Seasonal Influence on Skid Resistance and Equipment Calibration

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Author: G MackeyCo-Authors: D Poli and D Holloway

# 5496678

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 16Spray Seal 8

Time:Period of operation 2 years

Equipment:Grip Tester U of M +/- 6%

Seasonal Influence on Skid Resistance and Equipment Calibration

Seasonal Influence on Skid Resistance and Equipment Calibration

Uncontrollable FactorsExist in any real world situation.Their influence must be understood and existance recognised.

Policy/ StrategyWill 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 GeometryAge of the stone/pavement seal.WeatherVehicle quality (speed, brakes, tread [depth and patterns])Driver capability

Asphalt Sites

Skid Resistance Annual Ave Std Dev C of V

Max Variation

Span % Ave

Traffic AADT

% Comm. Vehicles

Year of Surfacing

AC1 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

AC2 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

AC3 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; Asphalt

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

SS1 0.70 0.06 8% 0.20 28% 1600 7 1999

SS2 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

SS3 0.60 0.09 15% 0.31 52% 2000 21 2006

SS4 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

Site Same Month

One Month Forward Offset

AC1 Site 1 0.54 0.80

Site 2 0.52 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

AC2 Site 1 0.58 0.75

Site 2 0.56 0.63

AC3 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

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

Site Same MonthOne Month Forward

Offset

SS1 0.31 0.65

SS2 Site 1 0.33 0.23

SS2 Site 2 0.21 0.23

SS3 0.00 0.36

SS4 Site 1 0.42 0.28

SS4 Site 2 -0.08 0.27

SS4 Old Site 1 0.26 0.31

SS4 Old Site 2 0.40 0.58

CORRELATIONS

Spray Seal Sites

Rainfall and Test results

• Same month

• One month offset

Site Same MonthOne Month

Forward Offset

SS1 0.31 0.65

SS2 Site 1 0.33 0.23

Site 2 0.21 0.23

SS3 0.00 0.36

SS4 Site 1 0.42 0.28

Site 2 -0.08 0.27

Old Site 1 0.26 0.31

Old Site 2 0.4 0.58

> 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 (Oliver) v Time

53

54

55

56

57

58

59

60

61

1/11

/197

8

9/02

/197

9

20/0

5/19

79

28/0

8/19

79

6/12

/197

9

15/0

3/19

80

23/0

6/19

80

1/10

/198

0

9/01

/198

1

Time

Brit

ish

Pen

dulu

m P

SV

0

20

40

60

80

100

120

140

SRV(20) Rainfall Oliver ABS Poly. (Rainfall Oliver ABS) Poly. (SRV(20))

Rai

nfal

l mm

Skid Resistance & Rainfall v Time

GRAPHS OF DTEI PROJECT WORK

Spray Seal

Asphalt

Relationship?

Present but Weak

RN SS2 Site 2 Skid Resistance and Rainfall v Time

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Jul-06 Oct-06 Jan-07 Apr-07 Aug-07 Nov-07 Feb-08

Time

Grip Number

0.0

20.0

40.0

60.0

80.0

100.0

120.0 Rainfall mm

Skid Resistance Rainfall Poly. (Rainfall) Poly. (Skid Resistance )

RN AC1, Site 1, Skid Resistance & Rainfall v Time

0.2

0.3

0.4

0.5

0.6

Jul-

06

Oct

-0

6 Jan-

07

Apr

-0

7 Aug

-0

7 Nov

-0

7

Fe

b-0

8 Jun-

08

Sep

-0

8

Dec

-0

8 Ma

r-0

9

Time

Grip Number

-40.0

-20.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

Rainfal

l mm

Site 1 Rainfall Poly. (Site 1) Poly. (Rainfall)

0.00

0.100.20

0.30

0.400.50

0.60

0.70

0.800.90

1.00

28/0

4/20

07

14/1

1/20

07

1/06

/200

8

18/1

2/20

08

6/07

/200

9

22/0

1/20

10

10/0

8/20

10

26/0

2/20

11

@65 @65 @95 @95

SEASONAL INFLUENCE

Asphalt Pavement over the years with,Negligible Use

LOCAL SEASONAL INFLUENCES

0.15

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

1.05

1210 1710 2210 2710 3210 3710 4210

(metres)

Gri

p N

um

ber

Recommended Investigatory Level

Tested 4/6/2010Av = 0.64SD = 0.04C of V = 6.9%

Start Bridge

Tested 16/2/2010Av = 0.39SD = 0.06C of V = 14.3%

Tested 16/2/201034 days of no rain prior to testing

Tested 4/6/201046 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 dayB2 Constant (360/365)B1 and B3 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

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

Skid Resistance Adjustment Value Asphalt

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Sep Oct

Nov

Dec Ja

n

Feb Mar

Apr

May

Jun

Jul

Aug

Time Months

Sk

id R

es

ista

nc

e A

dju

stm

en

t (G

N)

Skid Resistance Average Poly. (Skid Resistance Average)

Skid Resistance Adjustment Value Spray Seal

Sep

tem

ber

Oct

ober

Nov

embe

r

Dec

embe

r

Janu

ary

Feb

ruar

y

Mar

ch

Apr

il

May

June Ju

ly Aug

ust

-0.10

-0.08

-0.06

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08

Time Months

Ski

d R

esis

tan

ce A

dju

stm

ent

Fac

tor

(GN

)

Skid Resistance Average Poly. (Skid Resistance Average)

Monthly Skid Resistance Normalisation Factors, Asphalt and Spray Seal

Polynomial : y = 8E-0.5x4 + 0.0021x3 – 0.0142x2 + 0.0208x + 0.0084. R2 = 0.80

Polynomial: y = -0.0001x4 + 0.0029x3 - 0.0161x2 - 0.0036x + 0.0666. R2 = 0.92

Combined Skid Resistance Adjustment

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

0.1

Se

pte

mb

er

Oct

ob

er

No

vem

be

r

De

cem

be

r

Jan

ua

ry

Fe

bru

ary

Ma

rch

Ap

ril

Ma

y

Jun

e

July

Au

gu

st

Time Months

Ski

d R

esis

tan

ce A

dju

stm

ent

Fac

tor

(GN

)

Skid Resistance Poly. (Skid Resistance)

COMBINING THE TWO PREVIOUS GRAPHS

Nominal change only

Polynomial: y = -0.001x4 + 0.0026x3 – 0.0155x2 + 0.0042x + 0.048. R2 = 0.91

Combined Normalisation Factor to July/August Skid Resistance

-0.20

-0.15

-0.10

-0.05

0.00

0.05

Septe

mber

Octo

ber

Novem

ber

Decem

ber

January

Febru

ary

Marc

h

April

May

June

July

August

Time Months

Skid

Resis

tan

ce G

N,

Ad

justm

en

t F

acto

r

Skid Resistance Poly. (Skid Resistance)

ADJUSTMENT TO MONTHS OF

JULY / AUGUST

Combined Skid Resistance

Normalisation factor to

July/August

Polynomial y = -0.0001x4 + 0.0026x3 – 0.0155x2 + 0.0042x – 0.0338. R2 = 0.91

Skid Resistance

Mean 0.59

Standard Deviation 0.09

Mean Confidence Level (95%) +/-0.07

Lower Limit Mean Upper Limit

0.52 -------------- 0.59 ----------------0.66

Data Confidence Level (95%) 0.59 +/- 0.17 (+/-29%)

Lower Limit Mean Upper Limit

0.42 ------------- 0.59 --------------- 0.75

Skid Resistance

Mean 0.59

Standard Deviation 0.09

Mean Confidence Level (95%)

+/-0.07

Lower Limit Mean Upper Limit

0.52 -------------- 0.59 ----------------0.66

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

Mean 0.59

Standard Deviation 0.09

Mean Confidence Level (95%)

+/-0.07

Lower Limit Mean Upper Limit

0.52 -------------- 0.59 ----------------0.66

Data Confidence Level (95%) 0.59 +/- 0.17 (+/-29%)

 

Lower Limit Mean Upper Limit

0.42 ------------- 0.59 --------------- 0.75

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

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Chainage metres

EQUIPMENT CALIBRATION AND MAINTENANCE

VERIFICATION SITE

Multiple results from a local verification siteConsistent 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

Thank You

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