusion were used namely Response Surface Method (RSM) and artificial neural networks (ANN). The results obtained show the effectiveness of the statistical modeling to predict the properties of concretes by fusion of NDT measurements. In the context of this study, the performances of the two techniques of fusion appear relevant in terms of water content and concrete strength prediction. ANN models exhibit better predictive ability than RSM ones.< Réduire

Mots clés en anglais

Non-destructive testing

Fusion

Concrete durability evaluation

ANN

RSM

NONDESTRUCTIVE EVALUATION

NEURAL-NETWORKS

RADAR

STRENGTH

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Concrete properties evaluation by statistical fusion of NDT techniques

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