Artificial neural networks for non-destructive identification of the interlayer bonding between repair overlay and concrete substrate

Abstract The article presents the application of artificial neural networks (ANNs) for the non-destructive identification of the pull-off adhesion fb 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 fb value of the repair overlay of variable thickness from the repaired element and in a very wide range of identified pull-off adhesion fb 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 fb.

[1]  Emre Sancak,et al.  Prediction of bond strength of lightweight concretes by using artificial neural networks , 2009 .

[2]  Jamal A. Abdalla,et al.  Prediction of FRP-concrete ultimate bond strength using Artificial Neural Network , 2011, 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization.

[3]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[4]  Mehdi Nikoo,et al.  Principal Component Analysis combined with a Self Organization Feature Map to determine the pull-off adhesion between concrete layers , 2015 .

[5]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[6]  Jerzy Hoła,et al.  Evaluation of the height 3D roughness parameters of concrete substrate and the adhesion to epoxy resin , 2016 .

[7]  Paul D. Wilcox,et al.  Ultrasonic techniques for detection of weak adhesion. , 2009 .

[8]  AN Kolmogorov-Smirnov,et al.  Sulla determinazione empírica di uma legge di distribuzione , 1933 .

[9]  S. Czarnecki,et al.  Non-destructive neural identification of the bond between concrete layers in existing elements , 2016 .

[10]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[11]  Mohammad Reza Nikoo,et al.  Hybrid Metaheuristic-Neural Assessment of the Adhesion in Existing Cement Composites , 2017 .

[12]  Jacek Reiner,et al.  Usefulness of 3D surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers , 2015 .

[13]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[14]  Angelo Del Zotto.  Concrete Damage in Field Conditions and Protective Sealer and Coating Systems , 2017 .

[15]  T. Błaszczyński,et al.  Aspects of bond layer role in concrete repairs , 2006 .

[16]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[17]  Mary Sansalone,et al.  Impact-echo : nondestructive evaluation of concrete and masonry , 1997 .

[18]  P. G. Asteris,et al.  Modeling of masonry failure surface under biaxial compressive stress using Neural Networks , 2014 .

[19]  R. Calçada,et al.  Push-Off Tests in the Study of Cyclic Behavior of Interfaces between Concretes Cast at Different Times , 2016 .

[20]  Lukasz Sadowski,et al.  ANN modeling of pull-off adhesion of concrete layers , 2015, Adv. Eng. Softw..

[21]  Ł. Sadowski.  Methodology of the assessment of the interlayer bond in concrete composites using NDT methods , 2018 .

[22]  Zhiye Zhao,et al.  An optimal neural network and concrete strength modeling , 2002 .

[23]  Piervincenzo Rizzo,et al.  Detecting the Presence of High Water-to-Cement Ratio in Concrete Surfaces Using Highly Nonlinear Solitary Waves , 2016 .

[24]  C. Spearman.  The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.

[25]  Uwe Reuter,et al.  A comparative study of machine learning approaches for modeling concrete failure surfaces , 2018, Adv. Eng. Softw..

[26]  Jerzy Hoła,et al.  Pull-off adhesion prediction of variable thick overlay to the substrate , 2018 .