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The Development of HDM-4 TechnologyRoad Deterioration Models for Australias

Sealed Granular Pavements

Martin, T.C.Principal Research EngineerARRB Transport Research

Toole, T.Manager Asset ManagementARRB Transport Research

Oliver, J.W.H.Chief Research ScientistARRB Transport Research

SYNOPSIS

Road agencies in Australia are adapting HDM-4 technology to the strategic management of their roadnetworks. There is a need to develop HDM-4 technology road deterioration (RD) models for sealed granularpavements as they comprise 95% of Australias rural arterial roads. Austroads has funded ARRB TransportResearch (ARRB) since 1994 to adapt HDM-4 technology to Australian conditions.

Since 1994 ARRB has monitored long term pavement performance (LTPP) sites to observe roaddeterioration with traffic loading, climate and pavement type. From 1998 onwards, ARRB has alsomonitored the influence of maintenance on sealed granular pavement performance using long termpavement performance maintenance (LTPPM) sites on Australias arterial roads, specifically varying surfacemaintenance treatments at each site. In addition, ARRB, in an independent consulting capacity, hasperformed field data-driven RD model calibrations for a number of States.

Accelerated load testing (ALT) of experimental sealed granular pavements commenced in 1999 to quantifythe influence of maintenance on relative pavement performance, under controlled conditions of loading,climate and maintenance. In 2003 ALT was used to quantify the influence of increased axle mass loadingon pavement deterioration.

Historical Australian seal life and binder hardening data was available to ARRB to develop a refined binderhardening model using variables for environmental conditions, elapsed time, binder characteristics and thenominal seal size. This refined model, in conjunction with an existing distress viscosity model, allowed thedevelopment of an explicit seal life model to predict the expected life of different nominal seal sizes indifferent climates throughout Australia.

This paper presents the current state-of-the-art characteristics, by means of re-calibrated default coefficients,for the RD models for roughness and rutting progression which were shown to vary with the environmentand surface maintenance treatments. The RD model re-calibration used the observational data from theLTPP and LTPPM sites in conjunction with the relative performance factors estimated for various surfacemaintenance treatments from the ALT data. The current imitations of these revised models with the abovesealed granular pavement data are stated, including those for the seal life model. As a result, the RDmodels are more responsive to changes in maintenance under a range of Australian climatic conditions.

It was not possible to predict the impact of surface maintenance treatments, time and traffic on the structuraldeterioration of sealed granular pavements. Consequently, the HDM-4 structural deterioration model forsealed granular pavements could not be re-calibrated. This outcome suggests that modifications to theHDM-4 structural deterioration model are needed for sealed granular pavements.

6th International Conference on Managing Pavements (2004)

TRB Committee AFD10 on Pavement Management Systems is providing the information contained herein for use by individual practitionersin state and local transportation agencies, researchers in academic institutions, and other members of the transportation research

community. The information in this paper was taken directly from the submission of the author(s).

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INTRODUCTION

Application of HDM-4 Technology in AustraliaRoad agencies in Australia are adapting HDM-4 technology to the strategic and program management oftheir road networks. Consequently, there is a need to develop HDM-4 technology that includes roaddeterioration (RD) models for sealed granular pavements as they comprise 95% of Australias rural arterialroads (Oliver 1999, ABS 2001). Austroads, Australias peak road agency organisation, has funded ARRBTransport Research (ARRB) since 1994 to adapt HDM-4 technology to Australian conditions through various

research initiatives aimed at quantifying and predicting long term pavement performance.

This paper presents the state-of-the-art of the re-calibrated RD models and a refined binder (seal) life modelthat stem from the Austroads funded research and ARRBs independent consulting work.

Austroads and Other Research InitiativesThe Austroads and other research initiatives comprise the following:

1. The monitoring of pavement deterioration (rutting, roughness, strength and cracking) of eight sealedgranular pavements at long term pavement performance maintenance (LTPPM) sites since 1998 undervarious conditions of traffic loading, climate, pavement strength and maintenance treatment (Tepper andMartin 2001). These sites are located in Victoria (4), New South Wales (1), Queensland (2) andTasmania (1).

2. The monitoring of pavement deterioration (rutting, roughness, strength and cracking) of five sealedgranular pavements at long term pavement performance (LTPP) sites since 1994 under variousconditions of traffic loading, climate and pavement strength (Tepperet al. 2002). These sites are locatedin Victoria (1), New South Wales (3) and Queensland (1).

3. Accelerated load testing (ALT) of sealed granular pavements aimed at quantifying the relativedeterioration performance of various surface maintenance treatments under controlled conditions ofloading and climate (Martin and Gleeson 1999, Martin et al. 2000, Martin et al. 2001). The majority ofthis testing was conducted within a large testing enclosure using ARRBs Accelerated Loading Facility,ALF (McLean 1985), at Dandenong, Victoria.

4. ALT aimed at quantifying the relative effect on deterioration performance of increased axle loads onthree types of sealed granular pavements under controlled loading and climatic conditions (Yeo and Koh2003). As for the above, this testing was conducted within the large testing enclosure at Dandenong,

Victoria.

5. Consulting work, independent of Austroads funding, calibrating HDM-4 RD models for Vicroads and theDepartment of Infrastructure, Energy and Resources (DIER), Tasmania, that are the Victorian andTasmanian State Road Agencies, respectively. This work involved use of the Agencies historicalpavement deterioration data (Martin and Toole 2003, Toole et al. 2004).

6. An existing binder hardening model, in combination with an existing distress viscosity model, thatpredicted the life of sprayed bituminous seals (Oliver 1990) for sealed granular pavements was modifiedto account for the size of the stone aggregate (Oliver 2003). For bituminous seals, the stone aggregatesize is the nominal size of the seal. This binder hardening model modification was mainly based onprevious data (Oliver 1987) that included the aggregate stone size as an independent variable.

Outcomes of the InitiativesThe experimental and observational data collected under items (1) to (3) was used to re-calibrate the HDM-4RD models (Morosiuk et al. 2001) for rutting and roughness progression for Australian conditions (Martin2004a). The experimental work data collected under item (4) for further re-calibration of the HDM-4 RDmodels currently has not been finalised and cannot as yet be reported in any detail here for any further re-calibration (Martin 2004b).

The results of the re-calibration of the HDM-4 RD models under item (5) offer an opportunity for comparisonof the results of the above re-calibration for Australian conditions.

The refinement of an existing binder hardening model, in combination with a distress viscosity model, underitem (6) with further review of the actual seal lives achieved under Australian conditions is likely to lead to the




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use of this model to predict seal life in Australia with some degree of confidence, particularly for lowertrafficked rural roads. Bitumen oxidation, rather than traffic effects largely influence the bitumen seal life onthese roads, which causes embrittlement leading to surface cracking. The initiation of surface cracking isconsidered to be the limit of seal life in Australia.

RE-CALIBRATION OF HDM-4 RD MODELS FOR AUSTRALIA

Use of ALT Data

For the ALT experiments the relative effects of various maintenance treatments were quantified in terms ofrelative performance factors for rutting and roughness deterioration (progression) on sealed granularpavement deterioration. These relative performance factors, in conjunction with the longer term deteriorationobserved at the LTPPM and LTPP sites under varying conditions of maintenance, environment and loading,provided a basis for the calibration of the HDM-4 road deterioration (RD) models for rutting and roughnessunder Australian conditions.

However, calibration of the HDM-4 RD models using experimental and limited observational data has thelimitation that the model predictions can vary from the outcomes found for pavements in practice (Robinson2003). This is because of the increased variability in these pavements relative to those used for theexperimental and observational data. The calibrated RD models may not always be relevant to pavementswith a much greater range in strength than those used to develop the calibrated RD models. Theselimitations are common to most current research on pavement deterioration modelling.

Experiment SummaryA summary of the ALT experiments is shown in Table 1. A nominal, or initial, value of the adjusted structuralnumber, SNP, was estimated for the pavement/subgrade strength of each experiment after the initialbedding in loading of 9000 accelerated load cycles at 40 kN (1 ESA) per load cycle. SNP was estimatedthroughout the accelerated loading, from Heavy Weight Deflectometer (HWD) deflections, D0 and D900, usinga relationship developed by Roberts (1995).

Table 1 Summary of experiments

Exp.No.

Description NominalSNP

SNP

Std. Dev.3

RDS/Rut4

Range5

Initial6

Average

#1 Single seal cracked (wet)1

4.9 0.45 0.4 1 0.4 0.7

#2 Single seal uncracked (wet)1

6.7 0.6 0.2 1.3 0.4 0.5

#3 Geotextile seal uncracked(wet)

1

5.0 0.4 0.2 0.6 0.5 0.4

1. Single seal cracked (wet)2

4.2 0.1 0.1 0.3 0.3 0.2

2. Single seal uncracked (wet)2

4.1 0.2 0.3 0.3 0.3

3. Single seal uncracked (dry)2

4.6 0.1 0.2 0.2 0.2

4. Double seal uncracked (wet)2

3.8 0.2 0.4 0.7 0.4 0.5

4A. Double seal uncracked (wet)2

3.7 0.1 0.3 0.5 0.3 0.4

5. Geotextile seal uncracked(wet)

2

4.5 0.2 0.2 0.3 0.2 0.3

5A Geotextile seal uncracked

(wet)2

4.0 0.2 0.2 0.4 0.2 0.3

6. Single seal uncracked (dry)2

4.6 0.2 0.3 0.4 0.4 0.3

7. Single seal uncracked (wet)2

4.2 0.2 0.1 0.4 0.2 0.2

8. Single seal cracked (wet)2

4.7 0.1 0.1 0.3 0.2 0.2

Note: 1. Experiments at Old Longwood on natural

subgrade.

4. Standard Deviation of Rut depth/Mean Rutdepth.

2. Experiments at Dandenong under controlledenvironment.

5. RDS/Rut estimated during the course of theexperiment.




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3. Standard Deviation of SNP along each testsection.

6. Initial RDS/Rut after bedding in.

Table 1 shows that the Old Longwood based experiments (#1, #2 and #3) all had higher values of SNP thanthe Dandenong based experiments (No.1 to No.8). In addition, the Old Longwood experiments had a higherstandard deviation for SNP than the Dandenong experiments. The values of RDS/Rut

1for the Old

Longwood experiments were greater than the Dandenong experiments except those conducted on thedouble seal experiments (No.4 and 4A).

The strength and RDS/Rut variations between the Old Longwood and Dandenong experiments stronglysuggested that these experiments were distinctly different and the data from these experiments wereconsequently kept separate in developing the relative effects of maintenance relationships.

Deterioration PhasesOn the basis of an extensive review of the data, the ALT deterioration data appeared to fall within thefollowing deterioration phases as identified in HDM-4 (Morosiuk et al. 2001):

an initial densification phase where the pavement undergoes permanent deformation and an increase instrength by compaction due to the accelerated load under the initial bedding in;

a gradual deterioration phase where the rate of deterioration is relatively uniform for a given pavementprior to the onset of the next phase. The pavement may, however, be cracked or uncracked at thisstage; and,

a rapid deterioration phase leading to the onset of abrupt failure.

Initial DensificationRut DensificationAll ALT experiments were subjected to an initial bedding in with the same accelerated loading, as notedearlier, under dry conditions. The change in rut depth was measured as a result of the bedding in loading.Only the Dandenong experiments were used to predict rut densification because the Old Longwood sealswere placed on an well established pavement where the densification was substantially less thanexperienced for the Dandenong pavements. The Dandenong experiments were grouped into two cases: (1)data falling into single seals, both cracked and uncracked; and, (2) data falling into uncracked two coat seals(double seals and geotextile seals).

0

4

8

12

16

20

3.5 4.0 4.5 5.0

SNP (nominal)

InitialRutDepth(mm)

Geotextile seal

Double Seal

Single Seal

HDM-4 Prediction

Single Seal (average)

Two Coat Seals(Double/Geotextile Sealsaverage)

(11.3)

(6.7)

Figure 1 Initial rut densification vs nominal SNP

Figure 1 shows the initial rutting densification data for the Dandenong experiments and the average valuesfor single seals and two coat seals. The average of the initial rut densification values were used for each of

1Standard Deviation of Rut Depth/Mean Rut Depth




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the above Dandenong cases as the relationships fitted to the data were not significant and the data showeda lot of scatter. The initial rut densification measured under ALT conditions is greater than that predicted byHDM-4 (Morosiuk et al. 2001) for a similar range of pavement/subgrade strength. The HDM-4 estimatesshown in Figure 1 assume 100% relative compaction which may be an over-estimate considering the natureof the Dandenong pavements.

Assuming that initial rut densification does not vary significantly with pavement/subgrade strength, assuggested by the ALT results, the relative impact of a two coat seal on initial rut densification is

approximately half (0.6) that of a single seal. Table 2 summarises these results for calibration of thecoefficients for initial rut densification, Krid, which assumes the Krid coefficient for a single seal remains at thedefault value of 1.0.

Table 2: Summary of Initial Rut Densification Calibration Coefficents

Range of SNP Krid (Single Seal)

Krid (Two Coat Seal)

3.7 4.7 1.0 0.6

Gradual DeteriorationLinear regression analysis of the rutting and roughness deterioration of the ALT data was used in developingstatistically significant relationships for the gradual deterioration phase of all the experiments summarised in

Table 1. The form of these relationships was as follows:

Rutting or Roughness = 1 + 2 independent variable (1)

where;Rutting or Roughness = rut value (mm) or roughness value (IRI, m/km) at a given point in time

1 = regression coefficients on y intercept (usually a minor contribution to thevariation in the relationship

2 = regression coefficients of independent variables.

Relative Performance Factors: Basis for EstimationThe approach used to estimate the performance of one form of maintenance treatment i relative to anothermaintenance treatment j was to divide the statistically based performance relationship for one form ofmaintenance by the statistically based performance relationship for the maintenance treatment that is beingrelated to. The relative performance factor is therefore calculated as follows:

Relative performance factor = 1 + 2 independent variable (treatment i) (2a)

3 + 4 independent variable (treatment j)

If the coefficients 1 and 3 are not statistically significant in the relationship then the relative performancefactor can be simplified as follows:

Relative performance factor 2 independent variable (treatment i) (2b)

4 independent variable (treatment j)where the coefficients above are as defined for equation (1).

In equation (2b) the y intercept regression coefficients, 1 and 3, of equation (2a) were also ignored if theydid not significantly contribute to the performance relationship. This was assessed by forcing a regressionanalysis through the origin using only one independent variable so that the goodness of fit, r

2, then

measures the proportion of the variability of the dependent variable explained by the regression analysis.Although the r

2found for the resulting relationship is not a true measure of its goodness of fit, it does assess

how much the independent variable coefficient influences the relationship.

The Relative Performance Factor for an Cracked Single Seal Relative to an Uncracked Single Seal ExampleAn example of the above methodology is shown below for the relative performance factor estimated forrutting of a cracked single seal relative to an uncracked single seal. Figure 2 shows the results of the ALTdata for two experiments with cracked single seals (Experiments No.1 and 8) and two experiments withuncracked single seals (Experiments No.2 and 7).




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The relationships in Figure 2 derived by linear regression for the cracked and uncracked single sealexperiments are both statistically significant and a reasonable fit to the experimental data. When theregression analyses are forced through the origin it was found that around 88% of the variation in rutting ofthe uncracked single seal was explained by the accelerated load, in millions of equivalent standard axles,MESA, while around 98% of the variation in rutting of the cracked single seal was explained by the load,MESA. This means that the simplified (2b) equation form can be used to estimate the relative performancefactor for rutting as follows:

Relative performance factor = 284 MESA (cracked single seal) = 1.7

162 MESA (uncracked single seal)

Summary of Relative Performance Factors (Gradual Deterioration)Table 3 summarises the relative performance factors, rpf, for rutting and roughness relative to an uncrackedsingle seal (ss) estimated for the various maintenance treatments. In order to extend the usefulness of theestimated relative performance factors from the ALT data, assumptions were made about the value of therelative performance factor (0.9) for a double seal relative to an uncracked single seal.

No relative performance factors were estimated for the deterioration of pavement strength with the variousmaintenance treatments. This was because for most treatments statistically significant relationships couldnot be derived from the ALT data for the loss of strength during loading. Even the cracked pavementssubject to a continuously wet surface were not observed to lose strength significantly with increased load.

0

10

20

30

40

50

0.00 0.02 0.04 0.06 0.08 0.10

MESA (ESA x 106)

M

eanRut(mm)

SNP = 4.7 (wet/cracked)

SNP = 4.2 (wet/cracked)

SNP = 4.2 (wet/uncracked)

SNP = 4.1 (wet/uncracked)

Cracked Seal Prediction

Uncracked Seal Prediction

Single Seals Cracked (wet)

Mean Rut = 7.1 + 284 x MESA (r2

= 0.99)

Single Seals Uncracked (wet)

Mean Rut = 12.8 + 162 x MESA (r2

= 0.84)

Figure 2 Mean rut (mm) vs accelerated load (MESA)

Table 3: Summary of relative performance factors, rpf, for rutting and roughness (relative to anuncracked single seal)

Rutting Roughness

Applicable strength, SNP, range 4.1 4.8 4.1 4.8

Maintenance treatment type ssck1

ds2

gs3

ssck1

ds2

gs3

Relative performance factor, rpf 1.7 0.9 0.5 1.9 0.9 0.5

Note: 1. ssck = single seal (100% cracked)2. ds = double seal (uncracked)




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3. gs = geotextile seal (uncracked)

Rapid DeteriorationTransition frontier to rapid deteriorationFigure 3 plots the mean rut (mm) and corresponding roughness (IRI) for all stages of all ALT experimentsduring loading. The tests experiencing gradual deterioration are defined separately from the testsexperiencing rapid deterioration. As Figure 3 shows, two frontiers can be established for the transition fromgradual to rapid deterioration on the basis of the increased rate of deterioration that characterises the rapid

deterioration phase. The first frontier is the boundary for the onset of rapid deterioration while the secondfrontier marks the boundary where all tests are definitely experiencing rapid deterioration.

The zone between the boundary for the onset of rapid deterioration and the boundary where all tests aredefinitely experiencing rapid deterioration is variable. However, the rutting limit (around 30 mm) androughness limit (7.5 IRI) at the frontier for rapid deterioration represents the most extreme levels of servicelimits used on sealed roads in Australia. The fact that these rutting and roughness limits are not toodissimilar to those used in practice suggests that the ALT simulation is reasonable from a physical limitviewpoint.

Prediction of rapid deteriorationIn practice there are limits on the extent of rutting and roughness that form the levels of service for varioustypes of pavements and conditions. Consequently, real pavements will not normally experience the rapiddeterioration phase; so the prediction of relative performance factors for this phase is unnecessary provided

the above limits are imposed in practice and in any life-cycle costing analysis.

0

10

20

30

40

50

0 5 10 15 20

IRI (m/km)

MeanRut(mm)

Gradual deterioration samples

Rapid deterioration samples

Frontier for rapid deterioration

Frontier for onset of rapid deterioration

Figure 3 Mean rut (mm) vs roughness (IRI) for all experiments

Use of Observational DataLong Term Pavement Performance Maintenance (LTPPM) SitesEach LTPPM site has five 200 metre long sections with a different maintenance treatment on each section.

In some cases the material differences in the resealing treatments are not significant, although they aredesignated as being distinct. The LTPPM sites have been monitored since 1997/98. The estimatednominal, or initial, value of SNP, based on FWD deflections using a relationship developed by Roberts(1995), was calculated for each of the 200 metre sections after the treatment was applied to the surface.

The treatments at the LTPPM sites being investigated are as follows:

minimum maintenance treatment with intervention only to ensure the safety of the road users;

routine maintenance treatments that address only surface defects such as cracking, potholes andshoulder drainage;




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normal reseals;

normal reseals with surface corrections to rutting and other defects; and,

higher quality reseals such as geotextile seals and scrap rubber seals.

Rutting at LTPPM SitesA plot of rutting rate (mm/year) against pavement age showed that, with the exception of the minimum

maintenance and routine maintenance treatments, there was no easily discernible difference in the ruttingrate (mm/year) between the different treatments when the estimated rutting rates were plotted for alltreatments on the LTPPM sites against their respective pavement age. Nor was there any significantdifference between the rutting rate on B Class Roads and A Class Roads. At this stage there also doesnot appear to be an increase in the rutting rate with increased pavement age.

In the case of the sections with minimum maintenance and routine maintenance treatments at the LTPPM1site, greater rutting rates were experienced on these sections suggesting rutting in the rapid deteriorationphase was reached. An average rutting rate of 0.84 mm/year for all LTPPM sites excluded the rutting ratesfor the minimum and routine maintenance treatments.

Roughness at LTPPM SitesThe plot of roughness rate (IRI/year) showed that there was no easily discernible difference in the roughnessrate between the different treatments when the estimated roughness rates were plotted for all treatments onthe LTPPM sites against their respective pavement age. The exception to this was the minimummaintenance and routine maintenance treatments at the LTPPM1 site. There also did not appear to be anincrease in the roughness progression rate with increased pavement age. There is a difference between theroughness rate on B Class Roads and A Class Roads with a higher roughness rate on B Class roads, asexpected.

In the case of the minimum maintenance and routine maintenance treatments at the LTPPM1 site, a greaterroughness progression rate was experienced suggesting that these pavements have reached the rapiddeterioration phase. The average roughness rate of 0.06 IRI per year for all LTPPM sites excludes theroughness rates for the minimum and routine maintenance treatments.

Strength at LTPPM SitesThere was no easily discernible difference in the strength deterioration rate, dSNP/dAGE, between thedifferent treatments when the estimated deterioration rates were plotted for all treatments on the LTPPMsites against their respective pavement age, including the minimum maintenance and routine maintenancetreatments. However, on the basis of average deterioration rates, there was a difference in the deteriorationrate between A Class and B Class Roads.

Long Term Pavement Performance (LTPP) SitesAll of the sealed granular pavement LTPP sites are 150 metres long, except for ARRB1 which is 200 metreslong. Most of these LTPP sites have monitored rutting, roughness and surface deflection since 1994/95.The nominal value of SNP, based on FWD deflections using a relationship developed by Roberts (1996),was estimated for each of the sites when they were first monitored. The performance data for the five LTPPsealed granular pavement sites (single and double seals) was combined with the performance data for six ofthe seven LTPPM sealed granular pavement normal reseal sections to increase the sample size with the aimof developing statistically significant performance relationships.

Rutting at LTPPM and LTPP SitesThe average rutting rate of all the LTPP sites is 0.33 mm/year which is significantly lower than that found for

all the LTPPM sites (0.84 mm/year) which have a higher average pavement age and a lower averagepavement strength than the LTPP sites. The LTPP sites rutting rates vary from a low rate of 0.12 mm/year(M Class Road) to a high rate of 0.73 mm/year (C Class Road).

When the single and double seals at the LTPPM and LTPP sections were combined it was possible todevelop improved statistically significant relationships. The separate single and double seal ruttingrelationships were not statistically significant which suggests that from the observations undertaken so farthat there is no significant difference in the rutting rate of single and double seals.

Separate relationships were derived from the LTPPM and LTPP site data for rutting rate against pavementstrength, SNP, traffic load, MESA, and Thornthwaite Index, I, a measure of climate. However, because




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pavement/subgrade strength and traffic load are highly correlated independent variables any ruttingrelationships involving these variables are not likely to be useful predictors of rutting rate. Figure 4 plots therutting rate against the Thornthwaite Index, I(Thornthwaite 1948).

Roughness at LTPPM and LTPP SitesThe average value of the roughness rate at the LTPP sites is 0.01 IRI/year which is significantly lower thanthat found for the LTPPM sites (0.06 IRI/year) with a higher average pavement age and a lower averagepavement strength than the LTPP sites.

When the single and double seals at the LTPPM and LTPP sites were combined it was possible to developimproved statistical relationships. The separate single and double seal relationships were not statisticallysignificant, although none of these combined section relationships were statistically significant either. Thisoutcome suggests that from the observations undertaken so far that there is no significant difference in theroughness rate of single and double seals. Figure 5 shows the relationship from the combined data forroughness rate influenced by pavement age, AGE, which was the best of the derived relationships, althoughit is not statistically significant.

Strength at LTPPM and LTPP SitesThere is no relationship for an increase in the strength deterioration rate, dSNP/dAGE, with an increase inpavement age for the LTPP sites. The average value of the strength deterioration rate at LTPP sites is0.023 SNP/year which is significantly lower than that found for the LTPPM sites (0.13 SNP/year. When thesingle and double seal sections and LTPPM and LTPP sites were combined improved statisticalrelationships were developed as the separate single and double seal relationships. However, none of thecombined section relationships were statistically significant. This outcome also suggests that from theobservations undertaken so far that there is no significant difference in the strength deterioration rate ofsingle and double seals.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

-20 0 20 40 60 80 100

Thornthwaite Index I

RutRate(mm/year)

LTPP Reseal Samples

LTPPM Reseal Samples

Rut Rate vs I

Rut Rate (mm/yr) = 0.38 + 0.009 x I (r2

= 0.52)

(all combined LTPP and LTPPM samples)

Figure 4 Mean rutting rate (mm/year) vs Thornthwaite Index (I) for LTPPM and LTPP sites




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-0.5

0.0

0.5

0 10 20 30 40 50AGE (years)

RoughnessRate(IRI/yr)

IRI/yr vs AGE

LTPPM Reseal Samples

LTPP Reseal Samples

Long Term Pavement Performance (LTPP

and LTPPM) Site Data (2 to 7 years)

IRI/year = -0.03 + 0.004 x AGE (r2 = 0.21)

(all combined LTPP and LTPPM samples:

Average IRI Rate = 0.05 IRI/year )

Figure 5: Lane roughness rate (IRI/year) vs pavement age (years) for LTPP and LTPPM sites

Re-calibration under Gradual DeteriorationRuttingRe-calibration of the structural rutting coefficient, Krst, was made as follows using the rutting rate relationshipshown in Figure 4 which varies with the Thornthwaite Index which was converted to the HDM-4environmental factor m (Paterson 1987) via an existing relationship (Martin 1996):

Krst = Rut (Figure 4 rutting relationship varying with I) relative performance factor (3)

HDM-4 Rut (= a0 SNPa1

YE4a2

COMPa3

)where;

Rut = 0.38 + 0.009 I(Figure 4 relationship)YE4 = annual number of ESA in millions per lane = MESA

COMP = relative compactionSNP = adjusted structural number of pavement/subgrade strength

a0, a1, a2 and a3 = model coefficients.

RoughnessThe re-calibration of roughness coefficients, Kgp, and Kgm, was made as follows using the roughness raterelationship shown in Figure 5 which varies with pavement age:

Kgp = Kgm = Roughness change relative performance factor (4)

HDM-4 roughnesschange

where;Roughness change = 0.03 + 0.004 AGE (Figure 5 relationship)

AGE = number of years since construction or last rehabilitation.HDM-4 roughness change = Roughness components due to rutting, traffic and environmental

deterioration (Morosuik et al. 2001).

Where the roughness rate is linear during gradual deterioration, the roughness calibration coefficients will

not change over annual roughness increments. Once the incremental roughness increase becomes non-linear the calibration coefficients will change with each annual roughness increment. The above approach isappropriate for gradual deterioration and assumed a 25 year pavement life before rapid deterioration occurs.

StrengthBoth the ALT and observational data showed that it was not possible to predict the impact of surfacemaintenance treatments, time and traffic on the structural deterioration of sealed granular pavements.Consequently, the HDM-4 structural deterioration model for sealed granular pavements could not be re-calibrated. This outcome suggests that modifications to the HDM-4 structural deterioration model areneeded for sealed granular pavements.




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Results of re-calibrationEquations (3) and (4) were applied to re-calibrate the rutting and roughness coefficients for single sealedgranular pavements over a range of climate, represented by the HDM-4 environmental factor m. Table 4summarises the re-calibrated rutting and roughness coefficients for single sealed granular pavements for therange of pavement strength, traffic load and environmental factor m experienced within the LTPP andLTPPM sites and the ALT data.

Table 4: Summary of re-calibrated coefficients for rutting and roughness

Range of SNP = 4.1 4.8: Range of YE4 = 0.06 0.17 MESA/lane/year

Environmental factor, m

Re-calibratedcoefficients (single seal)

0.035 0.032 0.029 0.027 0.024 0.022 0.020 0.016

Rutting, Krst 8.0 7.1 5.6 5.0 3.8 2.9 2.3 1.1

Roughness, Kgp = Kgm 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.5

Table 4 shows that the structural rutting coefficient, Krst, is highly sensitive to environmental conditions and ismuch greater than the default value of 1.0. Table 4 shows that the roughness coefficients, Kgp, and Kgm, aresignificantly lower than the default value of 1.0 and are not as sensitive to environmental conditions as therutting coefficient. Further observational data is needed to confirm these significant departures from thedefault values.

Summary of Re-calibrated Coefficients for Various Surface TreatmentsThe re-calibrated structural rutting and roughness coefficients for various surface treatments, other than asingle seal, can be estimated as follows:

Re-calibrated coefficient, Kij = Re-calibrated coefficient, Kij rpf, relative performance factor (5)

(various surface treatments) (single seal, Table 4) (Table 3)where;

Kij = re-calibrated rutting and roughness coefficient for various surface treatmentsKij = Re-calibrated rutting and roughness coefficient for single seal.

Table 5 summarises the re-calibrated rutting and roughness coefficients for a range of surface treatmentsover a range of climatic extremes.

Table 5: Summary of re-calibrated rutting/roughness coefficients for surface treatments and climates

Range of SNP = 4.1 4.8: Range of YE4 = 0.06 0.17 MESA/lane/year

Wet/humid m = 0.035 Temperate m = 0.024 Hot/dry m = 0.016

SurfaceTreatment

Rutting,Krst

Roughness,Kgp = Kgm

Rutting,Krst

Roughness,Kgp = Kgm

Rutting,Krst

Roughness,Kgp = Kgm

Single seal (SS) 8.0 0.2 3.8 0.3 1.1 0.5

Cracked SS 13.6 0.4 6.5 0.6 2.1 1.0

Double seal 7.2 0.2 3.4 0.3 1.0 0.5

Geotextile seal 4.0 0.1 1.9 0.2 0.6 0.3

DEVELOPMENT OF SPRAYED SEAL LIFE MODEL FOR AUSTRALIA

The main cause of bitumen hardening in a surface seal applied to a granular pavement, leading to surfacecracking, is chemical attack by atmospheric oxygen. This is a slow chemical reaction and the rate at which itproceeds at a particular site depends on bitumen temperature, the resistance of the bitumen to hardeningand binder film thickness. The intrinsic resistance of a bitumen to hardening can be measured using theARRB durability test (Standards Australia 1997), and a minimum bitumen durability value is specified by anumber of Australian road authorities.

Binder Life ModelBitumen Hardening ModelA simple mathematical model, describing the rate at which the bitumen binder in a sprayed seal hardens indifferent areas of Australia, was developed by Oliver (1987) based on information from 10 specially arrangedfull scale road trials and 13 non-trial sites. The non-trial sites were normal seals and reseals which had not




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been placed as road trials but which had been sampled and the binder viscosity tested at some stage of theirlife. In all, a total of 124 data points covering 45 different types of bitumen were used to construct thebitumen hardening model. This bitumen hardening model is defined as follows:

Log = 0.0394 T 0.023 D Y0.5

+ 3.34 (6)

where;

= viscosity of bitumen recovered from the distressed seal (Pa.s at 450C)

T = yearly mean of the daily air temperature (0C) = (TMAX + TMIN)/2

TMAX = yearly mean of the daily maximum air temperature (

0

C)TMIN = yearly mean of the daily minimum air temperature (0C)

D = ARRB Durability Test result (days)Y = number of years since the seal was applied.

Distress Viscosity ModelThe viscosity level at which seal distress occurs in a cool climate area, such as Tasmania, is likely to belower than the distress viscosity level in a warmer climate area, such as Darwin. Therefore the viscositylevel at which a seal shows distress depends on climate as well as some other factors. In order to develop adistress viscosity model, Australian State Road Agencies were requested to provide samples of seals whichwere just starting to show distress due to ageing. Twenty seven samples were obtained and these wereused to develop a model (Oliver 1990) based on the daily minimum temperature (obtained frommeteorological records) at the seal site. This model, defined as follows, was not as reliable as the binderhardening model:

Log = 0.105 TMIN + 4.78 (7)

where all the terms are as defined previously.

Combined ModelA combination of the binder hardening and distress viscosity models permits prediction of binder life or moreparticularly the intervention point for resealing, in different climatic areas of Australia. This is depicted inFigure 6. An attempt was made to include binder film thickness in the original bitumen hardening model butthis was unsuccessful. However, it is known from experience that large sized seals normally have longerlives than smaller seals and it was considered desirable to account for this factor in the model. AnAustroads funded project was, therefore, undertaken to develop a seal size term and this paper draws on theresults of that study. Further details of the work are given in a report by Oliver (2003).

3

4

5

6

7

0 5 10 15 20

Years since construction

Viscosity

measuredat45C(LogPa.s

)

.

Bitumen hardening curve for a site

(from eqn 6)

Distress viscosity for the site

(from eqn 7)

Binder life

Figure 6: Estimation of binder life




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Refinement of Bitumen Hardening ModelEstablishment of the DatabaseThe durability road trial database used for the binder hardening model (Oliver 1987) was reconstructed usingarchived original data, supplemented by a small amount of extra data for some trial sites (this extra data hadnot been available at the time of the original model development). However, data from the original 13 non-experimental sites was excluded because these sites did not have seal size data.

As was done for the original hardening model, the logarithm of recovered binder viscosity was regressed

against dependent variables. For the refined model these included average site temperature, bitumendurability, seal nominal size, seal age, and a number of combinations of these factors. The results of theregression analyses indicated that several terms had approximately equal weight for inclusion in the newmodel. Based on a knowledge of the hardening process and of the expected effect of seal size on seal life,the model chosen was:

Log = 0.0498 T Y0.5

0.0216 D Y0.5

0.000381 S2 Y

0.5+ 3.65 (8)

where;S = nominal seal size (mm).

and all other terms are as defined previously. The goodness of fit (r2) of equation (8) to the data is 0.88.

The model is applicable to properly constructed seals in which the bitumen hardens through thermaloxidation. Additional hardening caused by other reasons, such as loss of volatile oils in the bitumen thatwere not removed during refining, is not predicted.

Refined Binder (Seal) Life ModelA refined model for seal life can be developed by equating the bitumen viscosity term, Log , in bothequation (8), the refined hardening model, and equation (7), the distress viscosity model, and solving for Ywhich now becomes seal life. The resulting equation is defined as follows:

Y = 0.105 TMIN + 1.132

(9)

( 0.0498 T 0.0216 D 0.000381 S2

)

where all the terms are as defined previously. Application of the seal life model to a range of Australian sitesindicates that an increase in seal size from 10 mm to 14 mm results in a maximum seal life increase ofbetween 0.8 and 3.9 years, depending on location.

Further Improvement to the ModelAs indicated previously, the database for the distress viscosity model, equation (7), was less extensive thanthat of the binder hardening model, equation (8). Attempts are currently being made to obtain furthersamples of distressed seals from sites covering a range of climatic conditions so as to improve predictioncapability. Re-examination of the surfacings used to develop the distress viscosity model indicated that asubstantial proportion were clearly distressed when resealed. This has meant that, particularly in temperateareas, the binder life model tended to result in longer lives than those normally obtained in recent times.

HDM-4 RD MODEL CALIBRATIONS FOR AGENCIES

BackgroundOver the last two years a significant effort has been made in the Australian States of Victoria (Martin andToole 2003) and Tasmania (Toole et al. 2004) in adapting and calibrating HDM-4 to local conditions. In eachcase road performance data has been available for a significant period, with high quality data for in-service

roads dating back to the early 1990s. Both studies have involved assembling and analysing over 80 sites,and led to the need to modify the HDM-4 results through the application of appropriate calibration factors.The following discussion relates to the Victorian findings, as these are of wider interest as the climaticconditions span from sub-humid, temperate cool conditions in the south of the State to semi-arid conditionsin the north and west.

The studies also investigated a range of road design and functional classes, and included high standardfreeway pavements (Class M), highways (Class A) and arterial roads (Classes B and C).

The road deterioration model parameters that were calibrated during the studies included crack initiation,rutting and roughness. Crack progression was less easy to quantify due to the pro-active maintenancepolicies maintained by the road agencies. These aim to ensure that bituminous reseals are applied when




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the surface is in the early stages of distress, or may have oxidised to a point where cracking and/or ravellingis close to initiation.

Summary of Revised Calibration Coefficients from Field Performance DataThe Victorian calibration exercise resulted in a set of different calibration factors based on road class (roadlayout). Subgrade soil behaviour, drainage conditions and the quality and type of pavement materials werealso found to significantly affect performance. Most of the latter factors were found to be related to thephysical and geological environment within which a particular road section was located for calibration. The

cycle of deterioration, that is, whether immediately post construction or post periodic maintenance orrehabilitation was also considered as having a bearing on relative rate of deterioration, but this was notproved conclusively.

The average results for selected calibration factors for sealed granular pavements built on stable subgradesin rural areas by road class, and on a region by region basis, are shown in Tables 6 and 7. The averagetime-based rates of roughness progression are also shown. In Tables 6 ad 7, the calibration factor for initialdensification, Krid, shown is for double seals in all cases.

The data was also examined to investigate the association between crack initiation and other causalvariables. Figure 6 shows the predictions from HDM-4 and observations from Victoria. In the Figure 6, thetypical design refers to a fit for purpose structural number for the design traffic.

Table 6: Average calibration factors by road class for sealed granular pavements roads built on

stable subgradesRoad Class Modifying Factor Kcia

1Krid

2Krst

3Kgm

4Kgp

5IRI/year

M Stable Subgrade 1.16 0.72 1.93 0.38 0.38 0.04

A Stable Subgrade 0.87 0.81 2.26 0.54 0.54 0.08

B Stable Subgrade 0.99 0.49 1.3 0.59 0.59 0.09

C Stable Subgrade 1.16 0.72 1.93 0.65 0.63 0.14

Note: 1. Kcia = calibration factor for crack initiation

2, 3, 4, and 5 As defined previously.

Table 7: Average calibration factors for sealed granular pavements roads for Class C roads built on

stable subgrades

Region Climate Kcia Kgm Kgp Krid Krst IRI peryear

Eastern Subhumid, cool 0.95 0.55 0.55 1.02 2.52 0.12

North East Semi arid, cool 0.75 0.88 0.88 0.50 3.25 0.14

Western Semi arid, cool 1.00 1.50 1.50 1.00 1.50 0.16

South West Subhumid, cool 0.64 0.63 0.63 0.67 2.78 0.23

Predicted HDM-4 crack initiation time versus SNP

and YE4

0

2

4

6

8

1012

14

0 0.5 1 1.5 2 2.5

Rate of loading (MESA per year)

Crackinitationtim

e

(years)

SNP 4.5

SNP 4

SNP 5

Typical design

Observed crack initiation time for sites in Victoria

0

2

4

6

810

12

14

0 0.2 0.4 0.6 0.8 1

Rate of loading (MESA per year)

CrackInitiation

Time

(Years) Eastern

Metro SE

North

North East

South West

West

Figure 6 Plots of HDM-4 crack initiation model predictions and field observations




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Summary of calibration differencesAs indicated by the sample of results, the most significant differences are as follows:

1. The annual rate of increase in roughness for Class C roads on stable subgrades are approximatelythree times those on Class M roads, and nearly twice those on Class A and B roads despite the factthat they carry much lower traffic volumes. Whilst this may be surprising, the result demonstrates thevalue of a high investment design strategy for heavily used roads, and the critical role which road age

and environment location can play for lower cost structures.

2. The annual rates of roughness progression also varied significantly within a single road class, and werehighest in the south west of the State, reflecting the wetter conditions and the behaviour of weakpavements and pavement materials. More closer inspection of observations also revealed higherroughness progression rates in the northern areas of the State where irrigation is common. A furtherfinding was, the annual rates of increase in roughness on unstable subgrades was between two andthree times that of stable subgrades.

3. .Crack initiation times are variable, being both earlier and later than predicted by the default model.

4. Crack initiation times are not strongly dependent on traffic loading as currently predicted by HDM-4,although they are highly variable. Some of the shortest times are, however, associated with weakpavements, as typified by the south west of the State, and with drier climatic conditions.

5. The initial densification component of rutting was found to be significantly less than the defaultprediction, although the rate of rut progression is greater, by a factor of about two.

6. There was also a strong indication that the rate of roughness progression on lower class roads is relatedto drainage condition. For example, for Class C roads the rate varied by a factor of between three andfour, depending on whether the drains were in a good condition or were ineffective, or absent ordamaged. Class M roads were the least affected.

Comparison with earlier findingsThe above findings help demonstrate the variable response of in-service roads to the variety of factorsidentified as influencing their behaviour, and confirm the importance attached to adapting and/or developingmodels for local application.

With respect to the findings presented in earlier sections of this paper, the following comments andillustrations serve to reinforce the above statement and the general flexibility required for taking the resultstowards application. The comments are made in order of the preferred interactive approach to adaptingHDM-4 technology commencing with the modelling of cracking, then rutting or permanent deformation, andfinally roughness progression.

CrackingFor normally designed and constructed pavements and bituminous surfacings, as illustrated in Figure 6,crack initiation is likely to be more closely related to seal age. This is not surprising given that practitionersaim to obtain a reasonable life from any major treatment they apply, usually of the order of ten years ormore. The ARRB seal life model, and the results of the field studies, supports this notion. From an HDM-4viewpoint, consideration should therefore be given to providing users with the flexibility to adjust thecoefficients that determine both the minimum/maximum life and the reduction in seal life with increasedtraffic. The addition of temperature and seal size terms are also warranted based on the Australian findings.

RuttingThe relative initial densification for double seal treatments from the ALT experiments closely matches thosefound from field observations, being somewhat less than unity.

The relative performance factors for rutting of cracked and uncracked pavements derived from the ALTexperiments tend to have less sensitivity than those predicted by the HDM-4 models (Martin and Toole2003). Given that this finding is based on the ALT, further consideration needs to be given how theexperimental work relates to in-service conditions. As mentioned earlier, Australian practice tends to ensurecracks are sealed soon after appearance, therefore field data to quantify the effect of cracked pavements israre.




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The Victorian field observations did, however, demonstrate that higher rates of rutting than defaultpredictions were common, although they could not clearly separate climatic factors from other issues ofsubstandard materials and local drainage factors.

RoughnessThe field observations generally bear out the need to apply calibration factors substantially lower than unity,at least for higher standard roads. However, these factors vary considerably for lower class roads whoseperformance is largely governed by moisture movements during drying and wetting cycles. Greater variation

in climatic conditions coupled with particular soil conditions has a significant influence on the magnitude ofthe factors. Annual average rates of change in roughness is also shown to be more time dependent thantraffic dependent, this being confirmed by both the Australian wide LTPP studies and observations inVictoria.

CONCLUSIONS

ALT DataRut DensifcationThe ALT experiments allowed quantification of the rut densification coefficient, Krid, for double or two coatseals relative to single seals. This finding was also confirmed by field observations which showed that, Krid,for double or two coat seals is usually significantly less than unity for Australian sealed granular pavements.

Gradual Deterioration

The ALT experiments allowed formulation of relative performance factors for rutting and roughness duringgradual deterioration for a range of surface treatments. These factors allowed subsequent calibration of theHDM RD models in combination with the LTPP and LTPPM data.

No relative performance factors could be established for the deterioration of pavement strength. This couldbe because the accelerated rate of loading did not allow enough time for strength deterioration to occur.

Rapid DeteriorationThe ALT experiments established the zone for the transition from gradual to rapid deterioration, whichcannot usually be observed in practice. The zone limits are a rutting limit of 30 mm and a roughness limit of7.5 IRI if the onset of rapid deterioration is to be avoided. These limits are close to those used in practice,which suggests that the ALT physical simulation is reasonable.

Observational Data

LTPPM and LTPP DataThe average strength, rutting and roughness deterioration rates at all the LTPP sites were significantly lowerthan that estimated at the LTPPM sites. This is believed to be due to the generally higher average pavementstrength and lower average pavement age at the LTPP sites.

The observational data, similar to the ALT data, showed that it was not possible to consistently observe theimpact of surface treatment, time and traffic on the structural deterioration of sealed granular pavements.Consequently, the HDM-4 structural deterioration model for sealed granular pavements could not be re-calibrated. This outcome suggests that modifications to the HDM-4 structural deterioration model areneeded for sealed granular pavements.

Re-calibration for Single SealsThe structural rutting coefficient, Krst, was calibrated based on the observed rut deterioration with increasedseverity of climate. The roughness progression coefficients, Kgm, and Kgp, were calibrated based on the

observed deterioration of roughness with time.

Rutting and roughness coefficients were derived for a range of climates and surface maintenancetreatments, which showed significant departure from the HDM-4 default coefficient values.

Sprayed Seal Life ModelA refined seal life model was developed based on earlier work by ARRB. This model estimates seal life(years) taking into account climatic conditions, through annual extremes of air temperature, the durability ofthe seal and the nominal seal size. Further distressed seal data over a wide range of climatic and trafficconditions should improve the reliability of this model. If this reliability is achieved, then substantial




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replacement of the HDM-4 crack initiation model is warranted for sealed granular pavements underAustralian conditions.

HDM-4 RD Model Calibrations for AgenciesRutting CoefficientsThe relative initial densification for double seal treatments from the ALT experiments closely matched thosefound from field observations in Victoria.

The Victorian field observations demonstrated that higher rates of structural rutting than the defaultpredictions were common, although they could not clearly separate climatic factors from other issues ofsubstandard materials and local drainage factors. This outcome was similar to that generally predicted bythe combined ALT and LTPPM/LTPP data.

Roughness CoefficientsThe field observations generally showed that calibration factors substantially lower than unity, at least forhigher standard roads, are applicable. However, these factors vary considerably for lower class roadswhose performance is largely governed by moisture movements, variation in climatic conditions andparticular soil conditions.

SummaryThe ALT experimental data, the LTPP/LTPPM observational data and the results of HDM-4 RD modelcalibrations for two Australian road agencies showed that substantial variation from the default calibrations

factors for rutting and roughness are needed for Australian conditions. Further refinement of thesecalibration factors was also needed to account for the influence of road design standard, drainage andmaterials.

Further distressed seal data to improve the reliability of the refined binder life model may warrant thesubstantial replacement of the HDM-4 crack initiation model for sealed granular pavements under Australianconditions. The ALT and observational data showed that it was not possible to predict the impact of surfacemaintenance treatments, time and traffic on the structural deterioration of sealed granular pavements. Thisoutcome suggests that modifications to the HDM-4 structural deterioration model are needed for sealedgranular pavements.

REFERENCES

ABS (Australian Bureau of Statistics) (2001). 2001 Year Book Australia, pp 1091. (ABS: Canberra,

Australia).

MCLEAN, J.R. (1985). The ALF program and its relation to pavement research. Proceedings, Seminar onAccelerated Loading Facility, Gosford, New South Wales, pp 96. (ARRB: Vermont South, Victoria, Australia).

MARTIN, T. (1996). A review of existing pavement performance relationships. ARRB TR, Research ReportARR 282, pp 61. (ARRB TR: Vermont South, Victoria).

MARTIN, T. and GLEESON, B. (1999). The Effect of Maintenance on Pavement Performance: The Accelerated Load Pilot Test, ARRB Transport Research Contract Report RC7094B, pp 42 (ARRB TR:Vermont South, Victoria, Australia).

MARTIN, T., GLEESON, B., JOHNSON-CLARKE, J., TREDREA, P., and FOSSEY, D. (2000). The Effect ofMaintenance on Pavement Performance: Accelerated Load Testing in 1999/2000, ARRB Transport

Research Contract Report RC90264, pp 44. (ARRB TR: Vermont South, Victoria, Australia).

MARTIN, T., GLEESON, B., JOHNSON-CLARKE, J., TREDREA, P., LUKE, R. and FOSSEY, D. (2001).The Effect of Maintenance on Pavement Performance: Accelerated Load Testing in 2000/2001, ARRBTransport Research Contract Report RC1739, pp 62. (ARRB TR: Vermont South, Victoria, Australia).

MARTIN, T. and TOOLE. T. (2003). Improved HDM-4 model calibration factors and application guidelinesfor sealed roads in Victoria: Final Project Report Part 1 Project Implementation and Results of RoadDeterioration and Works Effects Studies, ARRB Transport Research Contract Report RC1739, pp 64.(ARRB TR: Vermont South, Victoria, Australia).




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MARTIN, T. (2004). Data review and calibration of HDM-4 road deterioration models, ARRB TR ResearchReport ARR 360, pp 118. (ARRB TR: Vermont South, Victoria, Australia).

MOROSIUK, G., RILEY, M. and ODOKI, J.B. (2001). HDM-4 Modelling Road Deterioration and WorksEffects, Review Draft Version 1.1, Vol.6(World Road Association (PIARC) and The World Bank, WashingtonD.C., US).

OLIVER, J.W.H. (1987). Asphalt hardening in sprayed seals. Trans. Res. Rec. 1106 pp. 196-201.

Transportation Research Board, USA.

OLIVER, J.W.H. (1990 ). Models to predict bitumen hardening rate and distress viscosity level in sprayedseals. Australian Road Research Board Research Report ARR 182. (ARRB: Vermont South, Victoria,Australia).

OLIVER, J. (1999). The performance of sprayed seals, ARRB Transport Research Report, ARR 326, pp 45(ARRB TR: Vermont South, Victoria, Australia).

OLIVER, J.W.H. (2003). Development of an Aggregate Size Term for a Reseal Intervention Model.ARRBTR Contract Report to Austroads RC2498 - A. (ARRB TR: Vermont South, Victoria, Australia).

ROBERTS, J.D. (1995). Pavement Management System: Operation Guide and System Description. 5thADB Road Improvement Project, Philippines, Kampsax in association with SMEC and OPCV of Australia,(SMEC International Pty Ltd: Cooma, NSW, Australia).

ROBINSON, G. (2003). Components of uncertainty about models for road pavement performance anddeterioration, CSIRO Report 03/49, pp 10. (CSIRO: Mathematical and Information Sciences, Clayton South,Victoria, Australia).

STANDARDS AUSTRALIA (1997) Australian Standard 2341.13 Methods of testing bitumen and relatedroadmaking products Method 13: Long-term exposure to heat and air.

TEPPER, S. and MARTIN, T. (2001). Long Term Pavement Performance Maintenance (LTPPM) ProgressReport, ARRB TR Contract Report for Austroads Project BS.A.N.001-4, pp 15 + Appendices (ARRB TR:Vermont South, Victoria, Australia).

TEPPER, S., FOSSEY, D. and KOH, S.L. (2002). Long Term Pavement Performance Study Data Report:2001/02 Season, ARRB TR Contract Report for Austroads Project T&E.PN.507, pp 81 (ARRB TR: VermontSouth, Victoria, Australia).

THORNTHWAITE, C.W. (1948). An Approach Toward a Rational Classification of Climate. GeographicalReview,38(55) (American Geographical Society: New York, USA).

TOOLE, T., ROPER, R. and MICHEL, N. (2004). Implementation of HDM-4 in Tasmania: Part 1 ProjectSummary and Results of Strategy Analysis, Draft ARRB Transport Research Contract Report RC3296, pp42. (ARRB TR: Vermont South, Victoria, Australia).

YEO, R.E.Y. and KOH, S.L. (2003). Impact of New Heavy Vehicles on Pavements and Surfacings Reporton Load Equivalence using ALF, ARRB TR Contract Report for Austroads Project T+E.P.N.504, pp 65.(ARRB TR: Vermont South, Victoria).

ACKNOWLEDGEMENTS

Austroads is acknowledged for providing long term funding for the adaptation of HDM-4 technology toAustralian conditions. In addition, the support of Vicroads and the DIER, Tasmania, in supplyingperformance data and their cooperation with ARRB in calibrating the HDM-4 RD models for their Agencies isacknowledged.

BIOGRAPHY OF PRESENTING AUTHOR

Tim Martin




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Tim Martin holds a Diploma of Civil Engineering and a Bachelor of Engineering (First Class Honours) fromMonash University and a Master of Engineering Science from the University of Melbourne. Before joiningARRB Transport Research in 1990, Tim spent some 17 years in investigation, planning, design, contractmanagement and economic evaluation of major Australian and international engineering projects thatincluded bridges, dams, river diversions and open cut mining works.

Tims research at ARRB has involved leading an extensive road track cost attribution study, the design andimplementation of major experimental work using long term pavement performance maintenance (LTPPM)

sites and accelerated load testing, and the development of a range pavement performance models for use ina life-cycle costing approach to asset management.

Tims areas of research expertise include:

Pavement performance relationships, including the influence of maintenance, climate and loading onpavement deterioration, at a network and project level.

The modelling of road networks for life-cycle costing analysis, using genetic optimisation, for strategicanalysis and allocation of maintenance and rehabilitation funding.

Integration of experimental performance data, including the selection and measurement of pavementperformance indicators, from specific experimental studies for the development of pavementperformance relationships.

Road track cost allocation to heavy vehicles as the basis for heavy vehicle road user charging.

Assessment of the impact of increased axle mass loading on the road infrastructure.

Assessment of community expectations for levels of service on local roads.

Project management, asset valuation and economic evaluation.

Major projects include:

Pavement life-cycle costing modelling, including pavement performance modelling at a network andproject level, for a wide range of Australian arterial roads, including the National Highway systemacross Australia.

Road track cost allocation to heavy vehicles as the basis for heavy vehicle road user charging forAustralias arterial road system.

Development of a methodology for the assessment of community expectations for levels of service onlocal roads under the funding provided by Austroads.

Consulting applications of pavement life-cycle costing for maintenance and rehabilitation management,asset valuation and the impact of increased axle mass limits.

Consulting applications for the calibration of World Bank HDM-4 road deterioration (RD) and workseffects (WE) models for road agencies in Australia.

Leading the experimental research using LTPPM monitoring and accelerated load testing for Austroads

for the refinement of pavement performance modelling under the funding provided by Austroads.


TRB Committee AFD10 on Pavement Management Systems is providing the information contained herein for use by individual practitioners

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