Nov 1, 2012 - one of the most prominent elements in the interior finish of such buildings and one of the principal ... of the scene by a plane of light. For this ...
Czech Society for Nondestructive Testing NDE for Safety / DEFEKTOSKOPIE 2012 October 30 - November 1, 2012 - Seč u Chrudimi - Czech Republic
CONCRETE SURFACE ROUGHNESS TESTING USING NONDESTRUCTIVE THREE-DIMENSIONAL OPTICAL METHOD Jerzy HOŁA1, Łukasz SADOWSKI1, Jacek REINER2, Maciej STANKIEWICZ2 1 Institute of Building Engineering, Wroclaw University of Technology 2 Institute of Machine Technology and Automation, Wroclaw University of Technology
Abstract In building practice the durability of concrete floors is to a large degree determined by the surface layer’s pull-off adhesion to the structural layer. The strength of the bond between the two layers largely depends on the preparation of the structural layer. Structural layer preparation can be described by surface roughness parameters. This paper presents results of concrete floor structural layer surface roughness tests done using 3D optical surface scanning. Two differently prepared structural layers were examined in this way. The results show that this method can be successfully used for this purpose. Key words: concrete, roughness, surface topography, bonding surface
1. Introduction Concrete floors are found in most general and industrial building structures. Owing to their durability they are commonly used in housing and in multi-storey garages. Concrete floors are one of the most prominent elements in the interior finish of such buildings and one of the principal factors having a bearing on the architectural shape of the interiors and the use of the rooms. The durability of concrete floors is to a high degree determined by the pull-off adhesion of the surface layer to the structural layer. It should be noted that the proper preparation of the structural layer, describable by surface roughness parameters, has a significant influence on the mutual adhesion of the bonded layers [1]. Roughness is a surface’s characteristic representing its irregularities (convexities and concavities) the height of which is at least one order of magnitude smaller than the size of the element. It is one of the properties describing a surface’s geometrical structure, i.e. a set of all its irregularities, including shape deviations, waviness and roughness. The latter is regarded to be a major indicator of surface preparation. The profile method is usually used to measure the roughness of concrete surfaces. In this way two-dimensional surface roughness parameters are determined. In [2], concrete floor surface roughness parameters were determined for five different ways of preparing the surface. The roughness of the surface was described using the parameters determined by the profile method. A single flat profile does not describe well enough a surface which has a three-dimensional character. Concrete surfaces undoubtedly belong to this category. Even though roughness
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parameters describing a flat profile are still successfully used in construction [3], recent years have brought a critical re-evaluation of the two-dimensional analysis, and new solutions in its description [4-7]. As a result, the surface method, which enables one to determine roughness parameters in three-dimensionality, has been increasingly used. Functional volumetric parameters can be useful in the evaluation of the roughness of concrete surfaces. Such parameters are determined on the basis of the Abbott-Firestone curve for a given surface [8]. According to [9], the functional volumetric parameters include: peak material volume of scale limited surface , core material volume of scale limited surface , and pit void volume of scale limited surface . core void volume of scale limited surface It should be noted that the Abbott-Firestone curve represents surface topography structure as a cumulative probability density of the amplitudes of its particular points. In the case of the functional parameters, the volumetric curve is an integral over the examined surface. Hence the position of the surface of a section relative to the highest point of the topography is marked on the Y-axis while the percentage of the material relative to the highest surface point is marked on the X-axis. The results of concrete surface testing using the three-dimensional optical method and the volumetric parameters are presented below. 2. Description of the surface roughness testing method The set for the optical testing of surface roughness comprises a laser profile scanner SICK IVC-3D mounted on a linear drive with a guide bar, an encoder and a laptop (fig. 1a). Scanning is effected by manually shifting the head above the measuring area during which several surface profiles, separated from one another by a distance of 0.07 mm, are being recorded with a resolution of 0.074 mm. As a result of the scanning one obtains a cloud of points modelling the topography of the concrete surface with a vertical resolution of 0.015 mm. The data from the intelligent camera after prefiltration are sent to the laptop to be archived and later processed in order to determine the surface roughness parameters. a)
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Fig. 1. View of: a) measuring set used in optical method, b) testing by nondestructive optical method. The method of the optical scanning of surface topography is based on triangulation by means of a line of light [10]. It consists in taking photographs at an angle relative to the direction of lighting (fig. 2a). The photographs depict the deformation of the-line-of-light profile caused by the shape of the illuminated object (fig. 2b). Because of its high concentration of energy,
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a class II/2M VIS laser equipped with a special optical system is used as the illuminator. In the case of the IVC-3D scanner, a low power (λ=658nm±15nm) diode laser was employed. The recorded image of the line contains information about the 3D geometry of a single section of the scene by a plane of light. For this reason this method is referred to as 2½D, as opposed to the methods capable of activating the full geometry of the whole measuring area (e.g. structural light, stereovision, tomography) [11]. Consequently, it is necessary to effect relative movement, which was achieved by means of the linear drive. During the relative shift of the object being scanned the discrete acquisition of photographs, synchronized by the encoder, takes place. Although the camera system offers a maximum scanning speed of 5000 profiles/sec., the actual scanning speed is limited by the exposure parameters and the profile line segmentation algorithms. In the case of the setup presented here manual shift is used instead of a controlled electric drive.
Fig. 1. Schematic of laser triangulation method (a) and of triangulation image (b) (1) – camera with lens, (2) – laser line generator, (B) - concrete, (G) – head, (E) – encoder. In this scanning method only the parts of the scanned surface which are visible from both the camera and laser perspectives are mapped. As the triangulation angle (the angle between the plane of light and the camera’s optical axis) decreases so does the number of invisible areas. But also the measurement resolution along the Z-axis decreases as a result. In the case of the IVC-3D scanner, both the lens and the triangulation angle (53°) have been set by the manufacturer to strike a balance between occlusion and resolution. The scanning system has been pre-calibrated and ensures constant resolution ∆z=0.015mm along the Z-axis. For the adopted measuring area of 50mm×50mm the following average resolutions are achieved: ● 0.07mm – along the shift (X-axis), ● 0.074mm – along the laser line (Y-axis), ● 0.015mm – height (Z-axis). The time of scanning the investigated area did not exceed 2 sec. The measurement data were exported in the csv format to be further processed by the Talymap 3D Analysis Software.
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3. Test results Tests were carried out on two concrete 50×50mm surfaces constituting the structural layer of a concrete floor. The surfaces were made of C30/37 grade, S3 consistency concrete with ratio w/c=0.5 and a maximum aggregate grading of 8 mm. They matured in a natural way in a laboratory at an ambient temperature of +18°C (±3°C) and a relative air humidity of 60%. For the first 7 days they were stored covered with plastic wrap. The following two ways of concrete surface preparation were used: - surface I: mechanical grinding and dust removal, - surface II: no surface preparation, i.e. the surface was left in the after-concreting condition. Figure 1 shows spatial images (in false colours) of the scanned concrete surface for surface I and II. 0
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Fig. 2. Graphic analysis of volumetric parameters for surface I. 104
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Fig. 3. Graphic analysis of volumetric parameters for surface II.
It appears from the graphic analyses of the volumetric parameters for surfaces I and II, shown in figs 2 and 3, that there are differences in the roughness parameters. For surface I the values of Vmp and Vmc are three times lower than for surface II, whereas the values of parameter Vvc are twice higher for surface II than for surface I, indicating greater surface irregularities of the concrete surface without preparation. It seems that parameter Vv is less useful for the analysis of concrete floor roughness since it does not reveal any differences between surface I and surface II. 4. Conclusion The results of surface roughness tests for a concrete surface constituting the structural layer of a concrete floor, carried out using the 3D optical method have been presented. Two concrete surfaces differing in the preparation of the structural layer were tested. The tests showed that the parameters: Vmp, Vmc and Vvc are particularly useful for evaluating the degree of roughness of the tested concrete surfaces. The proposed optical surface scanning method and the SICK IVC-3D sensor used, ensured a resolution sufficient for measuring surface roughness of concrete screeds without any noticeable occlusion or speckle effects or disturbances caused by a change in reflectiveness, as noted in [12]. In the designed test setup the motor drive of the head was abandoned and owing to the high scanning speed ensured by the manual shift no profiles were lost. The parallelism between the scanned concrete surface plane and the scanner shift plane was not a critical acquisition parameter and it was corrected (through levelling) at the data processing stage.
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5. References 1. Siewczyńska M.: Method for determining the parameters of surface roughness by usage of a 3D scanner, Archives of Civil and Mechanical Engineering, 1-2, p. 83-89 (2012). 2. Franck A, De Belie N.: Concrete floor-bovine claw contact pressures related to floor roughness and deformation of the claw, Journal of Dairy Science, 89, 8 (2006). 3. Erdem S., Dawson A., Thom N.: Impact load-induced micro-structural damage and micro-structure associated mechanical response of concrete made with different surface roughness and porosity aggregates, Cement and Concrete Research, 42, s. 291– 305 (2012). 4. Grzelka M., Chajda J., Budzik G., Gessner A., Wieczorowski M., Staniek R., Gapiński B., Koteras R., Krasicki P., Marciniak L.: Optical coordinate scanners applied for the inspection of large scale housings produced in foundry technology, Archives of Foundry Engineering, 49/1, 255-260 (2010). 5. Mathia T., Pawlus P., Wieczorowski M.: Recent trends in surface metrology, Wear, 271, s. 494–508 (2011). 6. Grzelka M., Majchrowski R., Sadowski Ł.: Investigations of concrete surface roughness by means of 3D scanner, Proceedings of Electrotechnical Institute, 16 (2011). 7. Gonzalez-Jorge H., Solla M., Armesto J., Arias P.: Novel method to determine laser scanner accuracy for applications in civil engineering, Optica Applicata, Vol. XLII, No. 1 (2012). 8. Abbott J., Firestone F.: Specifying surface quality: a method based on accurate measurement and comparison, Mechanical Engineering, 55, p. 569–572 (1933). 9. ISO 25178: Geometric Product Specifications (GPS) – Surface texture: areal. 10. Kowal J., Sioma A., Active vision system for 3D product inspection: Learn how to construct three-dimensional vision applications by reviewing the measurements procedures, Control Engineering USA; ISSN 0010-8049. vol. 56, pp. 46-48 (2009). 11. Tutsch R., Petz M., and Fischer M., Optical three-dimensional metrology with structured illumination, Optical Engineering, vol. 50, no. 10, p. 101507 (2011). 12. Reiner J., Stankiewicz M., and Wojcik M., Predictive segmentation method for 3D inspection accuracy and robustness improvement, in Optical Measurement Systems for Industrial Inspection VI, Peter H. Lehmann, Editors, 73890A, Munich (2009). Acknowledgements This research was carried out as part of research project “Grant Plus” [POKL 8.2.2] co-funded within the framework of the European Social Fund.
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