Usefulness of 3D surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers

Construction and Building Materials 84 (2015) 111–120

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Construction and Building Materials journal homepage: www.elsevier.com/locate/conbuildmat

Usefulness of 3D surface roughness parameters for nondestructive evaluation of pull-off adhesion of concrete layers Jerzy Hoła a, Łukasz Sadowski a,⇑, Jacek Reiner b, Sebastian Stach c a

Faculty of Civil Engineering, Wroclaw University of Technology, Poland Faculty of Mechanical Engineering, Wroclaw University of Technology, Poland c Institute of Informatics, University of Silesia in Katowice, Poland b

h i g h l i g h t s  Usefulness of 3D surface roughness parameters for the evaluation of the pull-off adhesion was presented.  Twenty-one 3D roughness parameters were identified.  Correlations between 3D roughness parameters and pull-off adhesion were established.  The values of Spearman’s rank correlation coefficient have confirmed the established correlations.  It is advisable to support 3D laser scanning with other non-destructive methods.

a r t i c l e

i n f o

Article history: Received 8 November 2014 Received in revised form 23 February 2015 Accepted 4 March 2015

Keywords: Concrete layers Surface topography Morphology Nondestructive tests Optical methods Laser scanning 3D roughness parameters Pull-off adhesion Correlation

a b s t r a c t 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 fb 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. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The pull-off adhesion of the concrete layer added to the existing concrete is critical for the durability of layered concrete structures [1–7]. Besides material factors, the way in which the surface of the existing concrete layer is prepared has a significant bearing on this durability [8], influencing the development of surface geometric structure, described by surface roughness parameters. Contact profilometry and recently non-contact optical scanning are used to measure the parameters. Non-contact scanning methods include: ⇑ Corresponding author at: Faculty of Civil Engineering, Wroclaw University of Technology, Wybrzeze Wyspian´skiego 27, 50-370 Wrocław, Poland. Tel.: +48 505 901 069. E-mail address: [email protected] (Ł. Sadowski). http://dx.doi.org/10.1016/j.conbuildmat.2015.03.014 0950-0618/Ó 2015 Elsevier Ltd. All rights reserved.

the structured light method and the triangulation method [9], confocal methods, the chromatic aberration method, interferometry, the time-of-flight method, the photometric stereo method and laser scanning [10]. Using the above methods one can determine many parameters of the investigated surface through point, linear or surface measurements (the scanning speed depends on the type of measurement). Since many parameters characterizing the surface of concrete can be determined using nondestructive laser scanning methods, the authors decided to check if any of the 3D surface roughness parameters determined in this way can be directly used to evaluate the pull-off adhesion of concrete layers. The adverb ‘‘directly’’ means that there is a correlation between a parameter and pull-off adhesion fb. If this proved to be true, it would be possible to

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6 x 417 mm = 2500 mm

11

417 mm

6

417 mm

1

417 mm

PS

417 mm

LS

417 mm 417 mm

10 x 250 mm = 2500 mm

100 mm 125 mm 40 mm

Added layer Existing concrete substrate Insulating sheet

` x

Foamed polystyrene

-50 x 50 mm surfaces investigated by 3D laser scanning

Fig. 1. Schematic of test element with arrangement of surfaces investigated by 3D laser scanning.

reliably nondestructively determine this adhesion on the basis of solely the existing concrete layer surface topography examinations without it being necessary to carry out arduous semi-nondestructive pull-off tests on the surface of the added concrete layer [11,12]. Thus the aim of this research was to check if there is a correlation between any of the roughness parameters (describing the development of the geometric structure of the existing concrete layer surface) determined by 3D laser scanning and pull-off adhesion fb determined through semi-nondestructive tests carried out on the surface of the added concrete layer.

2. Survey of literature The degree of preparation of concrete surfaces has been usually determined using. two-dimensional (2D) roughness parameters such as: average roughness Ra, mean peak-to-valley height Rz, etc. The 2D roughness parameters of the existing concrete surface determined by the profile method are used to investigate the influence of the existing concrete surface topography on pull-off adhesion [13,14]. Also the authors of work [15] showed that it is possible to adopt roughness parameters such as maximum valley depth Rv, total roughness height Ry and maximum peak-to-valley height Rmax and correlate them with bond strength. Only profile-

Fig. 2. Schematic of adopted laser triangulation system.

J. Hoła et al. / Construction and Building Materials 84 (2015) 111–120

based, i.e. 2D parameters were used in the presented applications. For this purpose a specifically developed non-destructive fast-laser roughness analyzer was used to characterize the 2D roughness parameters of the concrete substrate [16]. According to [7] the influence of surface roughness parameters, stemming from the treatment applied to the concrete surface, on adhesion is equivocal and it can depend on the strength of the existing concrete [17] and the composition of the concrete mixture, particularly on the type and grading of the aggregate [18]. On the other hand, the studies presented in [19] show that increasing the compressive strength of the added concrete relative to the compressive strength of the existing concrete substrate improves the bond strength. The studies presented in [20] show that an increase in surface roughness is beneficial in the case of highstrength concretes. Also the authors of [21,22] showed that higher bond strength between the existing layer and the added concrete layer is achieved by increasing the surface roughness of the concrete substrate through surface treatment. All the other factors affecting the bond between new and old concrete are listed in [23]. Exemplary analyzes of the influence of the ratio of a rough surface to its projection on a plane (RS) [24,25] were presented in [20]. In the last two decades numerous attempts have been undertaken to evaluate roughness parameters in 3D [24–28]. In order to limit the increasing number of such parameters and to standardize their use standard ISO 25178 was developed [29]. Following this development the number of available 3D measurement methods also increased [30–35]. In the authors’ opinion there was a need to develop a 3D laser scanner to obtain 3D roughness parameters. Another problem is data filtering. According to [32], filtering presents some advantages by separating texture into roughness and waviness. On the other hand, filtering also makes results dependable on the choice of the filter and of the cut-off length. It is concluded in [32,37] that if filtering could be avoided, the characterization of concrete surfaces would be much easier, faster and simpler to perform. There are few examples of the evaluation of concrete surface roughness through 3D roughness parameters in accordance with standard [29]. The exception are the studies presented in [36], where unprepared and sandblasted concrete surfaces were investigated using vertical scanning interferometry (VSI), and study [20] in which surface imaging by means of a 3D scanner was used. In [38,39] investigations of the surface roughness of the load-bearing concrete layer in a concrete floor were presented. A 3D scanner was used in the investigations and 3D surface roughness parameters were determined.

(a)

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In recent years laser scanning is increasingly commonly used to measure concrete surface roughness [40,41]. A multifractal surface analysis and the use of a laser scanner for examining concrete surfaces were presented in [42,43]. The evaluation of the quality of concrete surfaces by means of vision systems is based on spatial intensity images [44] or range images [16]. Intensity images carry information about mainly the absorptivity of the surface, which is used for crack detection, deformation measurements or monitoring, but topographic features can be brought out through proper illumination. The structured light (triangulation) methods and the photometry stereo method are key for the vision scanning of surface topography [45]. It should be noted that in the literature on the subject there are few examples of the application of the laser triangulation methods to the testing of the roughness of concrete surfaces. Concrete surface topography investigations led to the identification of interlayer adhesion fb. In [46,47] an attempt was undertaken to determine correlations between pull-off adhesion fb and the particular (single) roughness parameters belonging to height parameters and functional parameters, such as: arithmetical mean height of the surface Sa, root mean square height of the surface Sq or surface bearing index Sbi. However, very low values of determination coefficient r2 characterizing the correlations were obtained. The strength of the correlations was increased owing to the extension of the space of characteristics through the addition of other parameters determined by nondestructive methods and their processing by means of artificial neural networks, proposed in [48–51]. The preliminary studies presented in [39] indicated that volume parameters: peak material volume Vmp, core material volume Vmc and core void volume Vvc could be useful for the evaluation of concrete surface topography. However, no documented applications of the 3D roughness parameters to the evaluation of the pull-off adhesion (fb) of concrete layers can be found in the literature. 3. Investigations of concrete surface roughness by means of 3D laser scanning 3.1. Description of investigated surfaces Forty 50  50 mm concrete surfaces located on a large concrete element representing a 125 mm thick existing concrete substrate layer laid on a 100 mm thick layer of foamed polystyrene were investigated. The concrete element was made of grade C30/37 concrete with concrete mix consistency S3, w/c = 0.5 and the crushed basalt aggregate maximum grading of 8 mm. The constituents of this concrete were (/m3) 340 kg of type II:42.5 Portland cement,

(b)

Fig. 3. View of: (a) test setup for investigating existing concrete substrate surface roughness by 3D laser scanning using laser triangulation scanner, (b) surface scanning process.

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Fig. 4. Exemplary virtual models of existing concrete substrate: (a) surface LS, (b) surface PS.

Table 1 Sample statistical characteristics of 3D roughness parameters obtained by 3D optical scanning. Parameter symbol

Name and value of characteristic LS

PS

Average

Standard deviation

Variation coefficient [%]

Average

Standard deviation

Variation coefficient [%]

Height parameters Sq [mm] Ssk [] Sku [] Sp [mm] Sv [mm] Sz [mm] Sa [mm]

0.718 0.651 4.923 3.654 2.150 1.504 0.559

0.200 0.925 4.884 1.209 0.748 0.461 0.173

27.86 142.09 99.21 33.09 34.79 30.65 30.95

0.253 0.786 4.240 0.665 1.269 0.604 0.198

0.085 0.544 1.495 0.180 0.456 0.276 0.071

33.60 69.21 35.26 27.07 35.93 45.69 35.86

Functional parameters Smr [%] Smc [mm] Sxp [mm]

0.003 0.927 1.154

0.001 0.274 0.497

33.33 29.56 43.07

0.002 0.277 0.608

0.002 0.123 0.221

100.00 44.40 36.35

Spatial parameters Sal [mm] Str [] Std [°]

3.694 0.555 66.119

1.549 0.191 82.200

41.93 34.41 124.32

3.971 0.472 64.845

2.483 0.164 71.370

62.53 34.75 110.06

Hybrid parameters Sdq [] Sdr [%]

2.653 156.165

1.035 99.865

39.01 63.95

0.488 10.362

0.097 3.336

19.88 32.19

0.045 0.973 0.045 0.598 0.910 0.063

0.015 0.279 0.015 0.216 0.266 0.030

33.33 28.67 33.33 36.12 29.23 47.62

0.008 0.294 0.008 0.225 0.256 0.037

0.005 0.111 0.005 0.086 0.106 0.014

62.50 37.76 62.50 38.22 41.41 37.84

Volume parameters Vm [mm3/mm2] Vv [mm3/mm2] Vmp [mm3/mm2] Vmc [mm3/mm2] Vvc [mm3/mm2] Vvv [mm3/mm2]

2.22 l of a polycarboxylate superplasticizer, 168 l of water, 724 kg of sand with 1.53 fineness modulus, 1087 kg of crushed basalt aggregates with 4.51 fineness modulus and 40 kg of fly ash. The investigated member was cured naturally at an air temperature of +18 °C (±3 °C) and an air relative humidity of 60% (±5%), except that for the first seven days it was kept under PVC sheeting. The test element surface was divided into two halves (Fig. 1), which were prepared differently, i.e. – one half was left as after concreting (surface LS),

– the other half was subjected to mechanical grinding with dust removal (surface PS). From among the forty 50  50 mm concrete surfaces twenty were on surface LS and twenty on surface PS. 3.2. Description of 3D laser scanning A specially developed 3D triangulation scanner was used for the 3D scanning of the forty 50  50 mm concrete surfaces. The

J. Hoła et al. / Construction and Building Materials 84 (2015) 111–120 Table 2 Shapiro–Wilk test results. Parameter name

W

a

Wn (a)

LS Height parameters Sq [mm] Ssk [] Sku [] Sp [mm] Sv [mm] Sz [mm] Sa [mm]

W

a

Wn (a)

PS

0.962 0.733 0.391 0.917 0.968 0.951 0.939

0.01 0.01 0.01 0.01 0.01 0.01 0.01

0.956 0.956 0.956 0.956 0.956 0.956 0.956

0.915 0.955 0.928 0.963 0.931 0.966 0.879

0.01 0.01 0.01 0.01 0.01 0.01 0.01

0.956 0.956 0.956 0.956 0.956 0.956 0.956

Functional parameters Smr [%] 0.802 Smc [mm] 0.947 Sxp [mm] 0.973

0.01 0.01 0.01

0.956 0.956 0.956

0.553 0.908 0.827

0.01 0.01 0.01

0.956 0.956 0.956

Spatial parameters Sal [mm] Str [] Std [°]

0.628 0.958 0.467

0.01 0.01 0.01

0.956 0.956 0.956

0.806 0.963 0.784

0.01 0.01 0.01

0.956 0.956 0.956

Hybrid parameters Sdq [] Sdr [%]

0.972 0.965

0.01 0.01

0.956 0.956

0.981 0.967

0.01 0.01

0.956 0.956

Volume parameters Vm [mm3/mm2] Vv [mm3/mm2] Vmp [mm3/mm2] Vmc [mm3/mm2] Vvc [mm3/mm2] Vvv [mm3/mm2]

0.873 0.957 0.959 0.873 0.951 0.934

0.01 0.01 0.01 0.01 0.01 0.01

0.956 0.956 0.956 0.956 0.956 0.956

0.814 0.974 0.957 0.814 0.834 0.787

0.01 0.01 0.01 0.01 0.01 0.01

0.956 0.956 0.956 0.956 0.956 0.956

possibilities of investigating the morphology of concrete surfaces by means of this scanner for the purposes of the nondestructive identification of the pull-off adhesion of concrete layers in layered building elements and the advantages of the latter over the other methods were highlighted in [26]. It should be explained that the laser triangulation method is based on light sectioning, determining the position of a light point or line profile observed at a certain angle relative to the direction of light projection [52]. There are three main acquisition geometries [53]: (a) the camera axis is consistent with the surface normal, (b) the light plane is consistent with the surface normal, or (c) the camera axis and the light plane are angular to the surface. In general, the larger the angle (a) between the light incidence direction and the observation direction, the higher the sensitivity (s) of the method (Fig. 2). However, in the case of structures with high height profile dynamics, illumination or image occlusion occurs whereby there is no measurement information in the occluded areas. The range is calculated as follows [45]:

r¼B

b0 tan a  s b0 þ s tan a

ð1Þ

where: b0 – the distance of the sensor from the optical center, B – the distance of the light plane from the optical center, s – the position of the profile on the sensor. Considering the high energy densities and the precise adjustment of spatial energy distribution, typically a diode laser generator with a Powel lens is used. One range profile is calculated on the basis of one image. For the whole 3D surface topography a series of images is obtained through a relative shift of the object and the acquisition system by distance y (Fig. 3). The uncertainty of the laser triangulation scanner depends mainly on the accuracy of the light profile calculation and on the calibration of the system. The light line center in the image is determined using segmentation algorithms, e.g. thresholding

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methods or center of gravity, etc. Since the methods offer subpixel accuracy, the optical parameters of the lenses (distortions, aberrations and MTF) must be taken into account. Moreover, the imagining of the light profile strongly depends on the optical properties of the surface (scattering, absorptivity, surface normal variations). The cloud of points obtained as a result of segmentation is represented in image coordinate space and so it is necessary to transform it into a global coordinate system. Because the range calculation is non-linear and depends on the acquisition geometry, a calibration matrix, in form of a homography matrix, for the whole measurement range is needed. In order to obtain it, a range of measurement patterns (e.g. a saw-tooth pattern) is applied. For topography data acquisition a laser triangulation scanner was developed. To obtain a field of view width of 50 mm and lateral resolution ResX = 0.01 mm the IVC-3D smart camera was implemented. The camera includes a CMOS sensor with 2048 pixels, red diode laser k = 658 nm and microcontroller for image segmentation, with the maximum performance of 5000 3D profiles/ s. The triangulation angle (r = 53°) and the surface distance influence vertical resolution, which was set to ResZ = 0.01 mm. Longitudinal scanning was done manually using a linear guide integrated with an incremental encoder, ensuring the triggering of individual acquisition with resolution ResY = 0.01 mm. It takes less than 5 s to scan the test surface area of 50  50 mm, depending on the manual movement speed. The obtained images were processed, segmented and transformed to the global coordinate system by the embedded camera algorithms. The homography calibration matrix was obtained with a south-shape target and the uncertainty of calibration was experimentally proved not to exceed 0,01 mm. The scanning results, in the form of a cloud of points, were sent via the Ethernet interface to a computer for further processing. The 3D laser scanner’s main advantages are its speed, achieved owing to the fitting of the scanning parameters to the measurement requirements, and its mobility (less than 5 kg). The scanning field corresponds to the one tested by the pull-off method in order to semi-nondestructively identify pull-off adhesion fb of concrete layers. It has been shown to be suitable for tests aimed at identifying pull-off adhesion fb of concrete layers in layered members [48– 51]. The scanner generates a measurement raster with assigned heights of the surface points. The data matrix is saved in the ‘‘⁄.csv’’ text format. Thus the scanning result is a 3D virtual model of the examined surface topography, which is subsequently visualized or analyzed in external software packages to acquire the values of the parameters describing its topography. The main goal in developing the 3D laser scanner was its portability and the possibility of using it in situ. The equipment needs to be connected to a laptop with external software packages to assess scanning results. As mentioned in the literature survey, filtering can be avoided and the surface texture can be characterized with only primary parameters. This is why no filter was used here to analyze the surface topography data. In order to avoid the error of placing different samples under the scanner the data were levelled. Exemplary virtual images of the untreated existing concrete substrate surface (LS) and the existing concrete substrate surface after grinding (PS) are shown in Fig. 4. Then the surface and volume parameters of roughness were determined in accordance with standard ISO 25178 [29] implemented in the MountainsMap [54]. 3.3. Test results This section presents the results of tests and statistical analyzes of the volume parameters of 3D roughness obtained on the existing concrete substrate surface (surfaces LS and PS) by 3D laser

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(a)

LS 6

1

6

11

Steel disc

Core drilled in added layer

PSBL 16

Cut in added layer

Added layer made from cement mortar

Existing concrete substrate

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125

7

2

6 x 417 mm = 2500 mm

25

1

PS

LSBL 16 11

Fb

(b)

10 x 250 mm = 2500 mm

Insulating sheet `

(c)

(d)

100 mm 125 mm 40 mm

Added layer Existing concrete substrate

Insulating sheet

`

Foamed plystyrene

- points in which pull-off tests were carried out Fig. 5. View of: (a) schematic of test element with arrangement of points in which pull-off tests were carried out, (b) experimental setup for pull-off adhesion tests, (c) servomotor, (d) added layer after test.

scanning in accordance with ISO 25178. Their statistical characteristics are shown in Table 1. 3.4. Statistical analysis A test of goodness of fit with the normal distribution was carried out for the 3D parameters determined in 40 test points. For this purpose the Shapiro–Wilk test, in which the results are ordered in a non-decreasing sequence and then a test statistic is built, was used. According to [55], if probability level W of this test statistic falls below fixed test significance level Wn (a), the hypothesis on the goodness of fit with the normal distribution is rejected:

P n=2 W¼

i¼1 ai:n ðxniþ1  xi Þ

var x

2 ;

ð2Þ

where: ai:n – tabulated coefficients, xi – the value of variable x in point i. It appears from Table 2 that for the assumed significance level

a = 0.01 the hypothesis on the goodness of fit of the height and functional parameters with the normal distribution should be rejected. This supports the thesis put forward in [46,47], where the attempt to determine correlations between pull-off adhesion fb and the individual (single) roughness parameters belonging to the group of height parameters and functional parameters failed.

The hypothesis on the goodness of fit with the normal distribution should be rejected for volume parameters Vm, Vmc and Vm. Therefore the parameters belonging to the spatial, hybrid and volume groups, for which the hypothesis on the goodness of fit of their distribution with the normal distribution was adopted, i.e. Str, Sdq, Sdr, Vv and Vmp, were selected for the further analysis of the correlation with pull-off adhesion fb.

4. Pull-off adhesion tests 4.1. Description of tests After the existing concrete substrate surface roughness tests were carried out on ten LS surfaces and ten PS surfaces, but before the added layer was laid, a bonding coat was put on to increase pull-off adhesion fb at the interface between the layers. The surfaces were denoted LSBL and PSBL (Fig. 5b), respectively. A ready-made product for priming concrete substrates for added layers, in the form of concentrate to be diluted with water was used for the bonding coat. The surface of the existing concrete substrate was primed 4 h before laying the added layer. The 25 mm thick added layer was made of C20/25 class cement mortar and quartz aggregate with a maximum grading of 2 mm. The added layer was produced using commercially available cement mortar weber.floor 1000 Optiroc 1000 [56]. Semi-nondestructive pull-off tests were carried out on the surface of this layer after 28 days since concreting (Fig. 5). The tests were carried out in 40 test points

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J. Hoła et al. / Construction and Building Materials 84 (2015) 111–120 Table 3 Experimentally determined added layer pull-off adhesion to differently prepared existing concrete substrate layer surfaces. Point number

Pull-off adhesion fb [MPa]

Standard deviation [MPa]

Variation coefficient [%]

LS 1 2 3 4 5 6 7 8 9 10

0.51 0.61 0.43 0.46 0.48 0.51 0.48 0.41 0.53 0.61

0.02 0.03 0.02 0.01 0.01 0.02 0.02 0.01 0.02 0.02

3.92 4.92 4.65 2.17 2.08 3.92 4.17 2.44 3.77 3.28

LSBL 11 12 13 14 15 16 17 18 19 20

0.71 0.94 0.87 0.79 0.87 0.64 0.99 1.02 0.74 0.61

0.03 0.04 0.04 0.03 0.04 0.03 0.04 0.04 0.03 0.02

4.23 4.26 4.60 3.80 4.60 4.69 4.04 3.92 4.05 3.28

PS 21 22 23 24 25 26 27 28 29 30

0.38 0.51 0.64 0.56 0.61 0.56 0.51 0.36 0.43 0.41

0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.01 0.01 0.02

2.63 1.96 3.13 3.57 4.92 3.57 3.92 2.78 2.33 4.88

PSBL 31 32 33 34 35 36 37 38 39 40

0.89 0.76 0.74 0.82 0.94 0.99 1.07 1.02 1.07 1.10

0.03 0.02 0.03 0.04 0.04 0.04 0.05 0.04 0.05 0.05

3.37 2.63 4.05 4.88 4.26 4.04 4.67 3.92 4.67 4.55

fb ¼

ð3Þ

The values of the pull-off adhesion of the added layer to differently prepared existing concrete substrate layer surfaces, determined by the semi-nondestructive pull-off method are presented in Table 3. The semi-nondestructive pull-off tests yielded the following values of pull-off adhesion fb: 0.41–0.61 MPa for surface LS, 0.61– 1.02 MPa for surface LSBL, 0.36–0.64 MPa for surface PS and 0.74–1.10 MPa for surface PSBL. The highest values of pull-off adhesion fb were recorded for surface PSBL and the lowest for surface LS. The bonding coat increases interlayer adhesion, especially in the case of the surface subjected to grinding. 5. Correlation analyzes of results This section presents correlation analyzes of the test results. Str, Sdq, Sdr, Vv and Vmp from the spatial, hybrid and volume parameters were selected (on the basis of the statistics presented in Section 3) for the further correlation analysis. In order to check their correlation with pull-off adhesion, their correlation with the linear correlation coefficient and Spearman’s rank correlation coefficient (making the analysis of small-sample data possible) was determined. Variables with unknown distributions, i.e. other than the normal distribution, could be analyzed thanks to the use of Spearman’s rank correlation. 5.1. Analysis of correlation by means of linear correlation coefficient The calculated linear correlation coefficients R are presented in Table 4. The parameters for which the coefficient was higher than 0.7 were used in the further analysis. It appears from Table 4 that parameters Str and Vmp are characterized by the highest linear correlation coefficients R. Nonparametric statistics are used here since the size of the database is small. Such statistics can be used only when it is certain that a given variable is subject to the normal distribution and there is no way of unequivocally verifying them using the linear correlation coefficient because of the small sample size. The correlation coefficient is usually calculated to express the

Table 5 Calculated values of Spearman’s rank correlation coefficient qs.

Table 4 Calculated linear correlation coefficients R.

Spatial parameters Str

4  Fb

p  D2f

4.2. Test results

located on the surfaces on which surface topography scanning had been performed. Pull-off tests consist in drilling cores 50 mm in diameter in the added layer to ensure a consistent bond area and help reduce variations in the test results. Then the test disc is attached to the added concrete surface using an appropriate

Parameter name

adhesive. While pulling the disc with the added concrete layer off the existing concrete substrate layer surface by means of a special servomotor pull-off force Fb is recorded [11]. The tension force is applied to the disc at a steady load rate of 0.05 MPa/s. Knowing the average diameter Df of the pulled off disc one gets the value of pull-off adhesion fb according to [12] from relation (3):

Parameter name

Pull-off adhesion fb depending on surface preparation LS

LSBL

PS

PSBL

0.78

0.88

0.74

0.85

Hybrid parameters Sdq Sdr

0.42 0.40

0.22 0.49

0.36 0.24

0.48 0.36

Volume parameters Vv Vmp

0.31 0.83

0.37 0.86

0.38 0.85

0.27 0.79

Pull-off adhesion fb depending on surface preparation LS

LSBL

PS

PSBL

Spatial parameters Str

0.734

0.455

0.443

0.486

Hybrid parameters Sdq Sdr

0.055 0.055

0.279 0.094

0.146 0.024

0.437 0.291

0.159 0.430

0.064 0.473

0.268 0.441

0.182 0.485

Volume parameters Vv Vmp

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J. Hoła et al. / Construction and Building Materials 84 (2015) 111–120

(b)

1,1 1,0

fb

0,2

0,3

0,4

0,5

0,6

0,7

0,8 0,7

0,5 0,2

0,8

(d)

1,05

0,55

1,00

fb [MPa]

1,10

0,50 0,45 0,40

0,75

Str Str

0,6

0,7

0,8

0,8

0,9

0,85 0,80

0,5

0,7

0,90

0,30 0,4

0,6 Str [-]

0,95

0,35

0,3

0,5

1,15

0,60

0,2

0,4

Str

0,65

0,25 0,1

0,3

[-]

0,70

fb [MPa] fb

0,9

0,6

Str Str

(c)

1,2

fb [MPa]

0,66 0,64 0,62 0,60 0,58 0,56 0,54 0,52 0,50 0,48 0,46 0,44 0,42 0,40 0,38 0,36 0,1

fb

fb [MPa] fb

(a)

0,70 0,35

0,40

0,45

0,50

Str

[-]

0,55

0,60

0,65

0,70

0,75

[-]Str

Fig. 6. Correlation between pull-off adhesion fb determined by pull-off method and parameter Str for surfaces: (a) LS, (b) LSBL, (c) PS, (d) PSBL.

correlation between two variables. The nonparametric equivalent is Spearman’s rank correlation coefficient.

5.2. Analysis of correlation by means of Spearman’s rank correlation coefficient As a rank method Spearman’s rank correlation coefficient qs is only slightly sensitive to divergent observations, whereby it is particularly useful in the analysis of data the distribution of which does not conform to the normal distribution. Spearman’s rank correlation coefficient qs depends on only the ordering of the observed values and it can be applied to any variables whose values can be arranged in an increasing sequence. According to [57], the Spearman rank correlation of random variables x and y is expressed by the formula:

qs ¼ corr  ðf x ðxÞ; f y ðyÞÞ;

ð4Þ

where: corr – the Pearson correlation coefficient, f x ðxÞ – the distribution function of variable x in point x, f y ðyÞ – the distribution function of y in point y. Spearman’s rank correlation coefficient qs and tests of its significance can be used for any distribution of compared variables. The coefficient assumes values from 1 to 1. In the case of an analysis performed by artificial neural networks, experimental data are considered to be useful when Spearman’s rank correlation coefficient qs is in a range from 1 to 0.4 and from 0.4 to 1 [57].

The calculated values of Spearman’s rank coefficient for the correlation between the particular parameters and the output variable, i.e. pull-off adhesion fb, are presented in Table 5. It appears from Table 5 that Spearman’s rank correlation coefficient qs is in a range of 0.4–1 for parameters Str and Vmp, assuming the highest value (0.734) in the case of parameter Str for surface LS. A positive value of Spearman’s rank correlation coefficient means an increase in the values of parameter Str and Vmp with increasing pull-off adhesion fb. 5.3. Discussion of results This section presents a discussion of the obtained high values of linear correlation coefficient R and Spearman’s rank correlation coefficient for two roughness parameters Str and Vmp determined by 3D optical scanning and parameter fb determined by the seminondestructive pull-off method. Special attention is paid to the physical sense of the established correlations. The correlations between the above parameters and pull-off adhesion fb are shown graphically in Figs 6 and 7. Parameter Str (the texture aspect ratio) for which a correlation with pull-off adhesion fb was determined characterizes the directionality (isotropy) of a surface. When Str is close to 0, the surface is anisotropic, whereas when it assumes the maximum value of 1 (100%), the surface is isotropic. When the maximum grading of the added layer aggregate is much lower than that of the existing concrete substrate layer aggregate, which is thc case here, this correlation can have a physical sense, whereas it is difficult to generalize and physically substantiate the correlation between this parameter and pull-off adhesion fb for other configurations.

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0,66 0,64 0,62 0,60 0,58 0,56 0,54 0,52 0,50 0,48 0,46 0,44 0,42 0,40 0,38 0,36 0,02

1,2

(b)

1,1 1,0 0,9

fb

fb [MPa]

fb

fb [MPa]

(a)

0,8 0,7 0,6

0,03

0,04

0,05

0,06

0,07

0,5 0,02

0,08

0,03

0,04

Vmp3

1,10

0,60

1,05

0,55

1,00

fb

0,50 0,45 0,40

0,75

Vmp 3

0,03

0,85

0,30 0,008

0,025

0,90

0,80

0,006

0,09

0,95

0,35

0,004

0,08

1,15

0,65

fb [MPa]

fb

fb [MPa]

(d)

0,002

0,07

Vmp [mm3/mm2]

0,70

0,25 0,000

0,06 Vmp

Vmp [mm /mm2]

(c)

0,05

0,010

0,01

0,70 -0,005

0,000

0,005

0,010

0,015

Vmp

2

Vmp [mm /mm ]

3

0,020

2

Vmp [mm /mm ]

Fig. 7. Correlation between pull-off adhesion fb determined by pull-off method and parameter Vmp for surfaces: (a) LS, (b) LSBL, (c) PS, (d) PSBL.

0 0

10

30

40

50

60

70

80

90

100 %

10.0000 % 80.0000 %

0.25 0.5

20

Vmp

volumes which are removed during the interaction between the surfaces [58]. Therefore one should also critically view the interpretation of the correlation between Vmp and fb since this correlation has not been physically substantiated.

0.75

6. Conclusion

1 1.25

Vmc

Vvc

1.5 1.75 2 2.25

Vvv

2.5 2.75

mm Fig. 8. Graphic analysis of volume parameters for concrete surface.

The other parameter obtained from the statistical analysis, i.e. Vmp (peak material volume), is graphically represented by the Abbot–Firestone curve (Fig. 8). The parameter expresses a volume of the rises above the cut-off plane determined through an analysis of the volume components. This means that parameter Vmp characterizes the material

The usefulness of 3D roughness parameters for the nondestructive evaluation of the pull-off adhesion of concrete layers in a layered member by means of 3D laser scanning has been evaluated. From among the twenty-one 3D roughness parameters identified by laser scanning five were selected on the basis of statistical analyzes and correlations with pull-off adhesion fb determined by semi-nondestructive pull-off tests. The highest linear correlation coefficient R, amounting to 0.7, was obtained for two parameters: the texture aspect ratio (Str) and the peak material volume (Vmp). The calculated values of Spearman’s rank correlation coefficient confirmed the established correlations. However, the values are too low to definitely conclude that it is possible to determine pull-off adhesion fb on the basis of solely the existing concrete substrate layer surface topography examinations. The authors have doubts whether the correlations can be generalized to other concrete surfaces than the ones investigated as part of this research. The analyzed 3D roughness parameters (Str and Vmp) may prove useless for the regression modeling of pull-off adhesion fb of concrete layers in other test situations since it is difficult to physically substantiate their correlation with pull-off adhesion fb. Therefore, in the authors’ opinion, it is advisable to support 3D laser scanning

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