Concrete properties evaluation by statistical fusion of NDT techniques
Language
en
Article de revue
This item was published in
Construction and Building Materials. 2012-12-01, vol. 37, p. 943
Elsevier
English Abstract
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 ...Read more >
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.Read less <
English Keywords
Non-destructive testing
Fusion
Concrete durability evaluation
ANN
RSM
NONDESTRUCTIVE EVALUATION
NEURAL-NETWORKS
RADAR
STRENGTH
Origin
Hal imported