Panel Surface Analysis

The surface of a given pavement is very important when considering the overall performance of the pavement. Since the pavement surface is what interacts with the tires of the vehicles using the pavement, it can have a significant effect on the safety and comfort of the road users. The surface changes tire-pavement interactions, which govern noise, friction, and ride quality.

The description of a pavement’s surface is generally made with respect to its texture, which is generally broken down into three distinct categories: microtexture, macrotexture, and megatexture.

These texture categories are defined by ranges in wavelength () and amplitude (A), and each have effects on different parts of the tire-pavement interaction.

Microtexture ( < 0.5 mm, 1 m < A < 500 m) refers to the texture at a smaller-than-visible scale.

It is generally associated with the properties of the aggregate used in the pavement construction.

Macrotexture (0.5 mm <  < 50 mm, 0.1 mm < A < 20 mm) refers to the texture that can be easily seen on the pavement surface. The size and distribution of aggregate particles will affect the

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macrotexture, and in concrete pavements the macrotexture is often applied to the pavement by tining, burlap dragging, broom finishing, or diamond grinding.

Megatexture (50 mm <  < 500 mm, 0.1 mm < A < 50 mm) is on the same order of magnitude as the tire itself and is often manifested as deteriorations in the pavement (potholes, cracking, rutting, etc.). (Hall, Smith, & Littleton, 2008)

Pavement surfaces can be reasonably represented by a series of sine curves with different wavelengths and amplitudes. Each of these sine curves fall into one of the three categories for a scale that is meaningful for a pavement surface analysis. Figure 6.41 illustrates a simplified pavement surface as made up by sine curves of varying wavelengths and amplitudes. While the amplitudes and wavelengths are not to scale, the combination of different amplitudes and wavelengths to make up a pavement surface are illustrated.

Figure 6.41: Simplified pavement surface as combination of different sine waves

When an actual pavement surface is measured, a Fourier transform on this data can provide wavelength distribution information for the pavement surface that effectively decomposes the actual pavement surface into its component sine waves. (Sayers & Karamihas, 1998)

In the case of the PCIP trial section, the surface textures can be generally associated with the following considerations:

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• Microtexture: aggregate proportions and types, broom finish applied to the concrete surface following initial finishing

• Macrotexture: longitudinal tining, with a depth of 3 mm to 5 mm at 19 mm spacing was applied to the surface following the broom finish

• Megatexture: the joints provide some initial megatexture following installation, at a spacing of 4.66 m (panel length) and an amplitude defined by the vertical differential between adjacent panels, which was specified to be no greater than 3 mm.

Each of the three texture categories effect the tire-pavement interaction in different ways. When roads are dry, the microtexture has a significant effect on the frictional properties, while macrotexture plays a larger role when roads are wet, especially when vehicles are travelling at high speeds. Macrotexture and megatexture tend to influence the ride quality and noise characteristics of the pavement (Hall, Smith, & Littleton, 2008). In concrete pavements, one function of the macrotexture, specifically tining, is to remove water from the tire-pavement interface by providing a reservoir-like space below the riding surface and sometimes channelling water off of the pavement surface. Proper cross slope drainage ultimately plays the most significant role in removing water from the riding surface.

Therefore, when evaluating a new pavement type such as the PCIP, the pavement surface properties should be considered and evaluated. This section describes the surface evaluations that were undertaken on the PCIP trial section, including frictional properties testing, texture scanning, and surface roughness testing.

Due to the nature and location of the PCIP trial, the access to the pavement surface was limited.

The testing of the various aspects of the pavement surface were generally done during over night highway closures, in conjunction with other scheduled tests, such as the Falling Weight Deflectometer. For this reason, the timing of the testing was limited and selected areas of the trial were focused on. Specifically the right wheel path and centre of the panels were the focus of this chapter, where relatively high and relatively low traffic loading, respectively, is expected. The differences found between the two areas provide insight into the effects of the traffic-related abrasion on the panels.

179 6.4.1 Surface Texture Measurement

Pavement surface texture can be measured using several direct and indirect methods, including the sand patch method, the outflow meter, and the circular track meter (Liu, 2015). One common method is the mean profile depth (MPD), which calculates the average depth of the surface’s macrotexture based on a 2-dimensional profile of a 100 mm section (ASTM International, 2015).

The 2-dimensional profile can be measured using spot laser technology that measures the surface height along the longitudinal direction of the pavement. The MPD has been found to correlate well with wet pavement frictional properties, however it is only an approximation of the 3-dimensional surface texture, and cannot represent tire-pavement interaction well (Liu, 2015).

For this reason, Liu (2015) developed a set of 3-dimensional texture indicators that could be obtained using laser line scanning technology. Similar to the laser spot technology, the line laser moves parallel to the pavement’s longitudinal direction measuring surface height, however because the scanner spreads laser light across a defined width, the transverse direction is also measured during the laser sweep.

The method involves the development of a 3-dimensional texture height map using the output of a line laser scanner. The laser measures a patch of pavement of Length: 102 mm by Width: 100 mm, though the length of this section can be increased to 254 mm if required. The pavement surface measured is normalized based on the mean height and liner slopes of the various profiles measured within the scanned section. The texture map is then decomposed using discrete wavelets to provide indices based on the amplitudes and volumes of the macrotexture components. The indices developed by Liu are outlined in Table 6.7.

The volumetric indices related to the pavement texture are defined according to the bearing area curve of the texture, as shown in Figure 6.42. Using the measured texture, the curve is determined based on the normalized heights throughout the sample as a cumulative distribution. The peak material volume, or the highest 10%, is generally the first contact of the tire and wears away first.

The core volumes, or the middle 70%, represent the bulk of the texture and provide insight into the texture’s longevity. The valley void is the lowest region, and represents space available for water accumulate beneath the tire-pavement interface.

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Table 6.7: 3-Dimensional Pavement Texture Indices (Liu, 2015)

Symbol Description Type Characterize

SMTD Simulated Mean Texture Depth (mm), simulates sand patch method based on highest measured elevation

Texture amplitude

Macrotexture

Sq Root Mean Square Deviation (mm), summed Std.

Dev. of the differences between texture height and mean texture height

Texture amplitude

Macrotexture

Ssk Skewness, measurement of probability distribution’s asymmetry

Texture amplitude

Macrotexture

Sku Kurtosis, statistical measure describing the width of distribution of texture components around the mean

Texture amplitude

Macrotexture

Vmp Peak Material Volume (mL/m²), volume of material in top 10% of bearing area curve (see Figure 6.42)

Material Volume

Macrotexture

Vmc Core Material Volume (mL/m²), volume of material between 10% and 80% of bearing area curve (see Figure 6.42)

Material Volume

Macrotexture

Vvc Core Void Volume (mL/m²), volume of voids between 10% and 80% of bearing area curve (see Figure 6.42)

Material Volume

Macrotexture

Vvv Valley Void volume (mL/m²), volume of voids in lowest 20% of bearing area curve (see Figure 6.42)

Material