Seasonal Influence on Skid Resistance and Equipment Calibration Presented by Author: G Mackey Co-Authors: 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?

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

Asphalt Sites Skid Resistance Annual AveStd DevC of V Max Variation Span % Ave Traffic AADT % Comm. Vehicles Year of Surfacing AC1 Site %0.2042% Site %0.1938% Site %0.1327% Site %0.1937% Site %0.2031% Site %0.1827% Site %0.2445% AC2 Site %0.2241% Site %0.1831% AC3 Site %0.2650% Site %0.2243% Site %0.2249% Site %0.2765% Site %0.2651% Site %0.2355% Site %0.2351% Site %0.2451% Annual Overall Results; Asphalt

ANNUAL OVERALL RESULTS FOR SPRAY SEALS Spray Seals Site Skid Resistance Annual Ave Std DevC of V Max Variation Span % Ave Traffic AADT % Comm Vehicles Year of Surfacing SS %0.2028% SS2 Site %0.2034% Site %0.1936% SS %0.3152% SS4 Site %0.0914% Site %0.1932%

Correlations SiteSame Month One Month Forward Offset AC1 Site Site Site Site Site Site Site AC2 Site Site AC3 Site Site Site Site Site Site Site Site CORRELATIONS Asphalt Sites Rainfall and Test Results Same month One month offset > 0.7 Significant 0.5 – 0.7 Of Interest 0.5 < Some Interest

SiteSame Month One Month Forward Offset SS SS2 Site SS2 Site SS SS4 Site SS4 Site SS4 Old Site SS4 Old Site CORRELATIONS Spray Seal Sites Rainfall and Test results Same month One month offset SiteSame Month One Month Forward Offset SS SS2 Site Site SS SS4 Site Site Old Site Old Site > 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 Spray Seal Asphalt Relationship? Present but Weak

SEASONAL INFLUENCE Asphalt Pavement over the years with, Negligible Use

LOCAL SEASONAL INFLUENCES Tested 16/2/ days of no rain prior to testing Tested 4/6/ mm of rain over 11 days prior to testing 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.

Skid Number = B1 x Sin(B2 x JDay + B3) JDay = Julian calendar day B2 Constant (360/365) B1 and B3 are estimated regression coefficients. Diringer and Barros (1990). BPN = BPN terminal – 5 x Cos(2π/ x Jday) GN = GN terminal x Cos(2π/ x Jday) (towed) Cenek Models lack confidence levels MODELLING TO PREDICT SKID RESISTANCE

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. INFLUENCE OF AGGREGATES

METHODS OF ADJUSTMENT Monthly Skid Resistance Normalisation Factors, Asphalt and Spray Seal Polynomial : y = 8E-0.5x x3 – x x R2 = 0.80 Polynomial: y = x x x x R2 = 0.92

COMBINING THE TWO PREVIOUS GRAPHS Nominal change only Polynomial: y = x x3 – x x R2 = 0.91

ADJUSTMENT TO MONTHS OF JULY / AUGUST Combined Skid Resistance Normalisation factor to July/August Polynomial y = x x3 – x x – R2 = 0.91

Skid Resistance Mean0.59 Standard Deviation0.09 Mean Confidence Level (95%)+/-0.07 Lower Limit Mean Upper Limit Data Confidence Level (95%) / (+/-29%) Lower Limit Mean Upper Limit Skid Resistance Mean0.59 Standard Deviation0.09 Mean Confidence Level (95%) +/-0.07 Lower Limit Mean Upper Limit CONFIDENCE LIMITS FOR DATA Uncertainty Banding For a 95% confidence of locating the mean. For a 95% confidence of capturing the data. Banding is much larger. The span of uncertainty here is quite large and would be unacceptable. Skid Resistance Mean0.59 Standard Deviation0.09 Mean Confidence Level (95%) +/-0.07 Lower Limit Mean Upper Limit Data Confidence Level (95%) / (+/-29%) Lower Limit Mean Upper Limit

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? OPTIMAL TEST PERIODS SOUTH AUSTRALIA AND OTHERS

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

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, p69]. (Lester, 2010). STATISTICAL OPINION

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. CONCLUSIONS

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