Texture/Friction Measurements and Analysis at Runway 13-31 of James Armstrong Richardson International Airport in Winnipeg Qingfan Liu, EIT, PhD candidate, University of Manitoba Ahmed Shalaby, PhD, P. Eng., University of Manitoba Andrew Curwain, P. Eng., Winnipeg Airports Authority Inc. 1 OUTLINE Introduction Texture/Friction Measurement at Winnipeg International Airport Data Analysis Conclusions 2 1
Introduction 3 Runway 13-31 Friction Issues 1. Surface Characteristics 2001 Reconstruction of Centerline Composite AC and PCC Surface Localized transverse Grooving, Localized Grinding for Ride Differential Performance AC- PCC 2. Pavement Maintenance Works 2011 Rehabilitation Work centre 100ft 2013 Surface Friction Improvements - Diamond Grinding Selected 4 2
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Friction: TP312 5 th edition (Effective: Sep 15, 2015) Periodical measurements of a runway friction is required. Corrective maintenance action is taken when: [smooth tire, 1mm water film (0.5 mm before 2014-12-15)]. the average coefficient of friction (COF) for the entire runway is below 0.50; or any portions of a runway surface that are 100 m or greater in length have an average COF less than 0.30. 11 Texture: TP312 5th edition (Effective: Sep 15, 2015) Pavement surface texture can significantly affect the measured friction values. Measurements of texture are not normally required unless texture related friction problems are suspected. Pavement texture may become polished as the surface ages and wears under traffic. May require the periodic removal of rubber deposits or the re-texturing of pavement surface. 12 6
Crash Rate and Pavement Friction (Roadway perspective) * Skid Number [SN] or Friction Number (FN), a friction index, defined by ASTM E 274 locked wheel test: 13 Factors Affecting Pavement Friction 1. Pavement surface features Surface texture Material properties Temperature 2. Vehicle operational parameters Slip speed Driving maneuver 3. Tire properties Foot Print, Inflation pressure, load Tread design and condition, Rubber characteristic 4. Environmental factors Climate (water, snow and ice, wind, temperature) Contaminants (salt, sand, dirt, mud, debris) 14 7
Texture and Friction A need for in-depth understanding of the relationship between texture and friction (NCHRP 634, 2009) (The National Cooperative Highway Research Program) To use 3D texture measurement for more comprehensive understanding of texture (TRB AFD90, 2010, 2013) (Committee: Surface Properties - Vehicle Interaction) 15 Definition of Texture the deviations of the pavement surface from a true planar surface wavelength MPD=(Peak level 1 st +Peak level 2 nd )/2 16 8
Texture Classification The two levels of texture that predominantly affect friction are micro-texture and macro-texture Texture Classification Relative Wavelengths, λ Characteristics Micro-texture (I-texture) Macro-texture (A-texture) Megatexture Unevenness (Roughness) λ < 0.5 mm by fine sand or surface roughness of large aggregate 0.5 mm λ < 50 mm Spaces and depths between aggregate particles 50 mm λ < 500 mm Construction or pavement distress 0.5 m λ < 50 m Construction or pavement distress 17 Micro- and Macro-texture (I-texture) (A-texture) 18 9
Influence of Texture 19 Sand Patch Method (ASTM E 965) This volumetric based spot test mean texture depth (MTD) where: MTD = Mean texture depth, mm V = Sample volume, mm 3 D = Average material diameter, mm Simple, inexpensive, and direct measurement of pavement macrotexture Sensitive to operator s ability 20 10
Outflow Meter (ASTM E 2380) Simple, inexpensive, indication of hydroplaning No good correlation to MTD Circular Track Meter (ASTM E2157) Texture height(mm) 1.5 1 0.5 0-0.5-1 -1.5 0 111.5 223 334.5 446 557.5 669 780.5 892 CTM circumference (mm) Repeatable and independent of operators, well accepted MPD correlates well with MTD 22 11
Photometric Stereo Method Capture the shadow effect of texture 3D method more meaningful indices Measurement is slow Complicated analysis 23 Line-Laser Scanner (1) 12
Line-Laser Scanner (2) Using a line-laser to scan the target surface of 100 100 mm A 3D texture height map with over 5 million (2448 2048) data points. 25 Specifications Laser Line laser Sample interval (mm) <0.05 Vertical accuracy (mm) <0.05 Wavelength bands Texture map dimensions 3D Microtexture, Macrotexture Macrotexture indices SMTD, Skewness, Kurtosis, S q Microtexture index NPSE Method of measurement Stationary Standard/specification Attributes Research device Indices from 3D texture 26 heights 13
Texture Measurements: Summary Low speed Volumetric methods Sand patch method direct measurement of macro-texture --MTD Outflow meter indirect measurement of texture outflow time Image based methods Photometric stereo Range based methods Circular track meter 2D method, macro-texture only--mpd Line-laser scanner 3D micro- and macro-texture -- multiple indices High speed Range based methods ROSANv 2D macro-texture only MPD VTEXTURE 3D macro-texture only continuous MPD 27 Runway Texture/Friction Measurement 28 14
Friction Measurement (TP 312 4 th edition) Continuous friction tests were conducted at the end of September 27, 2013 using a SARSYS Surface Friction Tester (SFT). SFT at 65 km/h, a smooth-tread ASTM test tire at 200 kpa, under self-watering at 0.5mm (1.0 mm, TP312 5 th edition) water film depth. 29 Texture Measurement The texture/friction were measured at 3 m, 6 m, and 15 m offsets of both left and right of the centreline of runway 13-31 2years old PCC pavement with longitudinal diamond grind and new transverse diamond groove. 2-10 years old AC pavement Texture was measured on: Oct 3, 2013 and Oct25, 2013 30 15
Objectives of the Project Investigate the features of runway texture in 3D manner Explore the relationship between runway friction and texture Use texture indices as an economical backup of pavement friction characteristics 31 Significance of this Study Current texture measurement focus mainly on macro-texture only. This research covers Micro- and Macro-texture 2D profile and its indices (i.e. MPD) is not adequate to describe pavement texture; 3D is more informational Potential to update pavement design/management standards 1 0.8 0.6 Texture height (mm) 0.4 0.2 0-0.2-0.4-0.6-0.8 0 10 20 30 40 50 60 70 80 90 Longitudinal distance (mm) Tire/pavement foot print 3D texture heights 2D texture profile 32 16
Runway Texture Measurement 33 Recovered 3D Texture PCC with transverse groove and longitudinal grind 17
2D View of Texture (Longitudinal) Longitudinal direction Transverse Diamond Grove Oct. 2013 9 8 Texture height (mm) 7 6 5 4 10 20 30 40 50 60 70 80 90 100 Longitudinal distance (mm) 2D View of Texture (Transverse) Transverse direction 9 8 Texture height (mm) 7 6 5 4 10 20 30 40 50 60 70 80 90 100 Transverse distance (mm) 18
Data Analysis 37 Texture Analysis: Flowchart 3D texture measurement Remove mean and trend Discrete wavelet transform Macrotexture Amplitude: SMTD S q S sk S ku Volume: V mp V mc V vc V vv Microtexture Power spectrum energy: NPSE Regression, ANOVA, F-test, or t-test 19
Discrete Wavelet Transform(DWT) 39 3D Texture Parameters Simulated mean texture depth (SMTD) Sand to highest elevation Zero mean Negative texture Texture height (mm) 1 0.5 0-0.5-1 Sand to highest elevation Zero mean Negative texture 0 10 20 30 40 50 60 70 80 90 100 Transverse distance (mm) 2D View Positive texture 3D view 40 20
Kurtosis(S ku ) Kurtosis is a measure of whether a set of data are peaked or flat compared to a normal distribution (S ku=3 ). Uniform Kurtosis =1.8 41 SMTD and FN (3m left) 100 90 80 70 FN 2013-Tradewind SMTD 2013-UM 2 1.8 1.6 1.4 1.2 FN 60 50 40 1 0.8 0.6 SMTD 30 5000 5500 6000 6500 7000 7500 Chainage (m) 42 21
SMTD and FN (3m right) 90 80 FN 2013-Tradewind SMTD 2013-UM 1.8 1.6 70 FN 60 50 40 30 0.8 0.6 0.4 0.2 SMTD 20 0 5000 5500 6000 6500 7000 7500 Chainage (m) 43 SMTD and FN (6m left) 90 80 70 FN 2013-Tradewind SMTD 2013-UM 1.8 1.6 1.4 1.2 FN 60 50 40 30 1 0.8 0.6 0.4 0.2 SMTD 0 5000 5500 6000 6500 7000 7500 Chainage (m) 44 22
SMTD and FN (6m right) 100 90 80 70 FN 2013-Tradewind SMTD 2013-UM 1.8 1.6 1.4 1.2 FN 60 1 SMTD 50 40 30 0.4 0.2 20 0 5000 5500 6000 6500 7000 7500 Chainage (m) 45 Friction Prediction (1) 100 90 80 70 60 FN 50 40 30 20 10 = 60.5 +13.9 SMTD R 2 =0.15 0 0 0.5 1 1.5 2 2.5 3 SMTD (mm) A single macrotexture index is NOT adequate to define friction 46 23
Friction Prediction (2) FN=73.77+14.08 SMTD-1.81Sku R 2 = 0.73 The prediction of FN was improved than using SMTD only The texture distribution shape represented by kurtosis (Sku) of the texture is another contributor to FN 47 Statistical Analysis Regression model Coefficient Estimate Std error t-statistic t-critical p-value 73.773 5.0535 14.598 2.0452 1.284 10-14 Regression model 14.081 3.5452 3.9719 2.0452 4.531 10-4 -1.8107 0.2327 7.7819 2.0452 1.176 10-8 Number of observations 31 Root mean squared error 8.85 0.73 F-Statistic 37.9 F-Critical 3.3277 p-value (<0.05) 31.07 10-8 24
Predicted Vs. Measured FN 90 70 FN 50 Surface Friction Improvements - Diamond Grinding Selected 30 10 Measured Predicted 0 5+250_3L 5+480_3l 5+540_3l 5+834_3l 5+965_3l 6+150_3l 7+076_3l 7+183_3l Chainage (m) FN was predicted by using Oct 3, 2013 texture measurements 49 Predicted FN after Diamond Grinding 90 70 FN 50 30 10 0 6+950_3L 6+950_6L 7+255_3l 7+255_6l 7+315_3l 7+315_6l Chainage (m) FN was predicted by using Oct 25, 2013 texture measurements after diamond grinding to improve friction 50 25
Conclusions 51 Conclusions Runway pavement texture was recovered in 3D manner Runway surface texture can be represented by the proposed 3D texture parameters A significant relationship with an R 2 value of 0.73 was found between pavement friction and texture parameters Both texture amplitude (SMTD) and texture distribution (kurtosis) are import factors to runway friction. 3D texture measurement is an economic way to evaluate texture features for the construction of new pavement, to monitor localized polished areas for which a friction testing has not been arranged. 52 26
THANK YOU Questions? 53 27