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Non-destructive identification of the pull-off adhesion of a concrete substrate to an overlay mortar with variable thickness using artificial neural networks (ANNs) is studied in this paper. Selected ANNs with various training algorithms were tested on the basis of the parameter which describes the thickness of the overlay and also the parameters specified experimentally using non-destructive testing (NDT) methods. Real world data collected from experiments of pull-off adhesion were used for building our learner models. The tests were carried out in the same place where tests using NDT methods were performed. Three variant analyses of the possibility of such identification were conducted. The variance was calculated for these testing methods and parameters obtained with their usage, without considering the parameter that describes the thickness of the overlay in this work. [ABSTRACT FROM AUTHOR]

The paper presents the results regarding the identification of the value of the pull-off adhesion between a concrete added layer with a constant thickness and a substrate concrete layer in existing elements. A method of identification, which is based solely on the use of artificial neural networks (ANNs) and two non-destructive acoustic methods: impact-echo and impulse response on the surface of the added layer, was developed. The methodology of identifying the pull-off adhesion between a concrete added layer and a substrate layer in existing elements was developed and presented in the paper and is useful in construction practice. [ABSTRACT FROM AUTHOR]

When making and repairing concrete floors it is vital to properly prepare the interlayer bonding surface. The measure of the bond is the value of pull-off adhesion f b experimentally determined in building practice by the semi-destructive (SDT) pull-off method. In this paper it is proposed to assess pull-off adhesion by jointly the optical laser triangulation method and the acoustic impulse response method, using artificial neural networks (ANN), on the basis of a few parameters (independent of top layer thickness) determined by these methods. The proposed non-destructive (NDT) pull-off adhesion assessment method is devoid of the drawbacks and inconveniences of the pull-off method and makes possible the reliable mapping of the adhesion level on the tested surface without local damage to the latter. [ABSTRACT FROM AUTHOR]

The results of studies into the usefulness of 3D surface roughness parameters determined by 3D laser scanning, for the nondestructive evaluation of the pull-off adhesion of concrete layers are presented. Twenty-one 3D roughness parameters were identified. Their correlations with pull-off adhesion f b determined by the semi-nondestructive pull-off method were established. For two of the parameters, i.e. the texture aspect ratio ( Str ) and the peak material volume ( Vmp ) the correlation coefficient was found to be high. The possibility of the nondestructive evaluation of the pull-off adhesion on the basis of solely the existing concrete topography examinations is critically looked at. [ABSTRACT FROM AUTHOR]

• New method of evaluation of the bonding between the repair overlay and concrete element was presented. • The database was created based on laboratory tests of layered concrete elements. • Database was determined using non-destructive methods and pull-off method. • Selected artificial neural network algorithms such as: Levenberg-Marquardt, Broyden-Fletcher-Goldfarb-Shano and Conjugate Gradients were tested. • The new method has been experimentally verified. The article presents the application of artificial neural networks (ANNs) for the non-destructive identification of the pull-off adhesion f b values between the repair overlay with variable thickness and the substrate in concrete surface-repaired elements. For this purpose, a large database was built on the basis of the tests of model concrete elements. The numerical analyses were performed using this data and ANNs with various learning algorithms. Based on these analyses, it was shown that the ANN with the Broyden-Fletcher-Goldfarb-Shanno learning algorithm, with thirty-one input parameters and twenty hidden neurons, is the most useful for identifying the interlayer pull-off adhesion in repaired concrete elements. The reliability of the presented application of ANNs was confirmed on the basis of carried out validation, using a part of the database not used in the learning and testing. The application's reliability was also confirmed on the basis of experimental verification carried out using the results of tests performed on an additional model element made exclusively for this purpose. This is an important and original issue presented in the article. Another novelty presented in the article is the application of ANNs for a much more difficult case, which is the identification of the pull-off adhesion f b value of the repair overlay of variable thickness from the repaired element and in a very wide range of identified pull-off adhesion f b within the range of 0.5–3.60 MPa. Moreover, the unique value of the article is the use for the first time of spatial and function related parameters to describe the concrete surface morphology of a repaired element. The investigation presented in the article has also confirmed the high usefulness of these parameters for identifying the value of pull-off adhesion f b. [ABSTRACT FROM AUTHOR]