Measuring Surface Texture by the Sand-Patch Method

The circular texture (CT) meter is a laser-based device for measuring the mean profile depth (MPD) of pavement at a static location. MPD measurements from the CT meter and mean texture depth measurements from the sand patch test were obtained in five random locations in each of 45 sections of the 2000 National Center for Asphalt Technology (NCAT) test track. The NCAT test track provides a wide range of surface types, including coarse and fine dense graded Superpave® mixes, Hveem mixes, stone mastic asphalt, and Novachip. Testing indicated that the CT meter produced results comparable with the ASTM E965 sand patch test. When open-graded mixtures were excluded, this study indicated that the offset was nonsignificant between CT meter and sand patch test results. Previously developed equations to predict macrotexture were found to be inadequate for the wide range of mix types and aggregate types found at the NCAT test track. An equation was developed to relate fineness modulus to macrotexture. This equation was validated with independent data collected by the Virginia Transportation Research Council. Testing conducted as part of a mini round robin indicated that two readings should be averaged to represent a single CT meter measurement. The within-lab coefficient of variation for the CT meter is estimated to be 2.3%. The between-lab coefficient of variation for the CT meter is estimated to be 4.2%. Both estimates are based on the average of two tests being reported as a single measurement.

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Study of the Sand Patch and Outflow Meter Methods of Pavement Surface Texture Measurement

Surface Texture Versus Skidding: Measurements, Frictional Aspects, and Safety Features of Tire-Pavement Interactions ◽

10.1520/stp39043s ◽

2009 ◽

pp. 42-42-20 ◽

Cited By ~ 1

Author(s):

RN Doty

Keyword(s):

Surface Texture ◽

Texture Measurement ◽

Pavement Surface ◽

Sand Patch

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Measuring surface texture of in-service asphalt pavement: evaluation of two proposed hand-portable methods

Road Materials and Pavement Design ◽

10.1080/14680629.2021.2009902 ◽

2021 ◽

pp. 1-17

Author(s):

Glenn R. Matlack ◽

Andrea Horn ◽

Aldo Aldo ◽

Lubinda F. Walubita ◽

Bhaven Naik ◽

...

Keyword(s):

Surface Texture ◽

Asphalt Pavement ◽

Measuring Surface ◽

Pavement Evaluation

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Comparison of two stylus methods for measuring surface texture

Dental Materials ◽

10.1016/s0109-5641(99)00017-2 ◽

1999 ◽

Vol 15 (2) ◽

pp. 79-86 ◽

Cited By ~ 49

Author(s):

S.A. Whitehead ◽

A.C. Shearer ◽

D.C. Watts ◽

N.H.F. Wilson

Keyword(s):

Surface Texture ◽

Measuring Surface

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COMPARISON OF SURFACE MACROTEXTURE MEASUREMENT METHODS

Journal of Civil Engineering and Management ◽

10.3846/13923730.2013.802732 ◽

2014 ◽

Vol 19 (Supplement_1) ◽

pp. S153-S160 ◽

Cited By ~ 6

Author(s):

Nicholas Fisco ◽

Halil Sezen

Keyword(s):

Portland Cement Concrete ◽

Patch Tests ◽

Depth Data ◽

Measuring Devices ◽

Laser Equipment ◽

Measuring Surface ◽

Main Technique ◽

Profile Depth ◽

The Mean ◽

Sand Patch

Recent advances in technology allowed for the use of laser-based systems that can directly measure macrotexture properties of various surfaces. Volumetric or sand patch method has historically been used as the main technique for measuring macrotexture. Different available methods do not all measure the same surface properties and often generate different measurements. Thus, it is crucial to determine the most suitable method for measuring surface macrotexture. This paper investigates mean profile depth measurements from three laser based macrotexture measuring devices, including a laser profiler, a laser texture scanner and a circular texture meter. The results are compared with mean texture depth obtained from volumetric sand patch tests. Experiments were conducted to measure macrotexture of 26 laboratory specimens, which included asphalt and Portland cement concrete samples of various type and finish, as well as other common manufactured textured samples. Based on the evaluation of experimental data collected in this study, relationships are recommended to predict standard macrotexture using the mean profile depth data measured by a laser equipment or scanner.

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Topography-based flow-directional roughness: potential and challenges

Earth Surface Dynamics ◽

10.5194/esurf-4-343-2016 ◽

2016 ◽

Vol 4 (2) ◽

pp. 343-358 ◽

Cited By ~ 23

Author(s):

Sebastiano Trevisani ◽

Marco Cavalli

Keyword(s):

Surface Roughness ◽

Surface Texture ◽

Flow Direction ◽

Texture Modeling ◽

Digital Terrain ◽

Measuring Surface ◽

Morphological Detail ◽

Gravity Driven ◽

Gravity Driven Flow ◽

Landscape Morphology

Abstract. Surface texture analysis applied to high-resolution digital terrain models (HRDTMs) is a promising approach for extracting useful fine-scale morphological information. Surface roughness, considered here as a synonym of surface texture, can have a discriminant role in the detection of different geomorphic processes and factors. Very often, the local morphology presents, at different scales, anisotropic characteristics that could be taken into account when calculating or measuring surface roughness. The high morphological detail of HRDTMs permits the description of different aspects of surface roughness, beyond an evaluation limited to isotropic measures of surface roughness. The generalization of the concept of roughness implies the need to refer to a family of specific roughness indices capable of capturing specific multiscale and anisotropic aspects of surface morphology. An interesting set of roughness indices is represented by directional measures of roughness that can be meaningful in the context of analyzed and modeled flow processes. Accordingly, we test the application of a flow-oriented directional measure of roughness based on the geostatistical bivariate index MAD (median of absolute directional differences), which is computed considering surface gravity-driven flow direction. MAD is derived from a modification of a variogram and is specifically designed for the geomorphometric analysis of HRDTMs. The presented approach shows the potential impact of considering directionality in the calculation of roughness indices. The results demonstrate that the use of flow-directional roughness can improve geomorphometric modeling (e.g., sediment connectivity and surface texture modeling) and the interpretation of landscape morphology.

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