Seasonal Influence on Skid Resistance and Equipment Calibration

Seasonal Influence on Skid Resistance and Equipment Calibration Presented by Author: Co-Authors: # 5496678 G Mackey D Poli and D Holloway

Seasonal Influence on Skid Resistance and Equipment Calibration Road Asset Managers • Safety of road users. • Need to know pavement surface friction resistance. The ever present question: Do seasons influence skid resistances test results, and if they do, can the outputs be normalised thereby enabling testing to be undertaken all year round?

Seasonal Influence on Skid Resistance and Equipment Calibration Test Sites: Asphalt 16 Spray Seal 8 Time: Period of operation 2 years Equipment: Grip Tester U of M +/- 6%

Seasonal Influence on Skid Resistance and Equipment Calibration Uncontrollable Factors Exist in any real world situation. Their influence must be understood and existance recognised. Policy/ Strategy Will quantify the known’s and explain or address the unquantifiable factors. Examples of Uncontrollables: Binder(quality and quantity) Traffic loading, Type of surfacing and location (urban and rural) Road Geometry Age of the stone/pavement seal. Weather Vehicle quality (speed, brakes, tread [depth and patterns]) Driver capability

Annual Overall Results; Asphalt Sites Skid Resistance Annual Ave C of V Max Variation Span % Ave Traffic AADT % Comm. Vehicles Year of Surfacing Std Dev AC 1 Site 1 0. 48 0. 07 15. 0% 0. 20 42% 21000 6 2000 Site 2 0. 50 0. 07 13. 0% 0. 19 38% 21000 6 2000 Site 3 0. 48 0. 05 10% 0. 13 27% 21000 6 2000 Site 4 0. 53 0. 06 12% 0. 19 37% 21000 6 2000 Site 5 0. 65 0. 06 9% 0. 20 31% 21000 6 2000 Site 6 0. 68 0. 05 8% 0. 18 27% 21000 6 2000 Site 8 0. 53 0. 07 13% 0. 24 45% 21000 6 2000 AC 2 Site 1 0. 54 0. 07 14% 0. 22 41% 4500 3 2005 Site 2 0. 57 0. 06 10% 0. 18 31% 4500 3 2005 AC 3 Site 1 0. 52 0. 09 18% 0. 26 50% 15500 6 2004 Site 2 0. 51 0. 08 16% 0. 22 43% 15500 6 2004 Site 3 0. 45 0. 08 18% 0. 22 49% 15500 6 2004 Site 4 0. 42 0. 08 20% 0. 27 65% 15500 6 2004 Site 5 0. 50 0. 08 16% 0. 26 51% 15500 6 2004 Site 6 0. 42 0. 08 19% 0. 23 55% 15500 6 2004 Site 7 0. 45 0. 08 18% 0. 23 51% 15500 6 2004 Site 8 0. 46 0. 07 16% 0. 24 51% 15500 6 2004

ANNUAL OVERALL RESULTS FOR SPRAY SEALS Spray Seals Site Skid Resistance Annual Ave Std Dev C of V Max Variation Span % Ave Traffic AADT % Comm Vehicles Year of Surfacing SS 1 0. 70 0. 06 8% 0. 20 28% 1600 7 1999 SS 2 Site 1 0. 60 0. 06 9% 0. 20 34% 3900 18 2005 Site 2 0. 52 0. 05 10% 0. 19 36% 3900 18 2005 SS 3 0. 60 0. 09 15% 0. 31 52% 2000 21 2006 SS 4 Site 1 0. 66 0. 03 5% 0. 09 14% 1500 26 1997 Site 2 0. 59 0. 05 9% 0. 19 32% 1500 26 1993

CORRELATIONS Correlations Site Asphalt Sites Same Month One Month Forward Offset AC 1 Site 1 0. 54 0. 80 Site 2 0. 52 Site 3 0. 38 0. 63 Site 4 0. 55 0. 70 Site 5 0. 56 0. 51 Site 6 0. 55 0. 46 Site 8 0. 44 0. 59 AC 2 Site 1 0. 58 0. 75 Site 2 0. 56 0. 63 AC 3 Site 1 0. 51 0. 72 Site 2 0. 54 0. 83 Site 3 0. 54 0. 67 Site 4 0. 52 0. 54 Site 5 0. 50 0. 59 Site 6 0. 53 0. 63 Site 7 0. 50 0. 70 Site 8 0. 51 0. 61 Rainfall and Test Results • Same month • One month offset > 0. 7 Significant 0. 5 – 0. 7 Of Interest 0. 5 < Some Interest

CORRELATIONS Spray Seal Sites Site Same Month Site SS 1 One Month Forward Offset One Same Month 0. 31 SS 2 Site 1 0. 33 Site 2 SS 2 Site 2 SS 3 0. 21 SS 4 Site 1 0. 00 0. 31 SS 3 0. 65 0. 33 0. 23 0. 21 • Same month • One month offset 0. 23 0. 36 0. 42 0. 36 0. 28 -0. 08 0. 42 Rainfall and Test results 0. 23 0. 00 Site 2 SS 4 Site 1 Old Site 1 0. 65 Month Forward Offset 0. 27 0. 26 0. 28 0. 31 0. 4 0. 58 Old Site 2 SS 4 Site 2 -0. 08 SS 4 Old Site 1 0. 26 0. 31 SS 4 Old Site 2 0. 40 0. 58 0. 27 > 0. 7 Significant 0. 5 – 0. 7 Of Interest 0. 5 < Some Interest

PREVIOUS AUSTRALIAN RESEARCH This graph is a reproduction of the overview of South Australian results. John Oliver (ARRB) Skid Resistance & Rainfall v Time

GRAPHS OF DTEI PROJECT WORK RN SS 2 Site 2 Skid Resistance and Rainfall v Time 100. 0 0. 5 80. 0 0. 4 60. 0 0. 3 40. 0 0. 2 Rainfall mm 120. 0 0. 6 Grip Number 0. 7 Relationship? Present but Weak 20. 0 0. 1 0 0. 0 Jul-06 Oct-06 Jan-07 Apr-07 Aug-07 Nov-07 Feb-08 Time Spray Seal Poly. (Skid Resistance ) 0. 5 0. 4 Time Site 1 Rainfall Poly. (Site 1) Poly. (Rainfall) Dec 08 Sep 08 Jun 08 Feb 08 Nov 07 Aug 07 Apr 07 Jan 07 Jul 06 0. 2 Oct 06 0. 3 140. 0 120. 0 100. 0 80. 0 60. 0 40. 0 20. 0 -20. 0 -40. 0 Rainfall mm Poly. (Rainfall) RN AC 1, Site 1, Skid Resistance & Rainfall v Time 0. 6 Grip Number Rainfall Mar 09 Skid Resistance Asphalt

SEASONAL INFLUENCE Asphalt Pavement over the years with, Negligible Use

LOCAL SEASONAL INFLUENCES Current example of local climatic influences over a few weeks. Of significant concern to the road asset manager After two weeks of rain skid resistance has improved by 50%. Preplexing situation. Uninitiated doubt the testing service and quality of testing equipment. This is not the case. Tested 16/2/2010 34 days of no rain prior to testing Tested 4/6/2010 46 mm of rain over 11 days prior to testing

MODELLING TO PREDICT SKID RESISTANCE Skid Number = B 1 x Sin(B 2 x JDay + B 3) JDay = Julian calendar day B 2 Constant (360/365) B 1 and B 3 are estimated regression coefficients. Diringer and Barros (1990). BPN = BPN terminal – 5 x Cos(2π/365. 25 x Jday) GN = GN terminal + 0. 002 x Cos(2π/365. 25 x Jday) (towed) Cenek Models lack confidence levels

INFLUENCE OF AGGREGATES Research suggests that the amplitude of seasonal variation is influenced by aggregate factors and in particular the construct of the aggregate. • Polish susceptible stones give a more pronounced change • Age of the aggregate is influential • PAFV lab test is not useful in indicating performance, it is only a ranking tool.

METHODS OF ADJUSTMENT Monthly Skid Resistance Normalisation Factors, Asphalt and Spray Seal Polynomial: y = -0. 0001 x 4 + 0. 0029 x 3 - 0. 0161 x 2 - 0. 0036 x + 0. 0666. R 2 = 0. 92

COMBINING THE TWO PREVIOUS GRAPHS Nominal change only Polynomial: y = -0. 001 x 4 + 0. 0026 x 3 – 0. 0155 x 2 + 0. 0042 x + 0. 048. R 2 = 0. 91

ADJUSTMENT TO MONTHS OF JULY / AUGUST Combined Skid Resistance Normalisation factor to July/August Polynomial y = -0. 0001 x 4 + 0. 0026 x 3 – 0. 0155 x 2 + 0. 0042 x – 0. 0338. R 2 = 0. 91

CONFIDENCE LIMITS FOR DATA Skid Resistance Uncertainty Banding Skid Resistance Mean Standard Deviation Mean Confidence Level (95%) Lower Limit Mean 0. 59 0. 09 +/-0. 07 Mean 0. 09 +/-0. 07 Upper Limit Lower Limit Mean Upper 0. 52 ------- 0. 59 --------0. 66 Limit 0. 52 ------- 0. 59 --------0. 66 Data Confidence Level (95%) 0. 59 +/- 0. 17 (+/-29%) Data Confidence Level (95%) Lower Limit 0. 59 +/- 0. 17 (+/-29%) Mean Standard Deviation 0. 09 For a 95% confidence of locating the Mean Confidence Level +/-0. 07 mean. (95%) Lower Limit Mean Upper Limit 0. 52 ------- 0. 59 --------0. 66 For a 95% confidence of capturing the data. Upper Limit 0. 42 ------- 0. 59 -------- 0. 75 Lower Limit 0. 59 Upper Limit 0. 42 ------- 0. 59 -------- 0. 75 Banding is much larger. The span of uncertainty here is quite large and would be unacceptable.

OPTIMAL TEST PERIODS SOUTH AUSTRALIA AND OTHERS South Australia • Network Testing in Spring • Precludes the summer months, November to April. • Data is then presented without seasonal correction. UK • UK Highways Agency • Recognises seasonal variation • Addressed by controlling testing in the summer months. • Regular use of test sites to determine a correction/ adjustment factor for results. New Zealand • Recognise seasonall variation • Undertake the programmed network testing over a limited time period (November to February) • Regular use of test sites during assessment period, to determine a correction/adjustment factor. NSW and Victoria • Recognise that seasonal factors will influence results but do not recommend a correction factor. • Significant climatic changes throughout Victoria and New South Wales?

EQUIPMENT CALIBRATION AND MAINTENANCE VERIFICATION SITE Multiple results from a local verification site Consistent replication but significant variability

STATISTICAL OPINION In-House modelling undertaken with no significant results. (Used selected project and equipment verification site data) DTEI engaged a specialist statistician on the matter of harmonisation and predictive modeling. The report concluded in the negative. In summary “ Experience has shown that predicting skid resistance… is very difficult due to inherent variability of skid resistance measurement. The variability is due largely to environment factors (temperature, detritus building up, rainfall and cyclical polishing/abrading rejuvenation cycles) and the skid testing equipment and methodology used. Separating out these factors and determining their individual statistical significance has been difficult historically” [Wilson and Dunn, 2005, p 69]. (Lester, 2010).

CONCLUSIONS • Confirmed skid resistance variability is influenced by seasonal factors. • A relationship does exist between climate and skid resistance. • Local climate changes are of greater importance • Problem is not unique to any particular piece of equipment or climate. • Problem is ongoing and variability must be accommodated • No accurate or reliable harmonisation or correlation of results can be achieved between tests of the same section of road at different times using the same or similar equipment. • Predictive modeling is possible but only with significant uncertainty ranges. • Skid resistance results are only part of the process when assessing the condition of a road. • DTEI is reviewing the matter of pavement skid resistance and its associated matters to provide a safe road network.

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