Concrete properties evaluation by statistical fusion of NDT techniques
Idioma
en
Article de revue
Este ítem está publicado en
Construction and Building Materials. 2012-12-01, vol. 37, p. 943
Elsevier
Resumen en inglés
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 ...Leer más >
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.< Leer menos
Palabras clave en inglés
Non-destructive testing
Fusion
Concrete durability evaluation
ANN
RSM
NONDESTRUCTIVE EVALUATION
NEURAL-NETWORKS
RADAR
STRENGTH
Orígen
Importado de HalCentros de investigación