New nondestructive way of identifying the values of pull-off adhesion between concrete layers in floors.
Floors in buildings consist of a top layer (topping) laid on a base layer (base). In the course of laying, using and renovating a floor, defects and damage may arise at the interface between the two layers (Blaszczynski et al. 2006; Lowinska-Kluge, Blaszczynski 2012; Cwirzen et al. 2014; Siewczynska 2012; Stawiski 2012; Srinivas et al. 2013; Olofsson et al. 2005; Trapko, Musial 2011). A major defect, having a significant effect on floor life, is the lack of bond between the topping and the base. This defect is usually due to the improper preparation of the base layer surface.
A tangible measure of this bond is the value of pull-off adhesion f practically determined by the seminon-destructive pull-off method. It is both a qualitative method, since it enables one to detect a defect (no adhesion) at the interface, and a quantitative method, since using it one can determine pull-off adhesion [f.sub.b] It is required of concrete floors that pull-off adhesion [f.sub.b] be not less than 0.5 MPa and that one measurement per 3 [m.sup.2] should be taken (PN-EN 12636:2001). The drawback of the pull-off method is that damage is caused in each test point, as shown in Figure 1, which then needs to be repaired. This is a serious drawback which leads to the practice of reducing the number of test points used for evaluating floor quality. Considering the above, it seems that in building practice there is a demand for a new way of floor testing which does not cause damage to the tested floor surface.
The proper preparation of the base surface, which can be described using surface roughness parameters, has a significant bearing on pull off adhesion f between the concrete layers in floors (Gonzalez-Jorge et al. 2012; Hola et al. 2012; Mathia et al. 2011; Naderi, Ghodousian 2012; Niemczewska-Wojcik et al. 2013; Pawlus et al. 2013; Perez et al. 2009; Santos, Julio 2013; Sezen, Fisco 2013; Stach et al. 2005; Twardowski et al. 2011; Wieczorowski et al. 2010). The proper preparation of the base surface determines the mechanical adhesion and so the interlayer bond. According to literature reports, the concrete base surface roughness parameters determined prior to top layer concreting can be useful in the nondestructive assessment of the bond between the concrete layers.
From literature reports it also appears that non-destructive techniques can be useful in determining the bond between concrete layers (Bungey et al. 2006; Darowicki et al. 2003; Golaski et al. 2012; Gorzelanczyk 2012; Grzelka et al. 2012; Helmerich et al. 2012; Hola, Schabowicz 2010; Maierhofer et al. 2004). First of all, acoustic techniques: the impulse response technique and impact-echo technique can be used for this purpose.
A methodology for the nondestructive assessment of the interlayer bond in concrete floors by means of the above techniques has been developed (Hola et al. 2011). However, this methodology cannot be used to determine pull-off adhesion f Also attempts have been made to determine the correlations between pull-off adhesion f and the particular (individual) parameters determined using the optical technique (Sadowski 2013a) and acoustic techniques (Hola, Sadowski 2012). The attempts have been unsuccessful.
Therefore, a new way of identifying pull-off adhesion values on the basis of a few jointly considered parameters (describing the concrete base layer surface and determined on the surface of the top layer by acoustic techniques) needs to be found.
In order to interrelate a larger number of the parameters it is necessary to employ artificial neural networks (ANNs). In recent years in the literature on the subject one can find numerous documented successful applications of ANNs in building engineering (Chien-Ho 2013; Hola, Schabowicz 2005; Hasanzadehshooiili et al. 2012; Raab et al. 2013; Sadowski 2013b; Song, Yu 2013).
So far there has been no reliable employment of ANNs for evaluating pull-off adhesion f of the concrete layers in floors, on the basis of parameters estimated using nondestructive techniques. An attempt to employ an radial basis function (RBF) artificial neural network was made in Sadowski (2013c), but the obtained values of linear correlation coefficient R were not satisfactory.
1. Description of tests
Tests were carried out on two model test specimens no. 1 and 2, representing 2500*2500 mm concrete floor consisting of a 25 mm thick top layer laid on a 125 mm thick base layer. The base layer was laid on a sheet of underlayment membrane and on a 100 thick layer of sand.
The 25 mm thick top layer was made of grade C20/25 concrete with quartz aggregate of 2 mm maximum grading. The base layer was made of grade C30/37 concrete with concrete mix consistency S3, w/c = 0.5 and crushed basalt aggregate of 8 mm maximum grading.
In order to differentiate the pull-off adhesion at the top/base layer interface the surface of the base layer was prepared in four versions denoted with Roman numerals from I to IV, as shown in Figure 2. Surfaces no. I and II were produced on model test specimen no. 1 while surfaces no. III and IV were produced on model test specimen no. 2. A ready-made compound (in the form of concentrate to be diluted with water) for priming concrete bases for floorings and toppings was used for the bonding layer. The base layer was primed four hours prior to laying the top layer.
Figure 2 shows a scheme of the ways of preparing the base layer surface in conjunction with the investigative nondestructive techniques and the seminondestructive one and the parameters evaluated by them.
Figure 3 shows a sketch of model test specimens no. 1 and 2, the division of the base layer surface according to the way of preparing it and the arrangement of test points. A grid (with 100 x 100 mm mesh size) was marked on the surface of both the base layer and the top layer at a distance of 500 mm from the edge of each of the specimens. The grid columns were denoted with letters from A to H and the rows with successive numbers from 1 to 16 (Sadowski, Hola 2013).
As described in Sadowski and Hola (2013), the top layer was concreted 28 days after the base layer had been concreted and the tests were carried out after 90 days of top layer concrete curing.
In Figure 4 a schematic of the rig for investigating the base layer surface roughness by the optical technique using an optical camera, and the actual testing are presented.
As described in Sadowski and Hola (2013), the investigations of the base layer surface were carried out in the previously marked grid points, by means of 1028*768 pixel resolution camera mounted on a guide. The acquired data were transmitted to software installed on a portable computer. The camera with its lens by default fixed at an angle of 53[degrees], was manually shifted on the guide. The investigations consisted in scanning 50 x 50 mm surface profiles at every 0.1 mm. The scanning time of the test area in each grid point did not exceed 2 seconds. As a result, a 3D image of the concrete surface within the test area would be obtained. The data were processed by the software and the following nine concrete surface roughness parameters: [S.sub.a], [S.sub.q], [S.sub.sk], [S.sub.ku], [S.sub.bi], [S.sub.ci], [S.sub.vi], [S.sub.v] and [S.sub.p] were generated.
The investigations on the top layer surface were carried out using nondestructive acoustic techniques: the impulse response technique and the impact-echo technique.
The impulse response tests were carried out using equipment consisting of a calibrated hammer for exciting an elastic wave by striking the hammer against the surface of the tested material, a geophone and software for data recording and processing (Sadowski, Hola 2013). In Figure 5 a schematic of the rig for impulse-response testing and the actual testing are presented.
The impact-echo tests were carried out using a system consisting of a set of balls with different diameters, used to generate an elastic wave by striking against the surface, a receiving head and a software for recording and processing the data. In Figure 6 a schematic of the rig for impact-echo testing and the actual testing are presented. The tests were carried out in accordance with the recommendations given in Sansalone and Streett (1997).
The following five parameters: [K.sub.d], [N.sub.av], [M.sub.p]/N and v, determined by the impulse response technique, and [f.sub.T] determined using the impact-echo technique, were adopted.
As described in Sadowski and Hola (2013), a schematic of the rig for testing by the seminondestructive pull-off method is shown in Figure 7. The tests were performed in the same points in which the nondestructive tests had been carried out.
Figure 8 shows an example of model test specimen no. 1 after concrete cores had been pulled off from its top layer.
2. Test results
Table 1 shows sample parameter values obtained for model specimens no. 1 and 2 for the surfaces from I to IV. The full data can be found in Sadowski (2012). Then the sets (472) of experimental results were subjected to statistical analyses whereby the database was reduced to 460 sets of results constituting input variables for a neural network.
3. Statistical analyses of test results
In order to select input variables from the experimentally determined parameters, which would be suitable for the input layer of an artificial neural network, statistical analyses of test results were carried out.
For this reason a test of goodness of fit with the normal distribution was performed in 472 test points for all the parameters obtained by the nondestructive and the semi-nondestructive methods. The Shapiro-Wilk test was used for this purpose. According to Shapiro and Wilk (1965), if probability level W of the test statistic falls below the fixed test significance level [W.sub.n] (a), then the hypothesis about the fit with the normal distribution is rejected.
As mentioned in Sadowski (2012), Sadowski and Hola (2013), it often happens in testing practice that one or more results is/are much different from the rest. A doubt arises whether the given result should be taken into account in the analysis of the distribution or whether it should be rejected. For this reason Chauvenet's criterion for eliminating uncertain results is used.
The Shapiro-Wilk test results for the particular parameters listed in Figure 2, after the application of Chauvenet's criterion and Spearman's rank correlation coefficient for the correlations between the particular parameters and the output variable (pull-off adhesion fb), are presented in Table 2.
On the basis of the statistical analyses, the following parameters determined using the three nondestructive techniques were selected as useful for the nondestructive identification of the pull-off adhesion between the concrete layers in floors by means of ANNs:
--the average arithmetic deviation of the tested surface from the reference surface ([S.sub.a]), the surface load-bearing capacity index ([S.sub.bi]) and the mean square deviation of the tested surface from the reference surface ([S.sub.q]), determined using the nondestructive optical technique;
--stiffness [K.sub.d] and average mobility [N.sub.av], determined using the nondestructive impulse-response technique;
--the frequency of ultrasonic wave reflection from the bottom ([f.sub.T]), determined using the nondestructive impact-echo technique.
Also the following parameters determined using two nondestructive techniques were selected as useful for the nondestructive identification of the pull-off adhesion between the concrete layers in floors by means of ANNs on the basis of the statistical analysis:
--the average arithmetic deviation of the tested surface from the reference surface ([S.sub.a]), the surface load-bearing capacity index ([S.sub.bi]) and the mean square deviation of the tested surface from the reference surface ([S.sub.q]), determined using the nondestructive optical technique;
--stiffness [K.sub.d] and average mobility [N.sub.av], determined using the nondestructive impulse-response technique.
The parameter determined using the impact-echo technique was omitted since its value directly depends on the thickness of the tested element.
The parameters were randomly divided into training data, testing data and data for the experimental verification of the artificial neural network. From among the 456 sets of results, 322 sets were adopted for training the ANN. 69 for testing the ANN and 69 for the experimental verification of the ANN.
4. Numerical analyses by means of artificial neural networks
From among the 96 configurations of artificial neural networks A, B and C considered in Sadowski and Hola (2013), network C was selected to be trained and tested for the nondestructive identification of pull-off adhesion [f.sub.b]. It is a unidirectional multilayer error back propagation network with the QUASI-NEWTON algorithm, 10 hidden layer neurons and the tanh hidden layer activation function. The adopted number of epochs was 200. The structure of the ANN is shown in Figure 9. The five parameters experimentally determined using respectively the three nondestructive techniques (Fig. 9a) and the two nondestructive techniques (Fig. 9b) form the ANN's input layer.
The ANN training and testing results are presented in Figures 10 and 11, respectively. It shows that network C accurately maps the training data and precisely identifies the testing data, as evidenced by the situation of the points along the regression line related to the ideal mapping. It is significant that very high linear correlation coefficient R values were obtained for both training and testing, for the data acquired by the three nondestructive techniques: respectively 0.9775 and 0.9725 and by the two nondestructive techniques: respectively 0.8847 and 0. 8492.
5. Results of experimental verification
In order to experimentally verify network C sixty nine sets of results were selected from all the test results. The previously trained and tested neural network was fed the values of parameters [S.sub.a], [S.sub.q], [N.sub.av], [K.sub.d] and [f.sub.T] in each of the 69 randomly selected test points, including: 16 for surface 1, 16 for surface II, 12 for surface III and 25 surface IV.
Figure 12 presents the correlation between experimentally determined pull-off adhesion [f.sub.a, b] identified by network C in the verification. The results indicate that network C correctly identifies the randomly selected verification data, as evidenced by the situation of the points along the regression line related to the ideal mapping and by the high value of correlation coefficient R, equal to 0.9481 for the three nondestructive techniques and 0.8989 for the two nondestructive techniques.
The following conclusions have been drawn from the results of the experimental and numerical studies:
--The pull-off adhesion between the concrete layers in floors can be identified in the new nondestructive way on the basis of the parameters evaluated on the base layer surface, using the nondestructive optical technique, and on the floor surface, using the nondestructive acoustic impulse-response and impact-echo techniques, with data processing by means of an artificial neural network with a proper structure and training algorithm. The unidirectional multilayer error back propagation network with the QUASI-NEWTON algorithm was found to be most suitable for this purpose.
--The values of pull-off adhesion [f.sub.c,b] of the concrete layers in floors can be reliably identified on the basis of the values of parameters: [S.sub.a], [S.sub.q], [K.sub.d], [N.sub.av] and f determined using three nondestructive techniques: the optical technique and the acoustic impulseresponse and impact-echo techniques.
--Pull-off adhesion [f.sub.c,b] between the top layer and the base layer in floors can also be identified on the basis of the values of parameters [S.sub.a], [S.sub.q], [S.sub.bi], [K.sub.d] and [N.sub.av] determined using two nondestructive techniques: the optical technique and one acoustic technique (the impulse-response technique), doing without parameter f dependent on the top layer thickness, determined using the impact-echo technique. However, linear correlation coefficient R, amounting to 0.8847, 0.8492 and 0.8989 for respectively training, testing and verification, is then much lower than when the three nondestructive techniques are used.
--The new way proposed in this paper is not intended to completely replace the currently commonly used identification of the pull-off adhesion between the concrete layers in floors by the semi-nondestructive pull-off method, but represents a new approach to the identification of this adhesion, in which trained and tested ANNs are employed for this purpose.
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Lukasz SADOWSKI, Jerzy HOLA
Faculty of Civil Engineering, Wroclaw University of Technology, pl. Grunwaldzki 11, 50-377 Wroclaw, Poland
Received 09 Jul 2013; accepted 16 Oct 2013
Corresponding author: Lukasz Sadowski
Lukasz SADOWSKI. (PhD, Eng), a Researcher at Wroclaw University of Technology, Poland. Received his PhD in 2013 at Wroclaw University of Technology. He is a Research Assistant at WUT, Institute of Building Engineering. Research interests: concrete (especially concrete floors), non-destructive tests, acoustic techniques and artificial intelligence.
Jerzy HOLA. Full Professor at the Wroclaw University of Technology, Dean of the Faculty of Civil Engineering, Head of the Department of General Building Engineering, and Director of the Accredited Research Laboratory at the Institute of Building Engineering. He is a member of the following organizations among others: Senate of the Wroclaw University of Technology, Council of the Faculty of Civil Engineering, Section of Engineering Construction Materials the Committee of Civil Engineering Polish Academy of Sciences, Committee of Mechanics and Civil Engineering at the Polish Academy of Sciences Wroclaw, Science Committee of PZITB (Polish Association of Building Engineers and Technicians), and the program board of the magazine "Builder".
Table 1. Sample parameter values obtained for surfaces from I to IV of model specimens no. 1 and 2 Name of test method and parameter symbol Number Optical technique of test point [S.sub.a] [S.sub.q] [S.sub.sa] [S.sub.ku] mm mm -- -- I/B10 I 0.500 I 0.267 I -0.478 4.686 I/B11 1 0.737 1 1.033 1 -0.582 3.933 I/B12 0.825 0.730 -0.845 4.509 II/A5 1 0.125 1 0.179 1 -2.280 11.63 II/A6 I 0.121 I 0.166 I -1.763 8.083 II/A7 0.116 0.153 -1.245 4.529 III/E13 0.649 0.753 0.029 3.172 III/E14 0.688 0.853 -0.099 3.220 III/E15 0.728 0.953 -0.226 3.268 IV/C6 1 0.491 1 0.656 1 -0.301 4.404 IV/C7 I 0.457 I 0.612 I -0.121 4.330 IV/C9 0.468 0.598 0.152 3.293 Name of test method and parameter symbol Number Optical technique of test point [S.sub.bi] [S.sub.ci] [S.sub.vi] [S.sub.v] -- -- -- mm I/B10 I 0.040 0.697 I 0.024 I 1.311 I/B11 1 0.048 0.744 1 0.004 1 1.009 I/B12 0.033 0.730 0.012 1.250 II/A5 1 0.019 0.674 1 0.009 1 1.546 II/A6 I 0.010 0.705 I 0.020 I 1.117 II/A7 0.002 0.735 0.031 0.689 III/E13 0.004 0.754 0.030 2.879 III/E14 0.004 0.782 0.017 3.187 III/E15 0.003 0.810 0.004 3.494 IV/C6 1 0.002 0.724 1 0.029 1 3.135 IV/C7 I 0.001 0.726 I 0.025 I 2.963 IV/C9 0.005 0.786 0.018 2.309 Name of test method and parameter symbol Number Optical Impulse response technique of test technique point [S.sub.p] [K.sub.d] [N.sub.av] M[p.sub./]N mm -- m/s'N -- I/B10 0.694 0.017 62.674 I 0.828 I/B11 0.384 0.004 99.276 1 3.449 I/B12 0.489 0.041 57.401 1.746 II/A5 0.316 0.088 449.394 1 0.918 II/A6 0.318 0.082 569.617 I 3.213 II/A7 0.320 0.018 483.924 4.153 III/E13 2.576 0.005 162.676 1.863 III/E14 2.391 0.017 478.359 10.768 III/E15 2.206 0.008 550.975 6.831 IV/C6 2.527 0.009 67.542 1 1.107 IV/C7 2.782 0.013 97.146 I 2.663 IV/C9 2.109 0.019 89.291 0.938 Name of test method and parameter symbol Number Impact - Pull - of test echo off point technique method [nu] fr [f.sub.b] -- kHz MPa I/B10 I 1.116 I 7.500 I 0.970 I/B11 1 0.592 1 7.000 1 0.870 I/B12 0.544 8.000 0.940 II/A5 1 1.235 1 5.000 1 0.610 II/A6 I 0.252 I 4.500 I 0.560 II/A7 4.000 0.510 : III/E13 0.834 3.500 0.360 III/E14 1.270 4.000 0.410 III/E15 1.678 4.500 0.480 IV/C6 1 1.684 1 4.500 1 0.510 IV/C7 I 1.240 I 3.500 I 0.430 IV/C9 0.884 6.500 0.590 Table 2. Shapiro-Wilk test results and Spearman's rank Parameter name W [alpha] [W.sub.n ] ([alpha]) [S.sub.a] 0.957 0.01 0.956 -0.447 [S.sub.q] 0.958 0.01 0.956 -0.446 [S.sub.sk] 0.983 0.01 0.956 -0.116 [S.sub.ku] 0.877 0.01 0.956 -0.052 [S.sub.bi] 0.630 0.01 0.956 0.204 [S.sub.ci] 0.550 0.01 0.956 -0.077 [S.sub.vi] 0.913 0.01 0.956 0.176 [S.sub.v] 0.912 0.01 0.956 -0.037 [S.sub.p] 0.881 0.01 0.956 -0.067 [K.sub.d] 0.839 0.01 0.956 0.416 [N.sub.av] 0.617 0.01 0.956 -0.711 [M.sub.p]/N 0.721 0.01 0.956 -0.395 v 0.751 0.01 0.956 0.333 [f.sub.T] 0.964 0.01 0.956 0.925 [f.sub.b] 0.957 0.01 0.956 --
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|Author:||Sadowski, Lukasz; Hola, Jerzy|
|Publication:||Journal of Civil Engineering and Management|
|Date:||Aug 1, 2014|
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