Stiffness Variability

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25th ARRB Conference Shaping the future: Linking policy, research and outcomes, Perth, Australia 2012

ARRB Group Ltd and Authors 2012 1

ANALYSIS OF THE STIFFNESS VARIABILITY INASPHALT LAYERS USING THE MONTE CARLOSIMULATION

Laszlo Petho, ARRB Group Ltd., Australia

ABSTRACT

Heavy duty pavements are currently designed using the general mechanistic approach. A majorinput to the multi-layered pavement design is the stiffness of the pavement material. Thecharacteristics and performance of asphalt pavements are highly influenced by the daily,monthly and yearly fluctuations of the pavement temperature, due to thetemperature-dependent nature of the bituminous materials.

The pavement design is usually performed at a single temperature, which cannot describe fullythe continuous change of the hot mix asphalt mechanical property over the design period. In

this paper probability mass functions are provided to describe the stiffness fluctuation ofstructural asphalt layers under in-service conditions, where the well-established and validatedWitczak method was used for stiffness prediction. The calculations are based on the stochasticapproach, using the Monte Carlo Simulation (MCS). The calculations are based on standardbituminous binder properties combined with standard mix composition.

The conclusions of this paper confirm the importance of careful material selection within theasphalt pavement considering pavement design and asphalt technology issues, and alsohighlight the possible application of innovative technologies.

INTRODUCTION

In the analytical pavement design process it is essential to know the stiffness and fatigueproperties of bound pavement layers. The stiffness is a key performance indicator and a primaryinput for mechanistic pavement design. The stiffness value greatly influences the pavementthickness in the design procedure; therefore it is paramount to know realistic stiffness values ofdifferent asphalt mixes for the design of flexible or semi-rigid asphalt pavements. It should benoted that the fatigue performance of hot mix asphalt is of paramount importance in pavementdesign and the fine balance between stiffness and fatigue performance provides the mostcost-effective pavement design option. This approach also provides the basis for perpetualpavement design.

The road authorities might apply different strategies in their pavement design. The selectedstrategy in the pavement design approach might include a certain level of uncertainty in the

material behaviour, which leads to more or less conservative pavement design. This paper doesnot discuss these strategies in detail; however, it highlights the importance of the performancedistribution of the hot mix asphalt (HMA) products and therefore, indirectly, the reliability of thematerial performance. Before choosing any pavement design strategy it is crucial to have agood insight into the range of the HMA stiffness under in-service conditions, and how this mightinfluence the performance fluctuation. This paper deals with the performance assessment ofHMA as a function of the pavement temperature.

For HMA production there are many different types of bituminous binders available (plain binder,polymer modified binder, rubber modified binder, multigrade binder, etc.). Taking into accountthat many different aggregate sources are available (volcanic, sediment, metamorphic), and theaggregate source might have different chemical properties (acidic or basic) with many differentparticle size distributions (PSD), it is obvious that the variability of HMA properties is very high.

The pavement engineer is not aware of the in situ HMA properties when the pavement design is


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performed for a particular project, since pavement and alignment design happens on largeprojects sometimes several years before the actual HMA production starts. Because of thenature of the planning and construction process, this problem has always been an issue;consequently there was always a need for the pre-assessment of HMA product performance. Ina particular project, following the pavement design phase, a pre-assessment process can beperformed before the actual mix design starts, since the asphalt technologist tries to minimisethe number of iterative steps before and during the mix design.

The Shell stiffness prediction model for HMA

In 1963 Shell published a set of design charts for flexible pavements, based on an analyticalmethod with design criteria derived from empirical design methods, which basically resultedfrom the AASHO Road Test and laboratory data. In 1978 this system was extended toincorporate all relevant major design parameters and published in the Shell Pavement DesignManual (SPDM) (Shell 1978). In 1985 the method was updated in an Addendum based onexperience over the previous ten years. The PC version of SPDM was developed, sincepersonal computers had become within the reach of engineers (Valkering & Stapel 1992). Thethen introduced new program consisted of modules for the prediction of the binder stiffness (vander Poel nomograph), the asphalt mix stiffness and the fatigue life of the asphalt mix. Some ofthe modules had appeared in a package called Bitumen and Asphalt Nomographs Developed

by Shell (BANDS) (Koole, Valkering, & Stapel 1989), which is usually available within the Shellsoftware package.

In order to overcome the difficulties caused by the gap between the pavement design phaseand actual HMA production as described earlier, correlation equations were and are developedfor HMA property prediction. This approach helps the engineers to be able to assess themechanical properties of asphalt mixes from the basic physical properties of the ingredients andvolumetrics of the assumed HMA product. The method developed by Shell is well-known and itis applied and used by many European countries and Australia. It has been successfully utilisedby researchers and the industry for many infrastructure projects. The method used is based onthe work reported by Bonnaure et al. (1977) and it was developed on twelve typical asphaltsfrom the 1970s. The method was developed based on laboratory testing using a 2-pointbending apparatus for trapezoidal specimens. Twelve typical formulations of asphalt mixes were

selected for the tests so as to cover a whole range of mixes for road, air-field and hydraulicapplications as follows:

five wearing course mixes comprising two asphaltic concretes, a German Gussasphalt, aBritish rolled asphalt and a British open-graded mix

five basecourse mixes, including coarse asphaltic concrete, gravel sand and bitumenstabilised sands

one asphalt grouting mix used in hydraulic structures and one filler/bitumen asphalt masticfor waterproofing (Bonnaure et al. 1977).

The mixes, vastly different in composition but all standard mixes for road applications in variouscountries were studied. The complex relationship obtained from the laboratory test series

formed the basis of the Smixdetermination as described in Equation 1.

; ; (1)where

Smix = bitumen stiffness (measured or obtained from the van der Poelnomograph)

Vbit = percentage by volume of the binder in the mix

Vagg= = percentage by volume of the mineral aggregate in the mix.


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Bonnaure and his co-authors experienced the limitations of the method (the Shell method) andnoted that the prediction model provided would not completely replace the laboratorymeasurement, but provides paving technologists with a fairly good approach to stress and straindistribution calculations in actual pavements. It should be noted that the determination of Sbitalso has its limitations, as the van der Poel nomographs are only valid for normal bitumenbinders. Since newly developed asphalt types, like open-graded asphalt (OGA), split masticasphalt (SMA) or dense graded asphalt (DGA) for heavy duty application could not be includedin the research work, the overall applicability of the method is limited. In detailed validation work,the measured stiffness (indirect tensile strain method) was compared with the predictedstiffness using the Shell method for 157 asphalt mixes (Bocz 2008). It was proven that there is agood correlation between the predicted and measured stiffness for DGA with normal bitumenapplication, but the correlation was found to be very poor for SMA and DGA asphalt with PMBbinders.

Pavement temperature variation in the Shell method

SPDM is the main framework for pavement modelling and design; SPDM utilises thesub-module of BANDS to calculate the mix stiffness, based on the estimated pavementtemperature. For pavement design purposes a procedure had been developed to derive theweighted mean annual air temperature (WMAAT) from mean monthly air temperatures (MMAT)

for a given location; the latter is usually readily available from local meteorological data records.The WMAAT is converted then to an asphalt mix temperature (Tmix) using the relationship asgiven in Figure 1.

Figure 1: Relationship between effective asphalt temperature and MMAT or WMAAT(reproduced based on Valkering & Stapel 1992)

The SPDM software takes into account the stiffness change due to the temperature influence;however, this only occurs while iterating the entire pavement thickness, and only adjusts theimpact of thickness change on the average pavement temperature. Unfortunately, it does notcalculate the modulus of each layer, and does not take into account the different performancebetween asphalt layers at different depths. Also, as explained earlier, the S mixstiffnessprediction model is not valid for all types of asphalt.

It can be seen that the Shell method provides a good estimate for pavement design purposes,and it is able to overcome the limits of the computation technology of the late 80s early 90s. The

computation power and technology available today would allow a more detailed and

0

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10

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20

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30

35

40

45

50

0 5 10 15 20 25 30

Mixtemperature,

Tmix(C)

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Asphaltthicknessh1 mm

50

100

400

600


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sophisticated approach for the calculation of the pavement response under loading. The Shellapproach is considered extremely helpful; however, more accurate methods are available forstiffness prediction for an enhanced pavement design, as explained in this paper.

Asphalt stiffness prediction using the Witczak equation

Different asphalt stiffness prediction methods were developed since Shell published its method.

The Hirsch model (Christensen, Pellinen & Bonaquist 2003) and the Witczak model are widelyaccepted and implemented. Over the past 35 years, the Witczak equation has become one ofthe most rational and comprehensive forms of predictive models available in the literature(Witczak 2005). The Witczak equation is a continuously developing model, where the new testresults are included in the multi-regression analysis, and the constants of the original equationsare continuously updated. The recent version of the equation has been developed based on 205mixes, 171 with unmodified asphalt binders, 34 with modified binders, and results are still beingcollected and the equation parameters are continuously updated accordingly. The goodness offit is represented by R

2= 0.96, provided by tests conducted at 0 to 130 F temperature range

and tested on 39 aggregate types. The method is often referred to as the Witczak PredictiveEquation (WPE) and it is shown in Equation 2.

log|

| 1.249937 0.029230.001767 (2)

0.002841p 0.058097V0.802208 VV V

3.871977 0.0021 0.003958 0.000017 0.00547

1 ...

where

E* = dynamic modulus, 10 psi

= bitumen viscosity, 10Poisef = loading frequency, Hz

Va = air void content, %

Vbeff = effective bitumen content, % by volume

p34 = cumulative % retained on the inch (19 mm) sieve

p38 = cumulative % retained on the 3/8 inch (9.5 mm) sieve

p4 = cumulative % retained on the No. 4 (4.76 mm) sieve

p200 = % passing the No. 200 (0.075 mm) sieve.

Since this model applies readily available asphalt properties from the design and productionstages, and also utilises the readily available bitumen viscosity, the Witczak equation wasadopted in this study. Binders in Australia are continuously tested and assessed in standardlaboratory procedures as part of the regular production control, and therefore reliable data setsare available. The parameters in Equation 2 used in this study are published in the NCHRPReport 547 (Witczak 2005). In this paper the term stiffness will be used instead of the dynamicmodulus to characterise the asphalt material.

As explained earlier, the primary aim of this study is to provide an insight into the temperatureimpact on the asphalt stiffness variability. The impact of the temperature on the asphalt mixstiffness is included in Equation 2 indirectly, and it is introduced through the viscosity change of

the binder.


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The impact of aggregated pavement temperature on the performanceassessment

The single temperature value at which HMA mixtures can be evaluated has been termed theeffective temperature in the literature (Fugro Consultants 2011). Effective temperature (Teff) canbe defined as a single test or design temperature at which the amount of a given distress withina given pavement system, would be equivalent to that which would occur from the seasonal

temperature fluctuation throughout the annual temperature cycle. The initial Teffapproachconsidered a comprehensive analytical framework involving a factorial of environmentallocations, effective depths to compute the Teffvalue, pavement structural system, various mixproperties and responses, tyre pressures, and models of permanent strain behaviour in asphaltmixtures (Fugro Consultants 2011). It should be noted that utilising a single value in thepavement performance assessment (a discrete Teffvalue) was desirable in order to limit thenumber of calculations due to limited computation power. It should also be noted that some ofthe properties and part of the performance are hidden behind a certain Teffvalue, and do notprovide direct insight into the variability of the material property (stiffness in this case).

For a particular pavement section, the air temperature is commonly used to arrive at an effectivepavement temperature to permit the determination of the stiffness of the asphalt bound materialfor pavement design purposes. Equation 3, developed originally by Witczak (1972), had been

used for a long time to estimate the pavement temperature at different depths.

1 1 4 34

4 6(3)

where

MMPT = mean monthly pavement temperature (F)

MMAT = mean monthly air temperature (F)

z = depth below the pavement surface (inch) (Shook et al.1982).

Equation 3 was developed on the detailed pavement temperature profile measurement from1966-67, which was set up at Potsdam, New York, Clarkson College of Technology. Thisresearch emphasised that solar radiation (radiation) has a greater influence on heat flow in thepavement than air temperature (convection) for the increasing or decreasing of bituminouspavement temperatures (Straub, Schenck, & Przybycien 1968). This highlights the limitations ofpavement temperature predictions from air temperatures. It also should be noted that airtemperature records are readily accessible, and this option forced researchers into the directionof predicting pavement temperatures from average air temperatures. However, it is well knowthat this approach provides data loss and has an impact on the accuracy. An improvedprediction would be possible if accurately measured pavement surface temperatures could beobtained; however, such a data set is not readily available and requires special setup and dataacquisition. The most advanced and accurate prediction is possible through detailed air

temperature and solar radiation data analysis. In recent years there have been many attemptsto develop validated pavement temperature distribution from measured air temperature andsolar radiation data (Sun, Jia, & Qin 2006; Solaimanian & Kennedy 1994).

The effective temperature approach was recently revised as part of the NCHRP Project 1-37A.Effective temperature for fatigue cracking (Teff(FC)) was developed from the observation ofLTPP sections, where sections were used to obtain varying levels of monthly based fatiguecracking from the Superpave fatigue cracking model. The approach used was to computeaverage monthly fatigue cracking and attempt to find a corresponding single temperature thatresulted in the equivalent amount of the average fatigue cracking (El-Basyouny & Jeong 2009;Fugro Consultants 2011) The revised effective temperature model is a function of thefrequency, MMAT, -MMAT, wind, sunshine and rain; however, because the effectivetemperature is an equivalent temperature value, it cannot be used to predict pavement

temperature profiles; therefore its use was not considered in this study. It should be noted that


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effective temperature was also developed for permanent deformation (Teff(PD)) within theNCHRP Project 1-37A, which was also based on field observations (Fugro Consultants 2011).

Weighted mean annual pavement temperature (WMAPT)

The weighted mean annual pavement temperature (WMAPT) is used in the Australianpavement design system to adjust the in-service modulus from the measured modulus in the

laboratory. The WMAPT value is derived from the weighted monthly average air temperaturesfollowing the SPDM method (Austroads 2012).

The primary aim of this study is to highlight the paramount importance of temperature impact onthe asphalt stiffness within the pavement structure; therefore the application of a singlepavement temperature value was not sufficient enough for the analysis provided in this study.On the other hand, as explained earlier, it is believed that the air temperature alone is notenough to derive pavement temperatures, since the pavement temperature is influenced moreby the solar radiation. It should be emphasised that by applying a single temperature value(such as WMAPT) it is considered accurate enough to perform pavement design and theutilisation of such a temperature value works well in pavement design systems , since they arevalidated by detailed long-term observations.

Although this method provides an appropriate approach in pavement engineering for generalapplication, pavement engineers are always facing the challenge of extrapolating beyond theexisting knowledge, and predict future pavement behaviour with increased traffic and/orimproved material compositions. The general mechanistic procedure (GMP) provides anexcellent basic tool for this assessment (Austroads 2012). The level of confidence might beincreased by providing more detailed material characteristics as an input for the pavementdesign by eliminating the average material properties and a more realistic performanceprediction could be developed. Powerful computation devices are now available to performthese calculations; however, sometimes the lack of input information limits the detailedassessment, as described later.

Detailed pavement temperature profiles

As described earlier, a simplified pavement temperature profile (or a single value) cannotprovide a basis for a detailed performance assessment of asphalt materials. However, based ondetailed pavement temperature profile, it is possible to construct the asphalt stiffness distributionfor a better understanding of the material variability due to temperature factors.

Detailed pavement temperature measurement was conducted in Australia in the 1970s and theresults are published in a series of documents (Dickinson 1981). Unfortunately the publicationsprovide analysed data focusing mainly on minimum and maximum pavement temperatures, andwith limited cumulative pavement temperature distribution. The recorded data set would beessential for the detailed analysis provided in this paper; however, the source data for thesereports is rumoured to have been purged in the course of an earlier mainframe computerupgrade (Rickards 2011). The document which summarises the Australian pavement

temperature measurement (Dickinson 1981) and the background documents for each Australiancapital city (Dickinson 1971; Dickinson 1975; Dunstan 1967) provide a general analysis of therecorded temperatures. Histograms with wide range bins (6 C) are available at limited depths,but these analyses unfortunately do not contain enough information to produce the probabilitymass functions.

In order to highlight the importance of the temperature variability within the pavement structure,it was decided to utilise detailed pavement temperature profiles recorded in the Central-European climate, because such a detailed temperature profile could not be obtained for theAustralian climate. The author had the opportunity to establish a temperature measurementdevice in 2006 on the access road of a major asphalt mix plant in Budapest. The devicemeasured the air temperature and the pavement temperature at 0 cm, -2 cm, -7 cm, -14 cm, -29 cm and -49 cm from August 2006 to July 2007. The frequency of the temperature

measurement was 10 minutes, and the accuracy of the output was 0.1 C (Petho 2008). This


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Table 1: Parameters used in the MCS

Property, input data Distribution,variables

Value Source

Asphalt type (median envelope) constant, DG 20 N/A MRTS 301

p34 (3/4 inch sieve = 19 mm)(cumulative per-cent retained %)

constant 5.0

p38 (3/8 inch sieve = 9.51 mm)(cumulative per-cent retained %)

constant 32.0

p4 (No4 sieve = 4.76 mm)(cumulative per-cent retained %)

constant 50.5

p200 (No200 sieve = 0.074 mm)(per-cent passing %)

constant 5.5

Vavoid content (%) constant 5.0

Vbeffbinder content (% by volume) constant 10.0

f frequency (Hz) constant 10.0 AGPT, T233 (4PB)

Binder type C320 N/A MRTS 301

C600 N/A

Temperature (C) Normal distribution, Brisbaneand Canberra, hot season200 mm

Pavement temperatureprofile measurement,1970s

Normal distribution, Brisbaneand Canberra cold season200 mm

Pavement temperatureprofile measurement,1970s

log viscosity (10^6 Poise) Normal distribution throughtemperature value

Standard laboratorytesting; refer to Table 2

Stiffness-WPE (MPa) Variable WPE

Note 1: Department of Transport and Main Roads (2010).

As described in Equation 2, the stiffness prediction greatly depends on the viscosity andindirectly on the temperature. Two common types of bitumen, namely C320 and C600 weretested, and viscosity values were measured at 25, 45, 60 and 135 C, and these two bindertypes were taken into consideration in the MCS. Regression analysis was performed and thetemperature dependency of the viscosity can be described by the equations provided in Table 2.It should be noted that based on production control data, probability mass functions could bedeveloped for bitumen viscosity which could be applied in a detailed analysis. In this study nodata was available to develop such probability mass functions for bitumen viscosity. Therefore

deterministic viscosity values obtained from laboratory testing performed by ARRB were used inthe calculations. Viscosity was measured at 25, 45, 60 and 135 C; the correlation equations are

provided in Table 2, where represents the bitumen viscosity in Pa.s and T is the actualtemperature in C. The correlation equations in Table 2 were applied in the analysis.

Table 2: Viscosity model equations adopted in the WPE calculation

Binder type Equation R2

C320 = 1E+17 * T-8.151

R = 0.9977

C600 = 4E+17 * T-8.347

R = 0.9982


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It should be noted that it is unlikely that the pavement temperature has a normal distribution andthe pavement temperature profile is likely to be described by lognormal distribution as shown inFigure 2, Figure 3 and Figure 4. In order to show the impact of the temperature distribution onthe asphalt stiffness in the Australian climate, the data reported by Dickinson (1981) was chosenas described in Table 3. In the series of pavement temperature measurement reports (Dickinson1981) mean and standard deviation values were only provided for two locations, namelyBrisbane and Canberra. Unfortunately the analysis provided mean and standard deviationvalues only for the hot season and the cold season and not for the whole year. Although thereports provided pavement temperature mean and standard deviation values for the surface,50 mm, 100 mm and 200 mm depth it was decided to run the calculations only for 200 mmdepth. For the demonstration the same DG20 asphalt was utilised for the Canberra region.

Table 3: Temperature distribution parameters, based on real measurement

Pavement profile Brisbane Brisbane Canberra Canberra

Mean Std. Dev. Mean Std. Dev.

Mean maximum temperature, hotseason, 200 mm deep (C)

43.2 3.4 34.6 3.1

Mean minimum temperature, cold

season, 200 mm deep (C) 19.4 1.4 7.8 1.9

Oracle Crystal Ball, Fusion edition (2011) was used for performing the MCS. Oracle Crystal Ballis auxiliary software running under the framework of MS Excel. It was decided to run 100,000calculations in each simulation, which approximately took 1 minute for each run. The predictedasphalt stiffness values for the hot season are summarised in Figure 5 and for the cold seasonin Figure 6 based on the Witczak equation (Equation 2) and the inputs presented in Table 1,Table 2 and Table 3.

Figure 5: Asphalt stiffness distribution at 200 mm depth in the pavement, Brisbane andCanberra climate, based on hot season values

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000

Relativedistributionof100,0

00prediction(%)

Stiffness (MPa)

DG20 C320 Brisbane hotseason 200 mm

DG20 C600 Brisbane hotseason 200 mm

DG20 C320 Canberrahot season 200 mm

DG20 C600 Canberrahot season 200 mm


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Figure 6: Asphalt stiffness distribution at 200 mm depth in the pavement, Brisbane andCanberra climate, based on cold season values

For comparison Table 4 summarises the calculated stiffness values at WMAPT for the Brisbaneregion and Canberra based on the Witczak equation (Equation 2) and the inputs presented inTable 1 and Table 2. The WMAPT values are determined according to Austroads 2012.

Table 4: Calculated stiffness at WMAPT temperature using different binder types

Location Brisbaneregion

Canberra

WMAPT (C) 32.0 23.0

Calculated stiffness at WMAPT temperature using

C320 binder (MPa) 2,685 5,951

Calculated stiffness at WMAPT temperature using

C600 binder (MPa)3,364 7,234

The stiffness of the top asphalt layer ranges between approximately 500 MPa and 20,000 MPafor the Brisbane region and it lies approximately between 1,000 MPa and 30,000 MPa for theCanberra region. The stiffness of the asphalt layer derived from the WMAPT provides a singlevalue, which is rather closer to the lower boundaries. It can also be seen that in the sameclimatic region the stiffness is higher for the lower layers. This difference may provide input to animproved pavement design, where the higher stiffness values of the lower layers could be takeninto account, resulting in a more realistic pavement modelling.

Unfortunately, without a continuous pavement temperature profile, a reliable assumption cannotbe made for the stiffness distribution in each layer within the pavement structure. Figure 5 andFigure 6 give a good indication of the achievable minimum and maximum stiffness within thepavement structure, but cannot replicate the real stiffness distribution over a full year cycle. As itcan be seen on Figure 5 and Figure 6, the stiffness distribution for the entire year would bemore desirable, since there is a wide gap between these minimum and maximum values, andthere is still a lack of knowledge of what is the real stiffness distribution throughout the year. Theaccuracy of the stiffness prediction provided in Figure 5 and Figure 6 is influenced therefore bythe input temperature values, and it is most likely that these results define the upper and lowerboundaries of the real stiffness distribution, providing the relative distribution of minimum andmaximum values, but do not provide the real distribution for the entire population.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5,000 10,000 15,000 20,000 25,000 30,000 35,000

Relativedistributionof100,000prediction(%)

Stiffness (MPa)

DG20 C320Brisbane coldseason 200mm

DG20 C600Brisbane coldseason 200mm

DG20 C320Canberra cold

season 200mm

DG20 C600Canberra coldseason 200mm


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Using Equation 4, pavement temperature was predicted at 16 cm depth for the Melbourne area,based on air temperature and solar radiation data from 2008. The asphalt stiffness distributioncould be predicted only for the Melbourne area, since this is the only location where all thenecessary data (air temperature, global radiation, regression coefficients) are available (Table4). The R

2value of the model provided in Equation 4 ranges from 0.924 to 0.975.

(4)

where

Tp = pavement temperature at H cm, C

Ta = air temperature at this time, C

Q = solar radiation at this time, kW/m

Ta5 = average air temperature for previous 5 hours, C

Q5 = average solar radiation for previous 5 hours, kW/m

H = predicted depth, cm

p1- p8 = regression coefficients for the prediction model, depending on thelocation (latitude).

Table 5: Summary of the available air temperature, solar radiation and regressioncoefficients

Location BOMSiteNo.

Latitude 10 minutesair

temperaturedata

30 minutessolar radiation

data

Equivalent location (basedon latitude)

2008

2010

2008

2009

2010

Latitude Location

Darwin 014015 S 12

Dataavailable

Data available Coefficients not available

Brisbane 040913 S 27 Data notavailable

Coefficients not available

Perth 009021 S 31 Data notavailable

N 31 Shanghai

Sydney 066195 S 33 Data not

available

Coefficients not available

Adelaide 023034 S 34 Data available Coefficients not available

Melbourne 086282 S 37 Data available N 37 Tangshan

Hobart 094008 S 42 Data notavailable

N 43 Urumqi

Although in Victoria dense graded asphalt mixes have different properties as provided in Table1, for this analysis the same dense graded asphalt type (DG20) was used as in the previousanalysis provided in this paper. This approach allows keeping this demonstration simple.Asphalt stiffness distribution was calculated based on the predicted pavement temperature at 16

cm depth and the results are summarised in Figure 11. It should be noted that the predictedasphalt stiffness values are not validated, but they are in the expected range.


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Figure 11: Asphalt stiffness at 16 cm from predicted pavement temperature, based on airtemperature and global radiation data from 2008 in Melbourne

Summary and opportunities for further research

The aim of this paper was to assess the impact of temperature distribution within the pavementstructure on asphalt stiffness variation. The real asphalt performance is influenced by variationin the mix composition and temperature at the same time. However, this study focused primarilyon the influences of the pavement temperature and considered that the mix composition wasconstant. This was necessary to avoid the overshadowing effect of the variation of the physicalproperties.

The results provided in the paper highlight the importance of the careful material selection in

pavement design and construction. In order to assess the impact of temperature variation in theAustralian climate, detailed pavement temperature measurement would be necessary. Based onsuch a comprehensive temperature record, the performance assessment of HMA in differentclimatic conditions would be relatively simple through the use of MCS. However, no such datafor Australia was identified for this study.

This paper highlighted that a relatively simple model can be developed for locations at differentlatitudes to predict pavement temperature at different depths. The model would be based on airtemperature and solar radiation readings. In Australia the Bureau of Meteorology providesglobal solar radiation and air temperature data. A relatively inexpensive option for Australiawould be to set up three measurement stations, one each in Darwin (S12), Brisbane (S27) andMelbourne (S37) and record the pavement temperature profiles throughout a calendar year.Based on the measured temperature the prediction model referenced in this paper could be

improved and validated. Such an improved and validated model would deliver pavementtemperature profiles throughout Australia at very low costs for any sites where detailed airtemperatures and solar radiation data are available. Consequently, more realistic pavementmodelling would be possible.

The probability mass functions shown in the paper also highlight that there might be anunderestimate in the asphalt pavement capacity, if the pavement design is based on minimumachievable asphalt stiffness values derived from WMAPT.

The MCS is an outstanding tool in assessing the impact of the variation in the asphaltproduction as well. It is possible to include all the different variables which have an impact onthe asphalt stiffness. In this study the physical properties of the asphalt mix were kept constantthrough the simulations in order to be able to assess the temperature impact on the stiffness

distribution. It would be also possible to keep the temperature value constant and include the

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 5,000 10,000 15,000 20,000

Relativedistributionof100,000prediction(%)

Stiffness (MPa)

DG20 C320 Melbourne 16 cm,predicted (not validated)


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variation of the grading, binder content and air void content. Based on the analysis of historicaldata it is possible to derive the real distribution of each asphalt property and assess the impactof the production variation on the asphalt performance through the WPE. Based on historicaldata analysis, the impact of the production variability could be checked for asphalt mixesproduced in Australia. This would open opportunities to focus on the most important parameters,which influence most of the asphalt mechanical properties.

The paramount advantage of combining the WPE calculation in MCS is that a desktop analysiscould be performed in a relatively simple way for the environmental impact on the asphaltstiffness change. The hardening effect could be easily included in the WPE approach byadjusting the viscosity change of the binder due to environmental effects. This analysis wouldprovide a comprehensive understanding of the asphalt material performance under in-serviceconditions.

REFERENCES

Austroads (2012), Guide to pavement technology: part 2: pavement structural design, by GWJameson,AGPT02/12, Austroads, Sydney, NSW

Bocz, P. (2008), Correlation between the mechanical properties of asphalt mixes and the

remaining life of the pavement structure, PhD thesis, Budapest University of Technology,Budapest, Hungary\

Bonneaure, F., Gest, G., Gravois, A. and Ug, P. (1977), A new method for predicting thestiffness of asphalt mixtures, Proceedings of the Association of Asphalt Paving Technologists,vol. 46, pp. 64-104

Christensen, D.W., Pellinen, T.K. and Bonaquist, R.F. (2003), Hirsch model for estimating themodulus of asphalt concrete, Journal of the Association of Asphalt Paving Technologists,vol.72, pp. 97-121

Department of Transport and Main Roads (2010), Dense graded and open graded asphalt, MainRoads technical standard, MRTS 30, DTMR, Bribane, Qld

Dickinson, E.J. (1971), Temperature conditions in bituminous surfacings at a site near Perthduring a period of one year, Australian Road Research, vol.4, no.7, pp.33-6

Dickinson, E.J. (1975), Temperature conditions in bituminous concrete pavements at a site nearBrisbane during a period of one year,Australian Road Research, vol.5, no.8, pp.9-15

Dickinson, E.J. (1981), Pavement temperature regimes in Australia, special report SR 23,Australian Road Research Board, Vermont South, Vic

Dunstan, D.G. (1967), Temperature variations in a bituminous concrete surfacing at a site nearMelbourne,Australian Road Research, vol.3, no.3, pp.3-11

El-Basyouny, M. and Jeong, M.G. (2009), Effective temperature for analysis of permanentdeformation and fatigue distress on asphalt mixtures, Transportation Research Record,no.2127, pp.155-63

Fugro Consultants (2011),A performance-related specification for hot-mixed asphalt, NCHRP704, Transportation Research Board, Washington, DC, USA

Koole, R., Valkering, C. and Stapel, F. (1989), Development of a pavement design program foruse on a personal computer; 5th Conference of Asphalt Pavements for South Africa(CAPSA89), Swaziland, CAPSA, South Africa, vol. II, pp.33 -43

Oracle (2011), Oracle crystal ball, Fusion edition, Oracle, Redwood Shores, CA, USA


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Petho, L. (2008), Influence of temperature distribution on the design of pavement structures,Eurasphalt and Eurobitume congress, 4th, 2008, Copenhagen, Denmark, European AsphaltPavement Association (EAPA), Brussels, Belgium, pp.758-68

Rickards, I. (2011), Master class in flexible pavements and AAPA project overview: asphaltpavement solutions for life, 16 September 2011, Ian Rickards Consultant, Australia, viewed 16April 2012,

http://c1.bbsweb.net.au/~aapaasn/cms_files/Final_Overview_Asphalt_Pavement_Solutions_%20For_Life_Project.pdf.

Shell (1978), Shell pavement design manual: asphalt pavement and overlays for road traffic, Shell International Petroleum Company Ltd, London, UK

Shook, J.F., Finn, F.N., Witczak, M.W. and Monismith, C.L. (1982), Thickness design of asphaltpavements: The Asphalt Institute method, International conference on asphalt pavements, 5

th,

1982, Delft, Netherlands, International Society for Asphalt Pavements, Minnesota, USA, pp.17-45

Solaimanian, M. and Kennedy, T. (1994), Predicting maximum pavement temperature usingmaximum air temperature and hourly solar radiation, Transportation Research Record,

no.1417, pp.1-11

Straub, A.L., Schenck, H.N. and Przybycien, F.E. (1968), Bituminous pavement temperaturerelated to climate, Highway Research Board, no 256, pp.53-77

Sun, L., Jia, L. and Qin, J. (2006), Temperature distribution prediction model for asphaltpavements, International conference on asphalt pavements, 10th, 2006, Quebec City, Canada,International Society for Asphalt Pavements, Minnesotta, USA, vol.2, pp.25-34

Toth, C. (2010), Analysis of the quality variances of asphalt production by Monte Carlosimulation, Periodica Polytechnica, Civil Engineering, vol. 5, no.1, pp.67-72

Valkering, C. and Stapel, F. (1992), The Shell pavement design method on personal computer,

International conference on asphalt pavements, 7th

, 1992, Nottingham, UK, International Societyfor Asphalt Pavements, Minnesotta, USA, pp.351-74

Witczak, M.W. (1972), Design of full-depth asphalt airfield pavements, International conferenceon the structural design of asphalt asphalt pavements,3

rd, 1972, London, UK, University of

Michigan, Department of Civil Engineering, Ann Arbor, Minnesotta, USA, pp.550-67

Witczak, M. (2005), Simple performance tests: summary of recommended methods anddatabase, NCHRP 547, Transportation Research Board, Washington, DC, USA

AUTHOR BIOGRAPHIES

Laszlo Petho is a graduate of the University of Technology, Budapest (BME) earning his PhD inpavement design and asphalt technology. After five years experience in road construction andquality control he worked as a researcher and lecturer at the Department of Highway andRailway Engineering of the BME. He conducted research projects on developing highperformance asphalt mixes for heavy duty pavements and performance-based asphalt mixcharacterisation for pavement design purposes. He gained experience in detailed pavementdesign, pavement performance assessment and in situ and laboratory material testing. Laszlo isa Chartered Professional Engineer of Engineers Australia, Active Member of the Association ofAsphalt Paving Technologists (AAPT), and the International Society for Asphalt Pavements(ISAP). He is currently the technical project leader for Austroads Asphalt properties and mixdesign procedures project.
http://c1.bbsweb.net.au/~aapaasn/cms_files/Final_Overview_Asphalt_Pavement_Solutions_%20For_Life_Project.pdfhttp://c1.bbsweb.net.au/~aapaasn/cms_files/Final_Overview_Asphalt_Pavement_Solutions_%20For_Life_Project.pdfhttp://c1.bbsweb.net.au/~aapaasn/cms_files/Final_Overview_Asphalt_Pavement_Solutions_%20For_Life_Project.pdfhttp://c1.bbsweb.net.au/~aapaasn/cms_files/Final_Overview_Asphalt_Pavement_Solutions_%20For_Life_Project.pdf


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Stiffness Variability