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Abstract The paper analyzes the causes of cracking in marble slabs that are embedded in the large-surface floor of a representative commercial building. In the case of many such objects, these slabs are laid on a concrete substrate and start cracking a short time after the start of operation. The "nature" of the occured cracks is presented, and the results of testing the adhesion between the marble slabs and concrete substrate using non-destructive impulse response and impact-echo methods, the results of strength tests of the marble slab specimens that were taken for laboratory tests, and also the results of visiual inspections of the concrete substrate are provided. These results were then related to the technology of laying marble slabs, and on this basis the causes of cracking were diagnosed while explaining their mechanism. The presented test results and analysis will be useful for people that implement and supervise works related to the execution of stone slab floors, in particular marble floors, with regards to an increase of the quality of such floors and the elimination of the causes of their cracking. Highlights • The paper analyses the causes of cracking in marble slabs, • The slabs were embedded in the floor of a representative commercial building, • The results of testing the adhesion between the slabs and substrate were presented, • The causes of cracking were diagnosed and the mechanism was explained, • The analysis will be useful for engineers that supervise the execution of slab floors. [ABSTRACT FROM AUTHOR]

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]

The use of the artificial neural network (ANN) for the non-destructive identification of the moisture content of saline ceramic bricks is presented in the following article. A database was built based on conducted laboratory tests of solid ceramic bricks using dielectric, resistive and microwave methods. The database consists of parameters determined with the above-mentioned methods and also parameters describing the type and concentration of salts in the bricks. As a result of carried out research and analysis it was proved that neural identification of the moisture content of saline ceramic bricks is possible. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the non-destructive location and identification of cracks in unilaterally accessible massive concrete walls by means of the state-of-the-art acoustic methods of impulse response and ultrasound tomography. An example of the practical verification of the methodology used to investigate the concrete structure of a hydroelectric power plant is provided. [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]

This paper presents a new nondestructive way of identifying the values of pull-off adhesion between the concrete layers in concrete floors. It based on the roughness parameters of the base layer surface, using the nondestructive optical technique, and on the floor surface, using the nondestructive acoustic techniques and employing artificial neural networks (ANNs) for this purpose. The new way has a potential for being widely used in practice, whereby it may become possible to employ previously trained ANNs to identify the pull-off adhesion, without impairing the surface of the tested concrete floor. [ABSTRACT FROM AUTHOR]

The paper deals with the nondestructive identification of defective concrete zones in unilaterally accessible massive members, for example, access galleries in hydroelectric power plants. The concrete in such zones is, for various reasons, excessively porous. The authors propose to use state-of-the-art acoustic testing techniques, including ultrasonic tomography, integratively to detect and identify defective zones. An original methodology for such tests has been developed. The methodology is illustrated with an example of its practical application to a real civil engineering structure. [ABSTRACT FROM PUBLISHER]

Abstract: A new technique of nondestructively assessing the compression strength of concrete, which employs artificial neural networks, is presented. A data set based on results of testing concretes (with a 28-day strength of 24–105MPa) by different nondestructive methods was used to train and test an artificial neural network. The methodology of neural identification of the strength is described. The obtained results, including those of the practical verification of the technique, are reported. [Copyright &y& Elsevier]

In this work, the results of experimental research and numerical analyses considering using non-destructive testing and machine learning algorithms are presented. They were used to evaluate the interlayer bonding between the concrete repair mortar and repaired element. The analyses of using different machine learning algorithms for this purpose are presented and compared. Based on non-destructive and destructive measurements performed in 300 measuring places, the database was built. In order to predict the output, which was the pull-off adhesion, the artificial neural network, support vector machine and random forest algorithm were used. For simplifications of the numerical model as input only a few parameters were selected. Based on the numerical analyses, the most accurate algorithm for non-destructive evaluation of the pull-off adhesion in concrete repaired elements was selected. Its potential for using in construction practice was strengthened by performing experimental verification on separately made model element especially for this purpose. • The usefulness of machine learning algorithms for engineering purposes. • Evaluating pull-off adhesion by means of nondestructive measurements. • Comparative analyses of selected machine learning algorithm and NDT. • Simplified method for investigating the interlayer bonding. [ABSTRACT FROM AUTHOR]

The article presents original complex methodology for the effective control of the entire process of executing floors made of cement-based materials. This methodology has been lacking in literature so far. The methodology was developed on the basis of many years of the authors' experience, which was acquired when diagnosing the technical condition of such floors. The methodology was preceded by a synthetic summary of the most important technological and technical requirements for floors made of cement-based materials. It was also enriched with a discussion of the problem documented by sample research results showing the state that may be the result of disregarding and not performing the necessary control activities. [ABSTRACT FROM AUTHOR]