Protocols for Network-Level Macrotexture Measurement (2021) / Chapter Skim
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From page 64... ...
64 This chapter discusses the analysis, results, and main findings of each of the three equipment comparison field experiments. All data from walking-speed and high-speed devices were treated with the adaptive outlier removal routine as described by Katicha et al.
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Data Analysis 65 4.1 Repeatability and Reproducibility The repeatability and reproducibility of most of the available devices were determined using the data collected in the first experiment at the Virginia Smart Road and verified with the data collected at MnROAD. 4.1.1 Methodology To overcome the limitations of other forms of device comparison (i.e., analysis of variance [ANOVA]
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From page 66... ...
66 Protocols for Network-Level Macrotexture Measurement be deemed sufficient for measuring macrotexture, as this is within the resolution required to delineate between investigatory and intervention levels, which is often given in tenths of a millimeter. The repeatability coefficient is derived from the device's mean square error (MSE)
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Data Analysis 67 For the walking-speed devices, m = 3 runs per section; therefore, f1 = f2 = 0.67. For comparison of static devices, Sc is simplified to just SD.
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From page 68... ...
68 Protocols for Network-Level Macrotexture Measurement 95% of measurements will be within the range of 0.798 to 0.802 mm based on these results. Average values represent how a road agency may gather and summarize data for a network of roads in their jurisdiction.
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From page 69... ...
Data Analysis 69 middle of the traffic pattern for the test wheel of the HVS. This location was most similar to the conditions in which the device would be used in the field.
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70 Protocols for Network-Level Macrotexture Measurement Note that Device 12 (a single-spot device) was rotated 90° on some surfaces (denoted by a "T")
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Data Analysis 71 (a) LOA, Device 6 and Device 12 (longitudinal orientation)
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72 Protocols for Network-Level Macrotexture Measurement Figure 42. Mean MPDs for walking-speed devices, calculated for each pavement section tested.
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Data Analysis 73 repeatability for all devices tested fell within a similar range (from 0.063 mm to 0.088 mm) , meaning that measurements on pavement types similar to those tested by the devices used will differ by no more than the repeatability coefficient (i.e., 0.063 mm)
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From page 74... ...
74 Protocols for Network-Level Macrotexture Measurement low MPDs, and Asphalt Section K (an open-graded friction course) had relatively high MPDs reported by all devices.
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Data Analysis 75 (a) Device Pair (Device 1 and Device 2)
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76 Protocols for Network-Level Macrotexture Measurement Texture Type All Random * Longitudinal Only *
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Data Analysis 77 (a)
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78 Protocols for Network-Level Macrotexture Measurement One concern with performing network-level macrotexture data collection is that uncontrolled factors (e.g., vehicle wander and a driver's ability to reproduce a vehicle's path) will distort the data.
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From page 79... ...
Data Analysis 79 values for the various sections, devices, and runs. It is clear, again, that the line lasers do not produce accurate texture measurements on the longitudinally textured concrete, as highlighted in the figure.
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From page 80... ...
80 Protocols for Network-Level Macrotexture Measurement repeatedly tested without moving the device. When the device was picked up and replaced (to replicate field conditions)
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From page 81... ...
Data Analysis 81 footprint was oriented perpendicular to the direction of measurement, with a width of approximately 50 mm at the height the laser was set. Displacement readings were collected at 0.1 mm intervals in the transverse and longitudinal directions.
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82 Protocols for Network-Level Macrotexture Measurement Figure 48. Example 3D plot of LAPS data from OGFC section.
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Data Analysis 83 Comparison of Single-Spot Lasers Figure 51 compares the CIs of the average MPD differences determined from data collected with the two SS lasers on the HMA and PCC sections. The horizontal axis in each figure identifies the combination of test variables (surface type, test speed, and exposure setting)
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84 Protocols for Network-Level Macrotexture Measurement (a) HMA sections (b)
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Data Analysis 85 (a) HMA sections (b)
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86 Protocols for Network-Level Macrotexture Measurement On the longitudinally grooved concrete section, the differences between SSIT and SSIA MPDs were not statistically significant in 11 of the 12 comparisons made. In all cases, the average MPD differences were within ±0.1 mm.
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From page 87... ...
Data Analysis 87 differences between LAPS and Acuity MPDs vary over a narrower range compared to the differences between LAPS and Optocator MPDs, highlighting the benefits of the higher-frequency laser. The following observations were noted from the results presented: • In general, the Acuity MPDs showed better agreement with the corresponding LAPS indices than the Optocator MPDs.
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From page 88... ...
88 Protocols for Network-Level Macrotexture Measurement Comparisons of MPDs Computed from LAPS and Line-Laser Measurements Figure 55 and Figure 56 show 95% CIs of the mean differences between MPDs computed from LAPS and SSIA, and SSIT measurements, respectively. The following observations were noted from the results presented: • The number of cases where the mean MPD differences were statistically significant was about the same for both the SSIA and the SSIT configurations.
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From page 89... ...
Data Analysis 89 Comparisons of MPDs Computed from LAPS and Walking Texture Measurements At the RELLIS experiment, the TM2 operator made three repeat runs on each pavement section. Because the TM2 produced repeatable results in the previous experiments, TTI researchers also compared the TM2 MPDs with the corresponding indices determined from LAPS data to evaluate its usefulness as a reference texture-measurement device.
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From page 90... ...
Figure 57. The 95% CIs of differences between TM2 and LAPS MPDs.
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From page 91... ...
Data Analysis 91 • The 95% CIs of the mean MPD differences overlapped with the ±0.1 mm tolerance band on all test sections, except for the PCCT section. • Among the sections tested, the CIs were within the ±0.1 mm tolerance band on DGF1, DGF2, HMAP, SMAF, and PCCL.
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From page 92... ...
92 Protocols for Network-Level Macrotexture Measurement between SSIT and LAPS MPDs above 0.1 mm were observed not only on the OGFC but also on the HMAP and SMAF sections. On the HMAP and OGFC sections, mean MPD differences above 0.2 mm were obtained from SSIT test data collected at the 40 µs exposure setting.
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From page 93... ...
Data Analysis 93 avoid distortion of the beginning and end of the profile. Mirrored portions were removed after filtering was complete.
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From page 94... ...
94 Protocols for Network-Level Macrotexture Measurement Figure 59. Example MPD results -- single-spot laser.
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From page 95... ...
Data Analysis 95 Figure 64 shows that, depending on the laser head orientation, outlier readings were measured on the side slope where the receiver did not capture the transmitted beam from the laser head. To minimize the observed outliers found from examining the initial reference plate readings, researchers reconfigured the LAPS to orient the line-laser footprint perpendicular to the transverse grooves, as shown in the figure.
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From page 96... ...
0 50 100 150 200 250 300 350 400 450 -1.5 -1 -0.5 0 0.5 1 1.5 Te xt ur e H ei gh t( m m ) Plate 1, Short Exposure, 40 km/hr, filter = ASTM Filter 0 50 100 150 200 250 300 350 400 -1.5 -1 -0.5 0 0.5 1 1.5 Plate 1, Short Exposure, 65 km/hr, filter = ASTM Filter 0 50 100 150 200 250 300 350 400 -1.5 -1 -0.5 0 0.5 1 1.5 Plate 1, Short Exposure, 90 km/hr, filter = ASTM Filter 0 50 100 150 200 250 300 350 400 450 -1.5 -1 -0.5 0 0.5 1 1.5 Te xt ur e H ei gh t( m m )
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Figure 62. Single-spot laser Plate 5, first 100 mm, all exposures and speeds.
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From page 98... ...
Figure 63. Single-spot laser Plate 6, first 100 mm, all exposures and speeds.
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From page 99... ...
Data Analysis 99 The ranges used on the forest plots in Figure 65 and Figure 66 are representative of the 95% CI for the measurements made. To obtain this interval, standard errors for each combination of the plates and speed/exposures were calculated for each device using a pooled estimate obtained from the 12 MSDs from both the reference measurement and the high-speed device.
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From page 100... ...
Plate 1, no filtering Difference of Mean MPDs (mm) -0.5 -0.3 + / - 0.1 0.3 0.5 1_LLT_Short_55 1_LLT_Short_40 1_LLT_Short_25 1_LLT_Med_55 1_LLT_Med_40 1_LLT_Med_25 1_LLT_Long_55 1_LLT_Long_40 1_LLT_Long_25 1_LLT_Auto_55 1_LLT_Auto_40 1_LLT_Auto_25 1_LLL_Short_55 1_LLL_Short_40 1_LLL_Short_25 1_LLL_Med_55 1_LLL_Med_40 1_LLL_Med_25 1_LLL_Long_55 1_LLL_Long_40 1_LLL_Long_25 1_LLL_Auto_55 1_LLL_Auto_40 1_LLL_Auto_25 1_SSL_Short_55 1_SSL_Short_40 1_SSL_Short_25 1_SSL_Med_55 1_SSL_Med_40 1_SSL_Med_25 1_SSL_Long_55 1_SSL_Long_40 1_SSL_Long_25 Plate 5, no filtering Difference of Mean MPDs (mm)
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From page 101... ...
Plate 1, E1845 filtering Difference of Mean MPDs (mm) -0.5 -0.3 + / - 0.1 0.3 0.5 1_LLT_Short_55 1_LLT_Short_40 1_LLT_Short_25 1_LLT_Med_55 1_LLT_Med_40 1_LLT_Med_25 1_LLT_Long_55 1_LLT_Long_40 1_LLT_Long_25 1_LLT_Auto_55 1_LLT_Auto_40 1_LLT_Auto_25 1_LLL_Short_55 1_LLL_Short_40 1_LLL_Short_25 1_LLL_Med_55 1_LLL_Med_40 1_LLL_Med_25 1_LLL_Long_55 1_LLL_Long_40 1_LLL_Long_25 1_LLL_Auto_55 1_LLL_Auto_40 1_LLL_Auto_25 1_SSL_Short_55 1_SSL_Short_40 1_SSL_Short_25 1_SSL_Med_55 1_SSL_Med_40 1_SSL_Med_25 1_SSL_Long_55 1_SSL_Long_40 1_SSL_Long_25 Plate 5, E1845 filtering Difference of Mean MPDs (mm)
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From page 102... ...
102 Protocols for Network-Level Macrotexture Measurement Plate 1 As seen in Figures 65 and 66, the transverse line laser did not reliably reproduce the plate with the smallest waveform (Plate 1)
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From page 103... ...
Data Analysis 103 higher than the lab reference measurements. This was thought to be due to the lobes created on the signal at higher speeds and exposure settings.
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From page 104... ...
104 Protocols for Network-Level Macrotexture Measurement – Filtering did not have any beneficial effect on the larger waveforms (Plates 5 and 6) and caused MPD values for Plate 1 to drop significantly.
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From page 105... ...
Data Analysis 105 Traditional Measures MPD and RMS are well-documented macrotexture parameters; information on their calculation can be obtained in the references given in Table 34. MDE takes the average difference of profile points as shown in Equation 25: ∑= − = − 1 , (25)
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From page 106... ...
106 Protocols for Network-Level Macrotexture Measurement EAWE proposed by Mogrovejo et al.
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From page 107... ...
Data Analysis 107 shifted along the signal to decompose the signal into the wavelet transformation. The research team used the Haar mother wavelet, which is a square-wavelet made up of two complementary components -- the difference in magnitude between two points (the details)
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From page 108... ...
108 Protocols for Network-Level Macrotexture Measurement • MPMSR = MPGZ MSEPGZ ; • MPROMGZ = mean of prominence for all peaks > zero; • MWGZ = mean of peak width (equal to half the peak prominence) for all peaks > zero; • MPWR = MPROMGZ MWGZ ; • MWMSR = MPGMWGZ MSEPGZ ; and • NPGZ = count of peaks above zero-mean line in a base length.
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Data Analysis 109 SCRIM GT OBSI EAWE (filter, d*
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From page 110... ...
110 Protocols for Network-Level Macrotexture Measurement (the HMA, OGFC, SMA, or the EP-5 or Cargill Safe Lane surface treatments) and transversely textured surfaces (the PCC surfaces which had been tined or grooved perpendicularly to the direction of travel)
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From page 111... ...
Data Analysis 111 4.4.3 Multiple Regression Each of the aforementioned predictor parameters brings its own strengths to the singlevariable linear regression. For example, some parameters characterize the peaks of the base length evaluated, whereas others give various measures of the enveloped tire profile on the surface, and still others evaluate the statistical measures of the base length at various waveletdecomposed scales.
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112 Protocols for Network-Level Macrotexture Measurement Variance Inflation If the predictor variables are not independent (i.e., collinear) , several regression parameters will be found to be solutions to the minimization problem described above.
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Data Analysis 113 analysis. The friction-related predicted values (SCRIM and GT)
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114 Protocols for Network-Level Macrotexture Measurement road projects are always carried out on a much larger scale, not through the replacement of a single meter of pavement. Therefore, it was of particular interest to aggregate the data (predicted and predictor variables)
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Data Analysis 115 4.4.5 Main Findings The analysis of the various macrotexture parameters led to the following findings: • The top 10 single-variable predictor parameters for random, transverse, and longitudinally textured pavements correlated better with friction and/or noise than the most commonly used predictor parameters (MPD and RMS)
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From page 116... ...
116 Protocols for Network-Level Macrotexture Measurement of 0.05, a failure to reject the null hypothesis would demonstrate no effect of variable speeds, and rejection of the null hypothesis would demonstrate an effect of the variable speeds. To analyze the effect of speed (24 km/h to 105 km/h in 16-km/h increments)
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From page 117... ...
Data Analysis 117 forms flatter surfaces, as can be observed in Figure 68. This indicates that not only does speed have an influence, but also that surface type tends to either magnify or reduce this effect.
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From page 118... ...
118 Protocols for Network-Level Macrotexture Measurement Speed (MPH) 25_50 50_25 50_0_50 50_25_50 1.17 1.18 1.19 1.20 1.21 1.22 1.23 AV G M PD Figure 69.
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