Non-destructive evaluation of the pull-off adhesion of concrete floor layers using rbf neural network

Abstract

The interlayer bond is one of the primary qualities assessed during an inspection of floor concrete workmanship. The measure of this bond is the value of pull-off adhesion f _b determined in practice by the pull-off method. The drawback of this method is that the tested floor is damaged in each of the test points and then needs to be repaired. This drawback can be overcome by developing a way which will make it possible to test floors in any point without damaging them locally. In this paper it is proposed to evaluate the pull-off adhesion of the top layer to the base layer in concrete floors by means of the radial basis function (RBF) artificial neural network using the parameters evaluated by the non-destructive acoustic impulse response technique and the non-destructive optical laser triangulation method. Presented RBF neural network model is useful tool in the non-destructive evaluation of the pull-off adhesion of concrete floor layers without the need to damage the top layer fragment from the base.

Keyword : artificial intelligence, concrete floor, interlayer bond, acoustic methods, impulse response technique, surface roughness