NCATReport17-02

EVALUATIONOFTHEAIMS2ANDMICRO-DEVALTOCHARACTERIZE

AGGREGATEFRICTIONPROPERTIES

ByMaryGreer

Dr.MichaelHeitzman,P.E.

April2017

EvaluationoftheAIMS2andMicro-DevaltoCharacterizeAggregateFrictionProperties

NCATReport17-02by

MaryGreerGraduateStudentAuburnUniversity

Dr.MichaelHeitzmanAssistantDirector

NationalCenterforAsphaltTechnology

April2017

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ACKNOWLEDGEMENTSThisauthorswishtothanktheFederalHighwayAdministrationforsponsoringthisresearch.

DISCLAIMERThecontentsof this reportreflect theviewsof theauthorswhoareresponsible for the factsandaccuracyofthedatapresentedherein.Thecontentsdonotnecessarilyreflecttheofficialviews or policies of the sponsoring agencies, theNational Center for Asphalt Technology, orAuburn University. This report does not constitute a standard, specification, or regulation.Commentscontainedinthisreportrelatedtospecifictestingequipmentandmaterialsshouldnotbeconsideredanendorsementofanycommercialproductorservice;nosuchendorsementisintendedorimplied.

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ABSTRACT

ThisNationalCenterforAsphaltTechnology(NCAT)reportisasummaryofastudyperformedatNCATbyAuburnUniversityCivilEngineeringgraduatestudentMaryGreer.DetailsofthestudyarereportedinherMaster’sThesisaspartofherqualificationsforaMasterofScienceDegreeinMay2015. Thereisaneedforalaboratoryprotocoltorapidlyevaluatethequalityofaggregatefrictionpropertiesforuseinanasphaltsurface.Aggregateswithgoodfrictionresistpolishingandretaintheirshapecharacteristics.Anasphaltsurfaceneedstomaintainadequatefrictionasitispolishedbytraffictoensuremotorists’safetyontheroadway.Somecurrentlaboratoryproceduresusedtoevaluatethefrictionpropertiesofaggregatesaretimeconsumingandsubjective.ThepurposeofthisresearchstudywastoevaluatethecorrelationofaggregatefieldfrictionperformancetoaggregatepropertiesusingalaboratoryprotocolconsistingofthesecondgenerationAggregateImagingMeasurementSystem ModelAFA2A(AIMS2)andMicro-Deval.ThetestprotocolusedtheAIMS2devicetoquantifyaggregateshapecharacteristics(angularity,texture,andform)beforeandafterpolishingintheMicro-Deval.TheaggregateswereselectedfortheirfrictionperformanceinasphaltsurfacesattheNCATTestTrack.Fieldfrictionperformancewasmeasuredwiththelocked-wheelskidtrailer.Aggregateangularityandtextureindexeswerecomparedwiththeskidtrailerfieldmeasurementstodetermineifacorrelationcouldbeestablished.TheAIMS2devicedetectedchangesinaggregateshapeafterconditioningintheMicro-Deval.However,theanalysisdidnotestablishgoodcorrelationbetweentheAIMS2indexesandthefieldfrictiondatausingthetestprotocoldevelopedinthisresearchstudy.Additionalstudiesareneededtoexpandtheaggregatetypesandenhancethelaboratorytestprotocoltoverifytheseconclusionsorimprovetheabilityofthetesttocorrelatetofieldfrictionperformance.

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TABLEOFCONTENTS

Abstract.........................................................................................................................................ivListofFigures................................................................................................................................viiListofTables................................................................................................................................viiiCHAPTER1Introduction.................................................................................................................91.1 Background........................................................................................................................91.2 ObjectivesandScope.........................................................................................................9

CHAPTER2LiteratureReview.......................................................................................................112.1 MeasuringFriction...........................................................................................................112.1.1 Locked-WheelSkidTrailer.........................................................................................112.1.2 LabTestCorrelationswithLocked-WheelSkidTrailer..............................................12

2.2 LaboratoryConditioningDevices.....................................................................................142.2.1 LosAngelesAbrasionTest.........................................................................................152.2.2 Micro-DevalAggregateConditioningTest................................................................162.2.3 L.A.AbrasionandMicro-DevalTestDifferences.......................................................17

2.3 AggregateImagingSystems.............................................................................................182.3.1 TheSecondGenerationAggregateImagingMeasurementSystem.........................18

2.4 RelevantResearchonAIMS.............................................................................................19CHAPTER3AIMS2TestDescription.............................................................................................243.1 AIMS2AggregateTesting.................................................................................................243.1.1 AIMS2CoarseAggregateTesting..............................................................................253.1.2 AIMS2FineAggregateTesting..................................................................................26

3.2 AIMS2AggregateShapeMeasurements.........................................................................273.2.1 AggregateAngularity................................................................................................273.2.2 SurfaceMicro-texture...............................................................................................283.2.3 AggregateForm........................................................................................................283.2.4 FlatandElongatedProperties...................................................................................29

CHAPTER4ExperimentalPlan......................................................................................................304.1 SelectionofNCATTestTrackPavementSections...........................................................304.2 MaterialSelection............................................................................................................314.3 TestProcedure.................................................................................................................314.3.1 CoarseAggregateMicro-DevalandAIMS2TestingProcedure.................................324.3.2 FineAggregateMicro-DevalandAIMS2TestingProcedure.....................................33

4.4 DataQualityControl........................................................................................................34CHAPTER5LaboratoryResultsandDiscussion............................................................................375.1 Micro-DevalAggregateMassLoss...................................................................................375.2 AIMS2AggregateAngularity............................................................................................385.3 AIMS2CoarseAggregateTexture....................................................................................425.4 AIMS2CoarseAggregateSphericity................................................................................455.5 AIMS2CoarseAggregateFlatandElongated(F&E)Ratio...............................................46

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5.6 AIMS2FineAggregateTwo-DimensionalForm(Form2D)...............................................475.7 SummaryofLabResults..................................................................................................49

CHAPTER6ComparisonofAIMS2LabResultsandFieldFriction................................................506.1 FieldFrictionData............................................................................................................506.1.1 DefiningTerminalFriction.........................................................................................51

6.2 ComparingAIMS2AggregateAngularitytoFieldFrictionPerformance.........................526.2.1 AIMS2CoarseAggregateAngularityandSN40R.......................................................526.2.2 AIMS2FineAggregateAngularityandSN40R...........................................................55

6.3 ComparingAIMS2TexturetoFieldFrictionPerformance...............................................566.4 ComparingMicro-DevalMassLosstoFieldFrictionPerformance..................................586.5 SummaryofComparisonResults.....................................................................................59

CHAPTER7ConclusionandRecommendations...........................................................................60References....................................................................................................................................62

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LISTOFFIGURES

Figure1Ribbed(ASTME501)versusSmoothTestTire(ASTME524)(3)..................................................11Figure2SmoothTireSNsversusRibbedTireSNsCorrelationPlot(4)......................................................12Figure3LaboratoryDFT60ValuesversusTWPDConditioningCyclesforFourSlabs(5)............................13Figure4FieldSN64RversusESALsforFourTestSections(5)...................................................................13Figure5Laboratory(DFT60*100)versusNCATTestTrack(SN64R)Correlation(5)...................................14Figure6L.A.AbrasionTestingEquipment(7)............................................................................................15Figure7PavementPerformanceRatingswithL.A.AbrasionResults(6)...................................................16Figure8Micro-DevalDevice......................................................................................................................16Figure9PavementPerformanceRatingswithMicro-DevalAbrasion(6).................................................17Figure10SecondGenerationAggregateImagingMeasurementSystem(12)..........................................19Figure11AIMS2Microscope-CameraSystem...........................................................................................24Figure12CoarseAggregatesSpreadonTrayTrough................................................................................25Figure13GrayscaleImageUsedtoCaptureCoarseAggregateTexture...................................................26Figure14FineAggregatesSpreadonTrayTrough....................................................................................27Figure15GradientVectorforSmoothversusAngularParticle(16).........................................................28Figure16RepresentationofAIMS2AggregateForm(11).........................................................................28Figure17ExampleofRemovingFirstOrderOutliersfromDataSet.........................................................35Figure18ChangeinCoarseAggregateMassLossfromMicro-DevalConditioning..................................37Figure19ChangeinFineAggregateMassLossfromMicro-DevalConditioning......................................37Figure20ChangeinAIMS2CoarseAggregateAngularityfromMicro-DevalConditioning......................39Figure21ChangeinAIMS2FineAggregateAngularityfromMicro-DevalConditioning...........................39Figure22ExampleofAIMS2AngularityDistributionChangeforOpelikaLimestone...............................40Figure23AIMS2CoarseAggregateAngularityCumulativeDistributionsafter100MinutesofMicro-DevalConditioning..............................................................................................................................................42Figure24AIMS2FineAggregateAngularityCumulativeDistributionsafter30MinutesofMicro-DevalConditioning..............................................................................................................................................42Figure25ChangeinAIMS2CoarseAggregateTexturefromMicro-DevalConditioning...........................43Figure26ExampleofAIMS2TextureDistributionChangeforOpelikaLimestone...................................44Figure27AIMS2CoarseAggregateTextureCumulativeDistributionDifferencesafter100MinutesofMicro-DevalConditioning..........................................................................................................................45Figure28ChangeinAIMS2CoarseAggregateSphericityfromMicro-DevalConditioning.......................45Figure29AIMS2CoarseAggregateSphericityCumulativeDistributionDifferencesafter100MinutesofMicro-DevalConditioning..........................................................................................................................46Figure30ChangeinAIMS2CoarseAggregateF&ERatiosfromMicro-DevalConditioning......................46Figure31ChangeinAIMS2FineAggregateForm2DfromMicro-DevalConditioning..............................47Figure32ChangeinAIMS2FineAggregateForm2DDistributionforBauxite..........................................48Figure33ChangeinAIMS2FineAggregateForm2DdistributionforColumbusGranite..........................48Figure34AIMS2FineAggregateForm2DDistributionDifferencesafter30MinutesofMicro-DevalConditioning..............................................................................................................................................49Figure35MixGradationsforTestTrackSectionGroups..........................................................................50Figure36AverageofFieldSN40RDataBasedonMixType......................................................................51Figure37SN40RTerminalFrictionforEachTestTrackGroup..................................................................52

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Figure38ComparisonofAIMS2CoarseAggregateAngularitywithFieldFriction....................................53Figure39LinearTrendinAIMS2CoarseAggregateAngularity.................................................................54Figure40ComparingTrend-linesforFieldFrictionandAIMS2CoarseAggregateAngularity..................54Figure41LinearTrendinAIMS2FineAggregateAngularity.....................................................................55Figure42ComparingTrend-linesforFieldFrictionandAIMS2FineAggregateAngularity......................56Figure43LinearTrendforAIMS2CoarseAggregateTexture...................................................................57Figure44ComparingTrend-linesforAIMS2CoarseAggregateTextureandFieldSN40R........................58

LISTOFTABLES

Table1PavementPerformanceEvaluationCriteria(6)............................................................................15Table2ExampleofResultsObtainedfromAASHTOT304,AIMSFAA,andAIMSForm2D(12)................20Table3ExampleofCAAResultsObtainedfromASTMD5821andAIMS(12)..........................................20Table4ExampleofAIMS2FittingParametersforPredictingAngularityLossfromMicro-DevalConditioning(16).......................................................................................................................................22Table5ExampleofAIMS2FittingParametersforPredictingSurfaceTextureLossfromMicro-DevalConditioning(16).......................................................................................................................................22Table6SummaryofAIMS2IndexRanges.................................................................................................29Table7NCATTestTrackSectionMixtureIdentification...........................................................................31Table8SummaryofLaboratoryTestedAggregateProperties..................................................................31Table9PercentMassLossRankingamongCoarseandFineAggregatesafterTotalConditioning...........38Table10PercentLossinAIMS2AngularityforCoarseandFineAggregates............................................40Table11ExampleofK-STestResultsforOpelikaLimestoneNo.4AIMS2Angularity..............................41Table12PercentLossinAIMS2CoarseAggregateTextureafter100MinutesofConditioning...............43Table13ExampleofK-STestResultsforOpelikaLimestoneNo.4AIMS2Texture..................................44Table14ExampleofK-STestResultsofAIMS2Form2DDistributionsforBauxiteandColumbusGranite...................................................................................................................................................................48Table15RankingAIMS2CoarseAggregateAngularityandFieldSN40R..................................................54Table16RankingAIMS2FineAggregateAngularityandFieldSN40R.......................................................56Table17RankingAIMS2CoarseAggregateTextureandFieldSN40R.......................................................57Table18RankingFieldFriction,CoarseAggregateMassLoss,FineAggregateMassLoss........................58

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LISTOFABBREVIATIONS

AASHTO AmericanAssociationofStateHighwayTransportationOfficialsAIMS2 AggregateImagingMeasurementSystemModelAFA2AAIMS-II alternateabbreviationforAIMS2ASTM AmericanSocietyforTestingandMaterialsBMD beforeMicro-DevalconditioningCA coarseaggregateCAA coarseaggregateangularityDFT DynamicFrictionTesterESALs equivalentsingleaxleloadsF&E flatandelongatedFA fineaggregateFAA fineaggregateangularityFDG fine-densegradedForm2D two-dimensionalformGsa apparentspecificgravityGsb bulkspecificgravityHFST highfrictionsurfacetreatmentK-Stest Kolmogorov-SmirnovtestL.A.Abrasion LosAngelesAbrasionNCAT NationalCenterforAsphaltTechnologyNMAS nominalmaximumaggregatesizeOGFC opengradedfrictioncourseRAP reclaimedasphaltpavementSMA stonematrixasphaltSN skidnumberSN40R locked-wheelskidtrailerskidnumberatatestspeedof40mphwitharibbedtireSN40S locked-wheelskidtrailerskidnumberatatestspeedof40mphwithasmoothtireTWPD ThreeWheelPolishingDevice

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CHAPTER1 INTRODUCTION

1.1 Background

Roadwaysafety isahighpriority forhighwayagencies.Pavement friction,also referred toasskidresistance,isanessentialcomponentofanasphaltpavementsurfacetooptimizeroadwaysafety.Heavytrafficpolishestheasphaltpavementsurfaceanddecreasesskidresistance.Stateagencies set minimum pavement surface friction thresholds to reduce the risk of crashescausedbyinadequatefrictionontheroadwaysurface,especiallyinwetweatherconditions.

Alocked-wheelskidtrailer,equippedwitheitheraribbedorsmoothtestingtire,isacommonmethod for measuring pavement friction in the field (1). If a pavement’s friction value fallsbelowtheminimumthreshold,correctivemeasuresareneeded,whichcanbecostlyandtimeconsuming. Therefore, laboratory test protocols are needed to rapidly evaluate a surfacemixture’sabilitytomaintainacceptablefrictionpriortoplacingthemixtureinthefield.

Aggregateisamajorcomponentofanasphaltpavementmixture,andsurfacemixturesshouldusegoodqualityaggregates.Shapecharacteristicsofanaggregatemaydetermine itsquality.Aggregatetextureandangularityinfluenceamixture’sabilitytoprovideasufficientamountoffrictionbetweenthepavementsurfaceandvehicletire (1).Understandingtheroleaggregatetexture and angularity play in pavement friction over the performance period of the surfacelayerisessentialtoachievingasafepavementsurface.

There are several testmethods to evaluate aggregate shape characteristics in the laboratorybeforeandaftertheaggregatesaresubjectedtoconditioning(polishing).Someofthesetestsare subjective and time consuming (1). Researchers continue to develop aggregate imagingsystems toquantifyaggregate shapecharacteristics. This research studyexamines theuseoftheAIMS2.TheAIMS2devicewasusedtoquantifyaggregateshapecharacteristicsbeforeandafter conditioning the aggregates using theMicro-Deval device. The study will evaluate theAIMS2measurementsasameansofdetectingchangesinaggregateshapecharacteristicswhenaggregatesweresubjectedtoconditioning.

1.2 ObjectivesandScope

The first objective of this studywas to evaluate the feasibility of using the AIMS2 device inconjunction with the Micro-Deval as an aggregate testing protocol in the laboratory forqualifyingpavementfrictionperformanceinthefield.Thesecondobjectivewastodetermineifa correlation could be established between theAIMS2 lab results and the locked-wheel skidtrailerfieldfrictiondata.

FivedifferentaggregatesourceswereselectedfortestingbasedontheiravailabilityanduseinsurfacemixturesofdifferenttestsectionsontheNCATTestTrack.Theaggregatesourcesweretwo limestone, two granite, and one bauxite, a high friction aggregate. No. 4 sieve particles(passingthe3/8inchsieveandretainedontheNo.4sieve)andNo.16sieveparticles(passingtheNo.8sieveandretainedontheNo.16sieve)wereextractedfromeachaggregatesource;however, only No. 16 sieve particles were available from the bauxite source. The shapecharacteristics of each aggregate sample were quantified using the AIMS2 device prior toconditioning. The No. 4 aggregates were then conditioned in theMicro-Deval at 20 minute

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intervalsuptoatotalof100minutes,andtheNo.16aggregateswereconditionedat10minuteintervalsupto30minutes.TheAIMS2devicewasusedtomeasurechangesinaggregateshapecharacteristicsaftereachMicro-Devalconditioninginterval.

Fieldfrictionmeasurementrecordsusingthelocked-wheelskidtrailerat40milesperhourandequippedwitharibbedtestingtirewereobtainedfromtheselectedNCATTestTracksections.TheAIMS2 lab resultswerecomparedwith the locked-wheel skid trailer field frictiondata todetermine any correlation. Field conditioning intervals and laboratory conditioning intervalswerematchedbasedonterminalfriction,whichreferstothepointatwhichfrictionvalues(andAIMS2 indexes) tend to remain constant or decrease at a steady rate. Although frictionnumbersmaydecrease,theslopeoffrictionlossremainsconstantwithincreasedconditioning(2).

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CHAPTER2 LITERATUREREVIEW

2.1 MeasuringFriction

Anumberofdifferentmethodsarecurrentlyusedformeasuringpavementsurfacefriction.TheBritishPendulumTesterisonetoolusedtoquantifyfrictionatlowspeedsforaggregatesandpavementmixturesfollowingAmericanSocietyforTestingandMaterials(ASTM)E303standardtesting procedure. The Dynamic Friction Tester (DFT) is another device used to measurepavement friction of mixtures in the field or laboratory following ASTM E1911. The locked-wheelskidtrailer(ASTME274)isoneofthemostcommonlyuseddevicestoobtainfieldfrictiondata.

2.1.1 Locked-WheelSkidTrailer

The locked-wheel skid trailer test involves a specifically designed trailerwith a testwheel tomeasuretheskidresistanceofpavement.Onceatowvehiclebringsthetrailertothedesiredspeed,waterissprayedinfrontofthetestwheelthenthebrakingsystemlocksthetestwheel.Themeasuredtorquefromtheinteractionbetweenthetireandpavementsurfaceisrecordedalongwiththetestspeedtocalculateaskidresistancevalue.Thisisreportedasaskidnumber(SN).

The testwheel caneitherbe ribbed (ASTME501)or smooth (ASTME524). The ribbed tire iscurrentlythemostcommontesttireusedintheUnitedStates(1).Figure1showsaribbedtesttire(left)comparedtoasmoothtesttire(right).

Figure1Ribbed(ASTME501)versusSmoothTestTire(ASTME524)(3)

The ribbed tire is sensitive to pavement micro-texture and tends to generate higher skidnumbers,andthesmoothtireismoresensitivetopavementmacro-texture(4).AspartoftheFederalHighwayAdministration’sLongTermPavementPerformancestudy,sixtestsectionsinConnecticutweretestedwiththeskidtrailerusingthesmoothtireandribbedtireat40mph.When the smooth tire test values (SN40S) were compared with the ribbed tire test values(SN40R),theresultsyieldednocorrelation(Figure2).Thisstudyverifiedthatthetwotireswere

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sensitivetodifferentaspectsofpavementtexture(4).Therefore,itisimportanttounderstandtheimplicationsoftiretypeonfrictiontestcomparisons.

Figure2SmoothTireSNsversusRibbedTireSNsCorrelationPlot(4)

2.1.2 LabTestCorrelationswithLocked-WheelSkidTrailer

A2010NCATstudyexaminedthecorrelationbetweentheDFTtestingoflaboratoryslabswiththe ribbed tire skid trailer testingof fieldpavement sectionsat theNCATTestTrack (5). Thestudyselectedfourdifferentasphaltsurfacemixturesfromthe2003TestTrackresearchcycleandprepared slabs for laboratory testing from the2003 surfacemixdesigns. The slabsweretestedusingtheDFTafterincrementsofpolishingappliedbytheThreeWheelPolishingDevice(TWPD).TheTWPDwasdevelopedbyNCATtopolishslabsinordertomeasurethechangeinsurfacetextureandfriction,asdiscussedlater.

The Test Track sectionswere tested using the skid trailer to obtain SN40R skid numbers. Toclarify, thestudy refers to theSN40R (mph)asametricequivalentSN64R (km/h) (5).Testingwas completed at different trafficking intervals, documented as equivalent single axle loads(ESALs).Asexpected,thefrictioncoefficientsobtainedfromtheDFTatvaryingspeedsandtheSN64Rs obtained from the skid trailer decreased with increased polishing and trafficking,respectively. The laboratory DFT values at 60 km/h (DFT60) were used to examine thecorrelation to skid trailer field data (SN64R). The measured friction at DFT60 speed bestmatchedSN64Rdata(5).TheDFTtestsrankedthelaboratoryslabs(Figure3)thesameastheskidtrailerrankedtheTestTrackpavementsections(Figure4).Therankingisshowninrednexttothemixdesignationatthetopofthegraphs.

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Figure3LaboratoryDFT60ValuesversusTWPDConditioningCyclesforFourSlabs(5)

Figure4FieldSN64RversusESALsforFourTestSections(5)

Figure4alsoshowsthatthesurfacemixtureusedonTestTracksectionW7wasreplacedwithadifferentsurfacemixtureaftersixmillionESALsduetoitspoorfrictionperformance.Therefore,the terminal SN64R that was used in the research analysis is circled in red in Figure 4. ThehigherSNsinthefigureforthatsectionportraythefrictionperformanceofthenewmixture.

A positive correlation was developed between the skid trailer SN64R field results andlaboratoryDFT60resultsasshowninFigure5.Notethatthex-axisDFTvaluesweremultipledby100toagreewiththeunitsofmeasureusedforthey-axisskidtrailerfrictionvalues.

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Figure5Laboratory(DFT60*100)versusNCATTestTrack(SN64R)Correlation(5)

Correlating laboratory mixtures to field mixtures is important and useful, but there is aninterest to improve the correlation between an aggregate source’s ability to resist polishingwiththeoverallmixture’sabilitytoresistpolishingundertraffic.Itisvaluableforagenciesandaggregatesuppliersto identifyaggregatesthatwouldbesuitable foruse inasurfacemixturethatarecapableofmaintaininggoodskidresistanceduringthelifeofthepavement.

2.2 LaboratoryConditioningDevices

Asphaltmixturesarecomposedprimarilyofaggregatesand it is important touseaggregatesthatresistpolishingtoensurethatthemixturesmaintaintheirfrictionproperties.Althoughanaggregatemightbeinitiallycharacterizedbyahighlevelofangularityortextureandmeasuregoodfrictionvalues,itmaynotbesuitableforapavementsurfacelayeriftheaggregatecannotmaintainasufficient leveloffrictionduetopolishingundertraffic.Laboratoryequipmentcanassess thepolishing resistanceof aggregatesandpavement surfaces. For thepurposeof thisresearch,theprimaryfocuswillbeontheequipmentusedtopolishaggregates.

Wu et al. tested 16 aggregate sources that varied in performance levels in asphalt concreteusingavarietyofimpacttests,includingtheLosAngelesAbrasionTest,Micro-Deval,aggregateimpactvalue,aggregatecrushingvalue,anddegradationinthegyratorycompactor(6).Table1shows what the researchers characterized as a good, fair, and poor historical pavementperformance rating, but does not focus on friction performance. For the purpose of thisresearchandliteraturereview,thefocuswillbeontheLosAngelesAbrasionandMicro-Devalaggregateconditioningtests,asthesearethetestscommonlyusedintheUnitedStates.

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Table1PavementPerformanceEvaluationCriteria(6)Pavement

PerformanceRating Description

Good Used for many years with no significant degradation problem duringconstructionandnosignificantpopouts,raveling,orpotholesduringservicelife.

FairUsedatleastoncewheresomedegradationoccurredduringconstructionand/orsomepopouts,raveling,andpotholesdeveloped,butpavementlifeextendedforover8years.

Poor Usedat leastoncewhereraveling,popouts,orcombinationsdevelopedduringthefirsttwoyears,severelyrestrictingpavementservicelife.

2.2.1 LosAngelesAbrasionTest

TheLosAngeles(L.A.)Abrasion(ASTMC535)device(Figure6)hasbeenusedtoassesscoarseaggregate resistance todegradation. Previous research concluded that the L.A.Abrasion testdoes not correlate well with field pavement performance and acts more as an impact testratherthansimulatingthepolishingactionfromheavytrafficinthefield(6).

Figure6L.A.AbrasionTestingEquipment(7)

Figure7presentstheL.A.Abrasionresultsofthesixteenaggregatesourcescomparedwiththehistoricalpavementperformancerating.TheseresultsdemonstratethattheL.A.Abrasiontestwas not capable of delineating between aggregates as related to good, fair, and poorperformance. In some cases, aggregate sources characterized as poor resulted in an L.A.Abrasionmasslossclosetoaggregatesourcesthatwerecharacterizedasgood.

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Figure7PavementPerformanceRatingswithL.A.AbrasionResults(6)

2.2.2 Micro-DevalAggregateConditioningTest

TheMicro-Deval device (Figure 8) is used to condition an aggregate sample tomeasure theaggregate’s resistance topolishing,abrasion,andbreakage.Previous researchhas shown theMicro-Deval test to be a good tool in evaluating coarse aggregatequality in thepresenceofwater.Anaggregate’squalityandabrasionresistancemaybecategorized intothreedifferentperformancelevelsasgood,fair,orpoordependingonthepercentlossmeasuredafterMicro-Devalconditioning.

Figure8Micro-DevalDevice

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As mentioned previously,Wu et al. tested sixteen aggregate sources from asphalt concretepavements with varying field performance. The study compared Micro-Deval performanceratings(good,fair,orpoor)withthesubjectiveTable1ratingsoftheasphaltconcretemixturesmadebyvariousstatetransportationagencies.TheyfoundthattheMicro-Devalratingswerecomparabletotheagencyratingsformostoftheaggregatesources.AsshowninFigure9,thesolidhorizontallinesdisplaytheaverageoftheMicro-Devalmasslossforeachofthedifferentgroups rated by the state agencies. Wu et al. determined that 18% mass loss delineatedaggregatesofpoorqualityfromthefairandgoodqualityaggregates,whichisdepictedbythedashedlineinthefigure.ItshouldbenotedthattheMicro-Devaltestwastheonlyimpacttestthatshowedcleardelineationsamongtheaggregategroups(6).

Figure9PavementPerformanceRatingswithMicro-DevalAbrasion(6)

Similarly, Cooley et al. selected 72 different aggregate sources from eight different states(Alabama, Georgia, Florida, Kentucky, Mississippi, North Carolina, South Carolina, andTennessee)andevaluatedtheirqualityusingL.A.AbrasionandMicro-Devaltests(8).ThestudynotedmixedresultsintheMicro-Deval;forexample,onlyMicro-DevalresultsforAlabamaandGeorgia distinguish between the three categories of aggregates (good, fair, and poor) ofhistoricalperformance.TheresearcherssuggestedthatspecificationsfortheMicro-Devaltestmethodbedependentonparentaggregatemineralogy.Despitethemixedresults,theMicro-Devalisstillconsideredtobeausefultoolinevaluatingaggregatequality.

2.2.3 L.A.AbrasionandMicro-DevalTestDifferences

As theMicro-Deval test procedure developed, it was compared to the widely accepted L.A.Abrasion. Research showed that the two tests resulted in significantly different results. Theimpact from the L.A. Abrasion test simulates the impact that aggregates experience duringhandling and construction, whereas the Micro-Deval test relates closer to what aggregateswouldexperienceinthefieldwhensubjectedtotraffic.

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Cooleyetal.studied72aggregatesourcesusingtheL.A.AbrasionandMicro-DevalfoundthattheL.A.AbrasionresultedinahigherpercentlossthanthatoftheMicro-Deval(8).ThiscanbeattributedtothefactthattheL.A.Abrasiontestusesfewer,significantlylarger46.8mmsteelchargescomparedtothehighnumberofsmaller,9.5mmchargesusedintheMicro-Devaltest.The baffles inside the L.A. Abrasion drumpick up the aggregate and steel charges and carrythemarounduntiltheydropontheoppositesideofthedrum.Thisharshimpacttendstoyieldhighermasslossvaluesforsomehighqualityaggregatesthathaveotherwiseperformedwellinthefield(9).

The L.A. Abrasion test is performed on oven-dried aggregates and the Micro-Deval testsaggregatesinwater.Aggregatesarerarelydryinthefieldandtheyexperiencemorepolishingand abrasion than the typeof impact simulatedby the L.A.Abrasion test (9). Therefore, theMicro-Devalappearstobemoreappropriate inevaluatinganaggregate’sresistancetotrafficwearwhiletheL.A.Abrasiontestismoreappropriateinassessingaggregatebreakdownduringhandling,mixing,andplacement.

2.3 AggregateImagingSystems

AggregateimaginganalysisisanautomatedprocessofquantifyingaggregateshapepropertiestoprovideamoreobjectivemeasureofSuperpaveconsensuspropertytests,suchasAmericanAssociation of State Highway Transportation Officials (AASHTO) T304: Uncompacted VoidContentofFineAggregates,ASTMD4271:FlatandElongatedParticles inCoarseAggregates,andASTMD5821:PercentofFracturedParticlesinCoarseAggregates.Severaldifferentimagingsystems have been developed and evaluated within the last decade to assess the ability toappropriatelycharacterizeaggregateshapeproperties.Thefocusofthisworkisontherecentlydeveloped AIMS2. A recent National Cooperative Highway Research Program studyrecommendedtheAIMS2devicetoquantifyaggregateshapecharacteristics(10).

2.3.1 TheSecondGenerationAggregateImagingMeasurementSystem

The Pine Instruments’ AIMS2, as shown in Figure 10, is a computer automated system thatcapturesaggregateimages.Thedeviceanalyzestheseimagestoquantifybothcoarseandfineaggregateshapecharacteristicsthroughaseriesofalgorithms.TheAIMS2isequippedwithtwolighting configurations (back lighting and top lighting) and a microscope-camera systemenclosedinacasetoprotectitfromoutsidelightsources(11).

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Figure10SecondGenerationAggregateImagingMeasurementSystem(12)

Aggregate particles placed on a rotating tray are individually scanned such that the cameracaptures images of each particle. Coarse aggregates require three separate scans,while fineaggregatesonlyuseasinglescanforanalysis.Forcoarseaggregates,thefirstscanusesthebacklightingtocaptureablackandwhitesilhouetteofeachaggregate.Thisscanisusedtoquantifythe aggregate’s angularity using the gradient method, as discussed in the next chapter.Additionally, the first scan is used to record the centroid of the particle so the systemmayrecognizetheparticlelocationforadditionalscans.Toplightingisusedduringthesecondscanto determine particle height measurements. The third scan captures greyscale images toanalyzeeachparticle’ssurfacetexturefromwaveletanalysis.Thesethreescansarecritical inquantifyingcoarseaggregateangularity(CAA),particlesurfacetexture,sphericity,andflatandelongated (F&E) ratios. For fine aggregate, a single scan is used to quantify fine aggregateangularity(FAA)andtwo-dimensionalform(Form2D).TheAIMS2softwareprogramexportsthedata into a spreadsheet containing the relevant shape characteristics for each particle andcorrespondingstatisticsforthesamplegroup.

2.4 RelevantResearchonAIMS

ResearchersareworkingtoreplacethemanualSuperpavetestswithanautomatedaggregateimaging system. Superpave consensus property tests are commonly used as part ofmaterialacceptanceforseveralstateagencies,buttheyarelaborious,timeconsuming,andsubjective.

Gudimettlaetal.comparedresultsobtainedfromSuperpaveconsensuspropertytestswiththefirstgenerationAIMStestresultsforavarietyofaggregates(12).ThefirstgenerationAIMSFAAand Form2D results ranked the aggregates the same when compared with AASHTO T304results.OnesetofresultsisshowninTable2withthenumericalrankingrepresentedasaletterinparenthesis.

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Table2ExampleofResultsObtainedfromAASHTOT304,AIMSFAA,andAIMSForm2D(12)

AASHTOT304FAA

(MethodA)

AIMSFAA AIMSForm2D

2.36mm

1.18mm

0.600mm

2.36mm

1.18mm

0.600mm

1/4"Washed 44(A) 4,081(A) 4,729(A) 4,422(A) 8.5(A) 9.0(A) 8.8(A)2AGravel 42(C) 3,271(C) 3,205(C) 3,381(C) 6.8(C) 6.5(C) 7.3(C)CGravel 42.8(B) 3,342(B) 3,677(B) 3,902(B) 6.9(B) 7.4(B) 8.3(B)

A comparison of the results from ASTM D5821 and AIMS CAA appeared to be inconclusivebecausetheydonotmeasuresimilarproperties.Amajorityoftheaggregateswerereportedtohave 100 percent fractured faces when following ASTM D5821 procedure, while the AIMSprovided different angularity values for those aggregates. The ASTM procedure does notmeasure angularity and the AIMS does not detect fractured faces (12). This introduces apotential limitation with the ASTM procedure, in that an aggregate source may becharacterizedbyhigherangularitywhencomparedtoanotheraggregatesourceasdefinedbyAIMS,but the two couldhave the samepercentageof fractured faces. TheASTMprocedurewould show no indication of any differences between the two aggregate sources whenconsideringangularity.Thereisaneedtobeabletosufficientlyquantifythisparameter.Table3showsafewexamplesoftheproject’sresultsobtainedfromASTMD5821andAIMSCAA.

Table3ExampleofCAAResultsObtainedfromASTMD5821andAIMS(12)Project

IDCoarse

AggregatesASTMD5821CAA

1FracturedFace(%)ASTMD5821CAA

2FracturedFaces(%) AIMSCAA

NJ06718's 100 100 2,74857's 100 100 2,995RAP 99 1 2,714

ME0570 1/2's 99 98 3,509

NE05691/4Washed 100 100 2,5852AGravel 32 21 2,248CGravel 60 43 2,314

Gudimettla et al. provided useful insight towards the advancement of using an automatedaggregate imaging system as an alternative to Superpave consensus property tests, butSuperpaveconsensuspropertytestsarenot intendedtoevaluateanaggregate’scapabilityofprovidingadequate friction toasurfacemixture (12).Superpaveconsensusproperty testsdonotadequatelyprovidecoarseaggregatesurfacemicro-texturemeasurements,buttheAIMSiscapable of measuring micro-texture, an important aggregate property that influencespavementfriction(1).

Although the first generationAIMS devicewas used in the Superpave andAIMS comparisonstudy, itcanbeassumedthatwith theadvancements in theAIMS2device, theresultswouldyieldsimilarrankingstothatofthefirstgeneration.AspartoftheAIMS2implementationstudy,resultsgeneratedbythefirstgenerationAIMSdevicewerecomparedwiththoseofthesecond

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generation for a variety of aggregate sources, including 32 coarse aggregate samples and21fineaggregatesamples.Bothsystemsrankedtheaggregatesourcesthesameamongeachoftheshapeparametersandprovidedcomparableresults(13).

The University of Illinois at Urbana-Champaign conducted a series of research tasks for anongoingproject incooperationwith the IllinoisDepartmentofTransportation,which focusedonevaluatingthefeasibilityofusingtwoaggregateimagingdevices,AIMS2andtheEnhancedUniversityof IllinoisAggregate ImageAnalyzer, inconjunctionwiththeMicro-Deval (14).Oneresearchtaskdeterminedthat210minuteswassufficientpolishingtimeintheMicro-Devalfortheaggregatestoreachterminaltextureandangularity.However,forbothimagedevices,therateoftexturelossreducedsignificantlyafter105minutesofconditioningintheMicro-Deval,indicatingtheinitialpointatwhichtheaggregatesbeganapproachingterminalvalues.

Another University of Illinois research task tracked an aggregate’s resistance to polishing,abrasion,andbreakage(16).Researchersretainedthematerialcapturedonthe9.5mmsievefor11aggregatesourcesbeforesubjectingthemtoconditioningintheMicro-Devalfor15,30,45, 60, 75, 90, 105, 180, and 210minutes. The aggregate samples were analyzed using theAIMS2and Illinoisdeviceprior toconditioningandaftereachconditioningcycle.ResearchersfoundthatbothimagingdeviceswerecapableofdetectingaggregatedegradationintheMicro-Deval.

Theresearchersdevelopedregressionequationsusingstatisticalanalysis topredictangularityandsurfacetextureasafunctionofMicro-Devalconditioningtimeforeachaggregatetypeanddevice in the form of Equation 1, Surface Texture Index (16). Each aggregate source wascharacterizedbyauniqueequationwithdifferentfittingparameterstoshowtherateoftextureorangularityloss.Table4showsanexampleofthefittingparametersusedtomodeltheAIMS2angularityresults,andTable5showsanexampleofthefittingparametersusedtomodeltheAIMS2textureresults.

𝑆ℎ𝑎𝑝𝑒 𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝑡 = 𝑎 + 𝑏 ∗ 𝑒𝑥𝑝!!" (1)wherea,b,c= Fittingparametersbasedonstatisticalanalysisrelatingtoinitial,final,andrate

ofchangeintexturet = Micro-Devalconditioningtime(minutes)

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Table4ExampleofAIMS2FittingParametersforPredictingAngularityLossfromMicro-DevalConditioning(16)

Table5ExampleofAIMS2FittingParametersforPredictingSurfaceTextureLossfromMicro-DevalConditioning(16)

While both measurements were successful in detecting aggregate degradation from Micro-Devalconditioning,thereweresomesignificantdifferencesinthetextureandangularityresultsfor the particles analyzed. The AIMS2 had higher repeatability and was considered a moredesiredtest.Moavenietal.alsospecifiedthattheAIMS2resultsshowedabettercorrelationwithhistoricalfrictiondataobtainedfromIllinoisDepartmentofTransportation(16).

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Mahmoudetal. andMoavenietal. concluded that theuseof theMicro-Deval foraggregatepolishinginconjunctionwiththeAIMS2foraggregateshapeanalysisprovedtobefeasibleforcoarse aggregates (14, 15).With the success of this study, research should be taken a stepfurther to show correlations of the polishing curveswith field friction data. Additionally, thefeasibility of using Micro-Deval with AIMS2 to detect changes in fine aggregate shapeproperties should be explored, as they are an important part of themix design. Correlatingthesetextureandangularityindexesobtainedfromtheimagingdeviceswithfieldfrictiondatashould be further studied, as these aggregate properties influence pavement performance,especially friction, and could be used as part ofmaterial acceptance tests and contribute toenhancingroadwaysafety.

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CHAPTER3 AIMS2TESTDESCRIPTION

3.1 AIMS2AggregateTesting

The AIMS2 automatically quantifies aggregate shape characteristics. As a tray of aggregatesrotates,amicroscope-camerasystemcaptures imagesof theaggregatesusing top lightingorbacklighting.AphotoofthiscamerasystemisshowninFigure11.

Figure11AIMS2Microscope-CameraSystem

TheAIMS2softwareprogramuses the imagesofeachcoarseaggregateparticle (retainedontheNo.4sieve)tocalculateCAA,surfacetexture,sphericity(three-dimensionalform),andF&Eratios. For fine aggregates (passing theNo. 4 and retainedon theNo. 200 sieve), the imagecalculatesFAAandForm2Dbutdoesnotmeasurethesurfacetexture.Thesoftwareallowstheoperatortoselecttheaggregatesizethatisbeinganalyzedandtheparticlecounttheywishtoachieve.Forthepurposeofthisresearch, theAIMS2scannedallparticlesplacedonthetray,resulting ina rangeofapproximately60-90 scannedparticles for coarseaggregatesand160-300scannedparticlesforfineaggregates.

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3.1.1 AIMS2CoarseAggregateTesting

Eachcoarseaggregateparticlewasplacedinthetrough,andparticleorientationdependedonhowtheaggregaterandomlycametorest(Figure12).Thedesignofthetrayandtroughallowsthesystemtoaligneachaggregatedirectlyunderthecameratoensurethateachaggregateisinfullviewforimageacquisition.

Figure12CoarseAggregatesSpreadonTrayTrough

The firstpassof imageacquisitionscannedeachaggregateunder thecameraunitusingbacklighting.This createdablackandwhite silhouetteofeachparticle todetermine thecentroidandquantifyangularity.Iftheentireparticlewasnotincameraview,theparticlewasrejectedfromtheanalysis,andthetrayrotatedtothenextparticle.Thesecondscanusedtoplightingtomeasure the particle height at the previously determined centroid. On the third scan, thecameraunitmagnifiedtheparticletogenerategrayscale imagestocaptureaggregatesurfacetexture,as shown inFigure13.The softwareprogramappliedmeasurements from the threescansinaseriesofalgorithmstocalculateAIMS2CAA,texture,sphericity,andF&Eratios.

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Figure13GrayscaleImageUsedtoCaptureCoarseAggregateTexture

3.1.2 AIMS2FineAggregateTesting

Fineaggregateimageacquisitiononlyrequiresonescantoobtaintheparticleshapeproperties.Atransparenttraysimilartowhatisusedforthecoarseaggregatesusesbacklightingtocaptureaggregateimages.Anopaquetray,designedforsmallerfineaggregates(retainedonorpassingthe No. 50 sieve) andmore translucent particles, uses top lighting for image scans. For thisresearch, the transparent tray was used for No. 16 aggregates that were dark enough forimagingpurposes.

Fine aggregate particleswere sprinkled into the rotating tray’s trough until the traywas full(Figure14).Particleorientationwasbasedonhow theparticle randomlycame to rest in thetrough. It was important to spread out the fine particles but it was difficult to control theplacement.Thecamerasystemusedbacklightingtocaptureimagesasthetrayrotated.TheseimageswereusedtoquantifyAIMS2FAAandForm2D.

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Figure14FineAggregatesSpreadonTrayTrough

3.2 AIMS2AggregateShapeMeasurements

The AIMS2 software uses scanned images to calculate the necessary aggregate propertiesthrough a series of algorithms. The results are thenexported into aMicrosoft Excel file thatmaybeusedforfurtheranalysis.TheExcelfileisorganizedbyshapeparameterandincludesallthe raw data, corresponding basic statistics, and a graph that reflects the cumulativedistributionoftheparticlesforeachparameter.

3.2.1 AggregateAngularity

Coarse and fine aggregate angularity is captured from two-dimensional images of the firstAIMS2scan.Agradientmethodquantifiesthevariationsattheparticleboundaryusingascaleof 0 to 10,000. A particle characterized as a perfect circle would have an angularity indexapproachingavalueof0.Thesharperthecornersoftheparticleare,orthegreaterthechangeininclinationofthegradientvectorsontheouteredgepoints,thehighertheangularityindex.AASHTOTP81definesanangularityindexof3,300orlessaslowangularity,3,300to6,600asmedium angularity, and greater than 6,600 as high angularity. Figure 15 provides arepresentationofhowgradientvectorsappearforasmoothparticleversusanangularparticle.

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Figure15GradientVectorforSmoothversusAngularParticle(16)

3.2.2 SurfaceMicro-texture

Surfacemicro-texture isa coarseaggregatepropertymeasuredusingwaveletanalysis,whichprovidestextureinformationinthehorizontal,vertical,anddiagonaldirections.Particlemicro-texture refers to the smoothness or roughness of a particle surface and is dependent onparticle surface irregularities at a wavelength less than 0.5 mm. According to the AIMS2method, the surface texture index ranges from 0 to 1,000 and is calculated “at a givendecompositionlevel[as]thearithmeticmeanofthesquaredvaluesofthewaveletcoefficientsfor all three directions” (10). A surface texture index approaching 0 indicates a smooth,polishedaggregatesurface.AIMS2denotesatextureindexof260orloweraslowtexture,260to550asmediumtexture,andabove550ashightexture.

3.2.3 AggregateForm

CoarseandfineaggregateformischaracterizedbytheAIMS2usingtheimagescapturedduringtheaggregatescans.Figure16showsarepresentationofwhatisreferredtoasformaccordingtotheAIMS2device.

Figure16RepresentationofAIMS2AggregateForm(11)

Coarseaggregateform,referredtoassphericityinAASHTOTP81,isusedtodescribetheoverallthree-dimensionalshapeoftheparticle.Sphericityrangesonascaleof0to1,whereaparticle

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that is characterized by equal dimensions (cubical) has a sphericity value of 1. Therefore, avalue that is characterized as a perfect circle would have a sphericity index approaching 0.AccordingtotheAIMS2software,anindexof0.3orlessisconsideredlowsphericity,0.3to0.7isconsideredmediumsphericity,andanindexgreaterthan0.7isconsideredhighsphericity.

Fine aggregate form (Form2D), referred to as two-dimensional form in AASHTO TP 81, isindicativeofthechangesintheparticleradiusinalldirectionsofatwo-dimensionalimage.Theparticleradius isdefinedasthedistancebetweentheparticlecenterandtheouteredgeatagivenpoint.Form2Discalculatedusinganequationwithvaluesrangingfrom0to20,whereaparticlecharacterizedasaperfectcirclewouldhaveavalueapproaching0.TheAIMS2softwareindicatesthataForm2Dvalueof6orlessisconsideredlow,6to12isconsideredmedium,andavaluegreaterthan12isconsideredhigh.

3.2.4 FlatandElongatedProperties

The F&E properties for coarse aggregates are represented using four ratios based on themeasured particle dimensions from the three scans: flatness ratio, elongation ratio, flat andelongatedratio,andflatvalueandelongatedvalueperAASHTOTP81.ThevariablesusedintheF&Eratiosaredefinedthesameastheywereforcalculatingsphericity.Foreachoftheaboveratios, the AIMS2 software records the cumulative percentage of particles that werecharacterizedbyaratiogreaterthan1:1,2:1,3:1,4:1,or5:1.Duetothelargeamountofdata,onlytheF&Eratiowasusedforthecoarseparticlesforthisresearch.

The range of aggregate shape values AIMS2 considers to be low, medium, and high aresummarizedinTable6.

Table6SummaryofAIMS2IndexRangesAIMS2Index AggregateSize Low Medium HighAngularity Coarse,Fine 0-3,300 3,300-6,600 6,600-10,000Texture Coarse 0-260 260-550 550-1,000Sphericity Coarse 0-0.3 0.3-0.7 0.7-1.0Form2D Fine 0-6 6-12 12-20

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CHAPTER4 EXPERIMENTALPLAN

Thepurposeofthisresearchwastoanalyzethe laboratorypolishingresistanceofaggregatesusingof theMicro-Deval andAIMS2 compared to field frictionperformancedata. This studyappliedportionsofexistingteststandards,including:ASTMD7428,ASTMD6928,andAASHTOTP81.Micro-Deval andAIMS2 testingwere conducted in theNCAT laboratoryon coarse andfine particles of five aggregate sources. Particle shape properties were analyzed using theAIMS2priortoanyconditioningandafterincrementsofMicro-Devalpolishing.

4.1 SelectionofNCATTestTrackPavementSections

Pavement sections from the 2000 and 2009 NCAT Test Track research cycles were selectedbased on their variation in mixture properties, field friction performance, and aggregateavailability.Pavementsurfacemixturesthatcontainedahighpercentageofreclaimedasphaltpavment(RAP)materialwereavoidedtoensurethatthefrictionperformancewasdominatedbythevirginaggregatesinthemixtureandnotthereclaimedaggregate.Historicalfieldfrictiondata were obtained from NCAT’s database for each selected pavement section to serve asgroundtruthfieldperformancemeasures.TestTrackfrictionmeasurementswerecollectedtoobtain skid numbers (SN40R) for each pavement section on a monthly basis following thetestingprotocolsetforthinASTME501.ThefrictionresultsforthisstudywereobtainedfromNCAT’shistoricdatarecords.

Thefirstsurfacemixselectedwasahighfrictionsurfacetreatment(HFST)composedofcalcinedbauxiterangingfromNo.5toNo.12sizedparticles.Thesecondmixtypewasanopengradedfriction course (OGFC) composedprimarily of LaGrange granite. The third and fourth surfacemixes,characterizedasastonematrixasphalt (SMA)mixanda fine-densegraded(FDG)mix,were composedprimarily of Columbus granite. The fifthmix typewas an SMAmix from the2000NCATTest Track research cyclemadeprimarily of Calera limestone. Table 7 provides asummary of the mixes selected and the mixture identifications used throughout the study.SomeofthetestsectionscharacterizedasanFDGColumbusgranitesurfacemixturecontainedupto25%RAP,buttheRAPshowednoeffectonfrictionfieldperformance.TheFDGmixdesignwithoutRAPwasusedinthecontrolsection,S9(17,18,19).

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Table7NCATTestTrackSectionMixtureIdentification

MixtureID YearConstructed Sections NMAS Materials

HFSTBauxite 2006 E2,E3 N/A CalcinedBauxite

OGFCLaGrangeGranite 2009 N1,N2,S8 12.5mm

78LaGrangeGranite

CoarseFractionRAP(15%bymixweight)

SMAColumbusGranite 2009 N12 12.5mm

7ColumbusGranite89ColumbusGranite

Other:FlyAsh,HydratedLime,Cellulose

FDGColumbusGranite 2009

N5,N6,N7,S9,S10,S11,

S129.5mm

89ColumbusGranite8910OpelikaLimestoneScreenings

M10ColumbusGraniteShorterCoarseSand

SMACaleraLimestone 2000 W7 9.5mm

7CaleraLimestone821CaleraLimestone

Other:FlyAsh

4.2 MaterialSelection

Five aggregate sourceswere representedby the selected field frictionperformance sections:Columbusgranite,LaGrangegranite,calcinedbauxite,Caleralimestone,andOpelikalimestone.Eachsourcewasscreenedintoacoarseandfinesample,exceptforthecalcinedbauxite,whichonlyprovidedafineaggregate.Acoarseaggregatesamplewasdefinedasmaterialpassingthe3/8inchsieveandretainedontheNo.4sieve.Afineaggregatesamplewasdefinedasparticlespassing the No. 8 sieve and retained on the No. 16 sieve. Table 8 shows a summary of theaggregateproperties.

Table8SummaryofLaboratoryTestedAggregateProperties

AggregateType SieveSize NCATStockpile Gsa Gsb Absorption

(%)

OpelikaLimestoneNo.4 OpelikaLimestone78 2.863 2.769 1.2No.16 OpelikaLimestone8910 2.812 2.784 0.4

ColumbusGraniteNo.4 ColumbusGranite89 2.713 2.61 1.5No.16 ColumbusGraniteM10 2.739 2.735 0.1

LaGrangeGraniteNo.4 LaGrange78 2.666 2.617 0.7No.16 LaGrangeM10 2.725 2.707 0.3

CaleraLimestoneNo.4 CaleraLimestone78 2.871 2.836 0.4No.16 CaleraLimestone820 2.779 2.645 1.8

Bauxite No.16 N/A N/A N/A N/A

4.3 TestProcedure

Thetestproceduresforthecoarseandfineaggregatesvariedandaredescribedseparately.TheMicro-Devaltestingproceduresforcoarseandfineaggregates(ASTMD6928andASTMD7428)

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wereusedas thebasis forconditioning theaggregates,butmodificationsweremade for thepurposeofthisresearchstudy.Single-sizedaggregatewastested,notthespecifiedgradations.The total conditioning times were modified based on aggregate size and were divided intoincremental conditioning times to track changes in aggregate shape parameters using theAIMS2.

4.3.1 CoarseAggregateMicro-DevalandAIMS2TestingProcedure

ThetestingprocedureusingtheAIMS2andMicro-Devalforallaggregatesourceswith#4sievesizesampleswasasfollows:

1. Theaggregatesourcesweresampledfromtheircorrespondingstockpiles,washed,ovendried,andsievedtoobtainparticlespassingthe3/8inchsieveandretainedontheNo.4sieve.

2. EachprocessedsamplewassplitperAASHTOT248toobtainapproximately1,500gramsasrequiredforMicro-DevaltestingperASTMD6928.

3. The Micro-Deval sample was split per AASHTO T248 to obtain 16 replicate AIMS2samples of approximately 90 grams each. TheAIMS2procedure required a 50 coarseparticle count minimum, which was approximately 90 grams. Using Excel’s randomnumbergenerator,threereplicatesofthe16sampleswereselectedforanalysisintheAIMS2. Each of the three smaller random samples are referred to as replicatesthroughoutthisstudy.

4. The three replicates were measured in the AIMS2 device for angularity, texture,sphericity,andF&Eratios.ThedatawereevaluatedforoutliersandrepeatabilityusingtheMinitabstatisticalsoftwareasdiscussedlater.

5. Theselectedreplicateswererecombinedwiththeothersamplereplicatestomakeupthe1,500 gramMicro-Deval sample. The1,500 gram samplewas then conditioned intheMicro-DevalfollowingASTM6928.The1,500gramsamplewassoakedintheMicro-Devalcontainerforanhourintwolitersofwater,and5,000gramsofsteelchargeswereaddedtothecontainerforconditioning.

Based on the single No. 4 sieve size selected for coarse aggregate testing, a totalconditioning timeof95minuteswaschosen fromthestandardMicro-Devalgradationthat contained the greatest mass of the No. 4 material. Total conditioning time wasrounded up to 100 minutes so the sample could be conditioned in 20-minuteincrements.

Moavenietal.foundthat210minuteswasthenecessaryconditioningtimeforcoarseaggregates to ensure that terminal angularity and texture indexes were reached.However,theynotedthattherateofangularityandtexturelossappearedtoslowdownsignificantly after 105minutes ofMicro-Deval conditioning, indicating 100minutes ofconditioningwasapproachingterminalconditioning(15).

6. After20minutesofMicro-Devalconditioning,thesteelchargeswereremovedusingamagnet.TheconditionedsamplewaswashedovertheNo.16sieve,dried,andweighed.ThematerialpassingtheNo.16sievewasrecordedasmass loss. Inthisstudy,acycle

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includesanincrementof20minutesofpolishingintheMicro-DevalfollowedbyAIMS2testing.ThefirstcycleconsistsofAIMS2testingpriortoanyconditioning.

7. Steps3through6wererepeateduntil thesamplecompletedfiveconditioning/testingcycles.

8. TheMicro-Devalsampleswerenotsievedtoobtainparticlesonlypassingthe3/8inchsieveandretainedontheNo.4sieveafterconditioning.TheAIMS2rejectsparticlesthataretoosmallanddonotfitentirelywithincameraview.Therefore,keepingtheentiresampletogetherwasconsideredappropriate.

4.3.2 FineAggregateMicro-DevalandAIMS2TestingProcedure

ThetestingprocedureusingtheAIMS2andMicro-DevalforallaggregatesourceswithNo.16sievesampleswasasfollows:

1. Theaggregatesourcesweresampledfromtheircorrespondingstockpiles,washed,ovendried,andsievedtoobtainparticlespassingtheNo.8sieveandretainedontheNo.16sieve.

2. EachprocessedsamplewassplitperAASHTOT248toobtainapproximately500gramsrequiredforMicro-DevaltestingperASTMD7428.

3. The Micro-Deval sample was split per AASHTO T248 to obtain 16 replicate AIMS2samples of approximately 30 grams each. The AIMS2 procedure required a 150 fineparticlecountminimum,whichwasapproximately30grams.Threereplicatesofthe16AIMS2 samples were selected using Excel’s random number generator. Each of thethreesmallerrandomsamplesarereferredtoasreplicatesinthisstudy.

4. The AIMS2 measured FAA and Form2D of the three replicates for each of the fineaggregatesources.TheAIMS2datawereevaluatedforoutliersandrepeatabilityusingMinitabstatisticalsoftwareasdiscussedlater.

5. Theselectedreplicateswererecombinedwiththeothersamplereplicatestomakeupthe500gramMicro-Deval sample.The500gramsamplewas thenconditioned in theMicro-Deval following ASTM D7428. The procedure was modified to condition thesingle-sizedaggregatesample,notthegradationprovidedbythetestingstandard.The500gramsamplewassoakedin0.75litersofwaterintheMicro-Devalcontainerforanhour. Approximately 1,250 grams of steel charges were added to the Micro-Devalcontainerbeforeconditioning.

ASTM D7428 specifies a total conditioning time of 15 minutes for the prescribedgradationcontainingthegreatestmassoftheNo.16sizedparticles.Thisstudyelectedto have a minimum of three testing cycles with a minimum of 10 minutes ofconditioningforeachcycle.Basedontheseparameters,thetotalconditioningtimewasmodifiedfrom15minutesto30minutes.

6. After10minutesofMicro-Devalconditioning,thesteelchargeswereremovedusingamagnet.TheconditionedsamplewaswashedovertheNo.50sieve,dried,andweighed.ThemasslosswasrecordedasthematerialpassingtheNo.50sieve.

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ASTMD7428specifiesthatmaterialpassingtheNo.200sieveisconsideredlostmaterialforthespecifiedgradations.ItwasdeterminedthattheNo.200sievewastoosmallofasievesizewhentestingonlyNo.16sizeaggregateparticles.TheMicro-Devalprocedurefor coarse aggregates (ASTMD7428) specified that particles passing theNo. 16 sievewereconsideredlostmaterial,whichistwostandardsievesizessmallerthantheNo.4sizeparticletested.Forconsistency,lostmaterialfortheNo.16fineparticlestestedwascharacterizedasmaterialpassingtheNo.50sieve,twostandardsievesizesbelowtheNo.16sieve.

7. Steps3through6wererepeateduntilthesamplecompletedthreeconditioning/testingcyclesforatotalof30minutesofconditioning.

8. TheMicro-Deval samples were not sieved to obtain particles only passing the No. 8sieveandretainedontheNo.16sieveafterconditioning.ThiswasdonetoensuretheMicro-Devalsampleremainedtogetherthroughouttheentireexperiment.

4.4 DataQualityControl

DataqualitycontrolanalysiswasperformedonAIMS2testresultstodetectoutliersamongthedata sets. The resultswere also checkedwith theAIMS2precision statement to ensure thatrepeatableresultswereachieved.Theseactivitiesaredescribedinthefollowingsubsections.

Some of the replicates appeared to have significantly lower or higher data points for eachshape property, which would misrepresent the true sample properties. Therefore, Minitabstatisticalsoftwarewasusedtoanalyzethedataandidentifyanyoutlierswithinthesample.

AftermeasurementintheAIMS2,thedatafromthethreereplicatesforeachaggregatesourcewere combined into one data set for analysis. The combined three replicates for statisticalanalysisarereferredtoasadataset.Minitabprovidedagraphicalsummaryusingaboxplottorepresent variability of the data. An example is shown in Figure 17. The rectangular boxrepresents50%of thedata and the line in themiddleof the rectangularbox represents themedian value of the data. The interquartile range is computed by subtracting the 25thpercentilefromthe75thpercentile.Thestemsoftheboxplotextend1.5timestheinterquartilerange (20).Anydatapointsoutside these stemsare representedbyanasterisk (*)andwereconsidered“firstorderoutliers”andwereremovedfromthedata.Thesearecircled inred inFigure17.

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Figure17ExampleofRemovingFirstOrderOutliersfromDataSet

Asecond levelstatisticswasperformedonthecleandatasettodetermine if therewereanysecond order outliers. This further analysis determined that all second order outliers wouldremain part of the analysis for all data sets. Minitab graphical summaries showing thedistributionsforAIMS2CAA,FAA,andcoarseaggregatetexturecanbefoundinthethesis(21).

The results for angularity and texturewere only checked against the precision statement inAASHTO TP81. The precision statement specifies acceptable upper and lower limits with acoefficient of variation as a percent of the overall average. The coefficient of variation

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corresponding to AIMS2 angularity indexes is 2.9% and the coefficient of variationcorrespondingtoAIMS2texture indexes is4.5%.Tablesshowingtheoverallmean, lowerandupperlimits,aswellasthemeansofeachreplicateareprovidedinthethesis(21).Someofthereplicates’ means fell outside the precision statement but were reasonably close and wereacceptedforuseindataanalysis.

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CHAPTER5 LABORATORYRESULTSANDDISCUSSION

5.1 Micro-DevalAggregateMassLoss

The test protocol recorded Micro-Deval mass loss for both coarse and fine aggregates aspreviously described in Chapter 4. Material passing the No. 16 sieve was considered lostmaterial for coarse aggregates, and material passing the No. 50 sieve was considered lostmaterialforfineaggregates.TheamountofmasslossduetoconditioningintheMicro-Devalisanindicationofaggregatedurability.Figures18and19showthecumulativepercentmasslossforcoarseaggregates(CA)andfineaggregates(FA),respectively.

Figure18ChangeinCoarseAggregateMassLossfromMicro-DevalConditioning

Figure19ChangeinFineAggregateMassLossfromMicro-DevalConditioning

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As expected, all aggregate sources experienced some mass loss due to conditioning in theMicro-Deval.Asteeperslopeinthefigureindicatesahigherrateofmasslossfortheaggregate.Fineaggregatesexperiencedmorepercentmasslossafter30minutesofconditioningthanthecoarse aggregates after 100 minutes of conditioning, with the exception of the Opelikalimestonesource.Thismaybeattributedtothefineaggregatesampleparticleshavingahighersurfaceareaexposedtoabrasionthanthecoarseaggregate,resultinginmorepiecesbrokenoffofmultiplefineparticles.Thebauxitewascharacterizedbytheleastpercentmasslossamongall fine aggregate samples tested, indicating that the bauxite was the most durable. This isconsistentwithfieldperformance,asbauxiteisthespecifiedaggregatesourceforuseinHFST.

Table 9 shows how the aggregate source rankings differ between coarse and fine aggregatetests.Therankingisbasedonthecumulativepercentlossattheendofthetotalconditioningtime (100 minutes for coarse aggregates and 30 minutes for fine aggragates). Bauxite wasexcludedfromthetablebecauseonlythefineaggregatewastested,andbauxiteexperiencedtheleastamountofmassloss.ThetableshowsthattherankingofaggregatedurabilitybasedonMicro-Devalmasslossvariedbythesizeofparticletested.Furtherdevelopmentoffrictiontestsmayneedtoconsidertheparticlesizebeingtested.Chapter6comparesthetestresultswithfieldfrictionperformance.

Table9PercentMassLossRankingamongCoarseandFineAggregatesafterTotalConditioning

AggregateSource CoarseAggregateRanking FineAggregateRankingOpelikaLimestone 4 1ColumbusGranite 3 4LaGrangeGranite 1 3CaleraLimestone 2 2

1=lowestmassloss,4=highestmassloss

5.2 AIMS2AggregateAngularity

The AIMS2 device measured aggregate angularity for coarse and fine aggregates. Coarseaggregate angularity is associatedwithmacro-texture and both CAA and FAA are associatedwithasphaltmixtureaggregatestructure.Figures20and21showtheaverageAIMS2CAAandFAAindexaftereachconditioninginterval.

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Figure20ChangeinAIMS2CoarseAggregateAngularityfromMicro-DevalConditioning

Figure21ChangeinAIMS2FineAggregateAngularityfromMicro-DevalConditioning

The figures show that the aggregate angularity indexes decreased as conditioning (polishing)timeincreasedintheMicro-Deval.WithintheMicro-Devalcontainer,theaggregateedgesareabrading from the impact of the steel charges and surrounding aggregates. As the rate ofangularity change decreases, the aggregate surfaces becomemore resistant to the abrasion.Moavenietal.observedthatforcoarseaggregates,therateofchangeinangularitysignificantlydecreasedatapproximately105minutesofMicro-Devalconditioningtime(15).Thetestresultsfrom this study showed that the rate of change decreased after 20 to 80 minutes ofconditioning depending on the aggregate source tested. The fine aggregates did not have a

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clear point atwhich the rate of angularity loss reduced significantly. Future research studiesshould consider taking fine particles beyond 30 minutes of conditioning time if a terminalabrasionlossisofinterest.The30-minuteconditioningtimeforthisstudywasdoublethetimespecifiedinASTMD7428.

Thereweredifferencesbetweentheangularityindexesofthecoarseandfineaggregatesfromthe same source. The fine aggregateswere characterized by a higher initial angularitywhencomparedtocoarseaggregates.Anaggregatemayinitiallybecharacterizedbyahighangularityindex, but it is important for the aggregate to be able to maintain an adequate level ofangularity when subjected to polishing. Table 10 shows the percent reduction in angularity(angularity after 30 minutes conditioning divided by the initial angularity) to evaluate theaggregates’durability.

Table10PercentLossinAIMS2AngularityforCoarseandFineAggregatesAggregateSource CoarseAggregate FineAggregateOpelikaLimestone 27.7% 8.9%ColumbusGranite 16.7% 26.5%LaGrangeGranite 19.3% 22.6%CaleraLimestone 19.3% 19.5%

Bauxite N/A 8.5%

ThecumulativedistributionofangularityindexeswastrackedbeforeMicro-Devalconditioning(BMD)andateachconditioningtime(20,40,60,80,and100minutesforcoarseaggregates;10,20,and30minutesforfineaggregates).Figure22providesanexampledistributionforOpelikalimestonecoarseaggregate.TheAIMS2delineationbetweenlow,medium,andhighangularityindexes are labeled and separated by vertical black lines within the graph. The cumulativedistributionsforalloftheaggregatesourcesarefoundinthethesis(21).

Figure22ExampleofAIMS2AngularityDistributionChangeforOpelikaLimestone

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Asimilartrendintheshiftoftheangularitydistributionwasobservedforallaggregatesources.The distribution shifted to the left as the aggregates were conditioned in the Micro-Deval,indicating that a larger percentage of particles became more rounded when subjected toconditioning. The change in angularity distribution decreased with successive increments ofpolishingandwasverifiedusing theKolmogorov-Smirnov test (K-S test) (22). Table11 showsthe K-S test results that correspond to Figure 22. The maximum difference between twosuccessivedistributionsdecreasedasthecumulativeamountofconditioningincreased.TheK-Stestresultsforallaggregatedistributionsarefoundinthethesis(21).

Table11ExampleofK-STestResultsforOpelikaLimestoneNo.4AIMS2AngularityOpelikaLimestoneNo.4Angularity

Comparisons MaxDifference P-valueBMDvs20 0.38 0.0020vs40 0.16 0.0140vs60 0.13 0.0660vs80 0.09 0.3480vs100 0.07 0.65BMDvs100 0.53 0.00

The K-S test results shown in Table 11 also present the corresponding p-value at a 95%confidence interval. A lowp-valuemeans the distributions are significantly different at a 5%level; thesevaluesareshown in red.The initialdistribution,prior toanyconditioning,andatthe total conditioning time were compared to determine that the K-S test recognized asignificantchangeindistributioncausedbyMicro-Devalconditioning.InTable11,theOpelikalimestone coarseaggregatebegins to reach terminalpolishingafter60minutes. TheK-S testresults from theAIMS2FAA showed several cases inwhich thedistributionat30minutesofconditioningsignificantlydifferedfromthedistributionat20minutes.Thisvalidatedthemassloss results that showed the tested fine aggregate sources needed more than 30 minutesconditioningtimetoreachterminalangularityvalues.

Figures 23 and 24 show the cumulative distribution of angularity for each of the coarseaggregate and fine aggregate sources after total conditioning time, respectively. Thedistributioncurveshadsimilarshape,sothelocationofthedistributioncurvesrankssimilarlytothemeanvalueofeachcurve.Distributioncurvesfurthertothelefthadalowermeanvalue.

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Figure23AIMS2CoarseAggregateAngularityCumulativeDistributionsafter100Minutesof

Micro-DevalConditioning

Figure24AIMS2FineAggregateAngularityCumulativeDistributionsafter30Minutesof

Micro-DevalConditioning

5.3 AIMS2CoarseAggregateTexture

Figure25displaystheAIMS2textureindexrecordedaftereachMicro-Devalconditioningcycleforeachcoarseaggregatesource.Thetextureindexrelatestothesurfacemicro-textureoftheindividual particles, a parameter that influences the overall pavement friction. As expected,coarseaggregatesurfacetexturedecreasedwithincreasedMicro-Devalconditioningtime.The

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figureshowsdistinctionsamongthedifferentaggregatetexturevalues.TheColumbusgranitewascharacterizedbytheroughestsurfacetextureandtheOpelikalimestonewascharacterizedbythesmoothestsurfacetexture.Itisimportantforanaggregatetomaintainadequatesurfacetexturewhensubjectedtopolishing.Table12showsthe lossof textureafter100minutesofconditioningasapercentofinitialsurfacetexture.

Figure25ChangeinAIMS2CoarseAggregateTexturefromMicro-DevalConditioning

Table12PercentLossinAIMS2CoarseAggregateTextureafter100MinutesofConditioningAggregateSource PercentTextureLossOpelikaLimestone 23.8%ColumbusGranite 10.2%LaGrangeGranite 14.6%CaleraLimestone 28.2%

Figure25showsthat theCalera limestonehadahigher terminal texture indexthanboththeLaGrangegraniteandOpelika limestone.However,Table12 shows that theCalera limestonehadthehighestrateoftexturelossafterconditioning.IftheMicro-Devalconditioningtimewasincreased,itispossiblethattheCaleralimestonecouldreachaterminalsurfacetexturevaluelessthanthatoftheLaGrangegranite.Thisisanimportantconsideration,asanaggregatethatmight appear to have a higher initial surface texturemay not retain itsmicro-texture underpolishingconditions.Thiscouldresultinamixture’sinabilitytomaintainanadequateamountofpavementfriction.

Similartoaggregateangularity,thecumulativedistributionofcoarseaggregatetextureindexeswere compared. Figure 26 shows an example of the No. 4 Opelika limestone with thecorrespondingK-StestresultsinTable13.ThecumulativedistributionsandcorrespondingK-Stestresultsofalltheaggregatesourcesarefoundinthethesis(21).

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Figure26ExampleofAIMS2TextureDistributionChangeforOpelikaLimestone

Table13ExampleofK-STestResultsforOpelikaLimestoneNo.4AIMS2TextureOpelikaLimestoneNo.4Texture

Comparisons MaxDifference P-valueBMDvs20 0.14 0.0320vs40 0.09 0.4540vs60 0.17 0.0160vs80 0.05 0.9980vs100 0.06 0.92BMDvs100 0.16 0.01

Similar to thetrendsobserved fromtheangularity results, thedistributionof texture indexesshift toward lower texture with increased conditioning time. The differences betweensuccessivedistributionsdecreaseswith increasedpolishingtimeand isquantified inTable13.ThemaximumdifferencedecreasesbyapproximatelyhalfwhencomparingBMDconditioningversus 20 minute, and 80 minute versus 100 minute. There was no statistical differencebetween the samples after 60 minutes of conditioning. This statistical analysis agreed withFigure25,whichshowedtheOpelikalimestonereachedaterminalAIMS2surfacetextureat60minutesofconditioning.

Figure27showsthecumulativedistributionofsurfacetextureforeachaggregatesourceafter100 minutes of Micro-Deval conditioning. The Columbus granite consisted of the largestpercentage of particles characterized by rougher texture compared to the other aggregatesources.Therefore,theColumbusgranitewouldprovidethemostmicro-textureonapavementsurface compared to other sources in this study and should provide high pavement surfacefriction.

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Figure27AIMS2CoarseAggregateTextureCumulativeDistributionDifferencesafter100

MinutesofMicro-DevalConditioning

5.4 AIMS2CoarseAggregateSphericity

Figure28showsthechangeintheaverageAIMS2sphericityindexforeachcoarseaggregateateachincrementalpolishingtime.ThefigurerevealsthattherewasnochangeinsphericitywithincreasedMicro-Devalconditioning.UsingAIMS2criteria,allaggregatesourceshadamediumlevel of sphericity (values between 0.3 and 0.7). TheOpelika limestone consistently had thelowestsphericityindex,indicatingthattheseparticlestendedtobeslightlymoresphericalthantheotheraggregatesources.ThiswasfurtherdemonstratedbythecumulativedistributionofsphericityshowninFigure29.

Figure28ChangeinAIMS2CoarseAggregateSphericityfromMicro-DevalConditioning

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Figure29AIMS2CoarseAggregateSphericityCumulativeDistributionDifferencesafter100

MinutesofMicro-DevalConditioning

5.5 AIMS2CoarseAggregateFlatandElongated(F&E)Ratio

CoarseaggregateF&Epropertiesareassociatedwithconstructiondegregationandnottiedtopavementfriction.Tocompletetheseriesoffrictioncomparisons,theaverageAIMS2F&Eratioforeachcoarseaggregatewasmonitoredtodetermineanytrends.Figure30revealsthattherewas no change in average F&E ratios with increased Micro-Deval conditioning time. Theseresultsagreedwiththeresultsforsphericity.

Figure30ChangeinAIMS2CoarseAggregateF&ERatiosfromMicro-DevalConditioning

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5.6 AIMS2FineAggregateTwo-DimensionalForm(Form2D)

Fine aggregate Form2DwasmeasuredasMicro-Deval conditioning time increased. Figure31showsthatAIMS2Form2Dvaluesdecreasedwithincreasedpolishingtime.This indicatesthatas the aggregateswere polished, they became increasingly circular. Using AIMS2 criteria, allForm2Daverageswerecharacterizedasmediumvaluesfallingwithintherangeof6to12.TheOpelika limestone measured the highest average value, which indicates that it portrayed amore elongated oval shape than the other aggregate sources. Bauxite measured a fairlyconsistentshapethroughoutconditioning,similartoitssmallchangeinangularityvaluesshowninFigure21andTable10.

Figure31ChangeinAIMS2FineAggregateForm2DfromMicro-DevalConditioning

Thecumulativedistributionwastrackedateachconditioningtime.Theprimarypurposeofthiswastoshowhowbauxitecomparedtotheotheraggregatesources.Figure32showstheAIMS2Form2DdistributionofthebauxitebeforeMicro-Devalconditioningandaftereachconditioningtime interval. The bauxite reflected minimal changes in distribution after increasedconditioning,demonstrating itsdurabilityandresistance tochange inshape.Forcomparison,Figure33showstheForm2DcumulativedistributionoftheColumbusgranitebeforeandafterincrementalMicro-Deval conditioning. The correspondingK-S test results are shown in Table14. The distributions of the Form2D value and the corresponding K-S test results for allaggregate sources are in the thesis (21). The Form2D distributions of the other aggregatesourcesweresimilartowhatwasobservedwiththeNo.16Columbusgranitewithanoticeableshiftindistributiontotheleftafterconditioning.

Figure34showsthedistributionsofForm2Dvaluesforeachaggregatesourceafter30minutesofconditioning.TherearefewdifferencesamongtheaggregatesourcesexceptfortheOpelikalimestone. This indicates that there is little variation in the overall two-dimensional shapeamongthefineaggregatestested.

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Figure32ChangeinAIMS2FineAggregateForm2DDistributionforBauxite

Figure33ChangeinAIMS2FineAggregateForm2DdistributionforColumbusGranite

Table14ExampleofK-STestResultsofAIMS2Form2DDistributionsforBauxiteandColumbusGranite

ComparisonsBauxite ColumbusGranite

MaxDifference P-value MaxDifference P-valueBMDvs10 0.04 0.78 0.28 0.0010vs20 0.08 0.12 0.10 0.0020vs30 0.04 0.76 0.09 0.02BMDvs30 0.07 0.15 0.37 0.00

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Figure34AIMS2FineAggregateForm2DDistributionDifferencesafter30MinutesofMicro-

DevalConditioning

5.7 SummaryofLabResults

Allaggregatesourcesweremeasuredformass lossafterconditioningintheMicro-Deval.Thebauxitemeasuredtheleastpercentageofmassloss,indicatingitwasthemostdurableoftheaggregate sources tested. TheAIMS2devicedetected changes in aggregate shapepropertiesamong both coarse and fine aggregates. Both AIMS2 CAA and FAA indexes decreased withincreasedMicro-Devalconditioningtime,indicatingthattheaggregatesbecamemoreroundedalongtheparticleedges.Thebauxiteexperiencedthe leastamountofangularity loss.Coarseaggregatetexturedecreasedwithincreasedpolishing.

Comparing the aggregate sources in this study, the Columbus granite measured a roughersurfacetexturebeforeandafterconditioning,indicatingitwouldbeabetteraggregatesourceforasurfacelayertoprovidesurfacefriction.Incomparison,thetextureindexesofalimestonesourcemaynotprovidesuitablepavementfriction.

Fine aggregate Form2D decreased with increased Micro-Deval conditioning, indicating theaggregates becamemore rounded. The bauxite showedminimal change in its particle shapeafterconditioning,validatingitsabilitytoresisttheeffectsfrompolishing.

No trends were observed for coarse aggregate sphericity or F&E values with increasedconditioningtime.Thesetwoparametersdonotplayaroleinpavementfriction,sothisdidnotintroduceanyhindranceswhenfindingcorrelationsbetweenlabdataandfieldfrictiondata.

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CHAPTER6 COMPARISONOFAIMS2LABRESULTSANDFIELDFRICTION

ThischaptercomparestheAIMS2deviceresultsfromthelabwiththelocked-wheelskidtrailerresults from the NCAT Test Track field test sections. The AIMS2 lab results for the OpelikalimestonewereomittedbecausenoneofthesurfacemixturesontheTestTracksectionswerecomposedofOpelikalimestoneasthedominantaggregateinthemix.

6.1 FieldFrictionData

Locked-wheel skid trailer friction data (SN40R) were obtained from NCAT’s 2009 Test Trackresearchcycle.Fieldfrictiondata,measuredseveralmonthsaftercompletionoftrafficloading,wereomittedduetoirregularitiesinthedata.

Initially, the analysis ranked the field test sections on an individual basis but generatedinconclusiveresults.Therefore,testsectionsweregroupedaccordingtothesurfacemixdesign,resultinginfiveseparategroupsofmixturesassummarizedpreviouslyinTable7.ThesegroupsincludedtwosectionsoftheHFSTusingbauxite,sevensectionsofFDGmixcomposedprimarilyofColumbusgranite,onesectionoftheSMAcomposedofColumbusgranite,threesectionsoftheOGFCcomposedofLaGrangegranite,andonesectioncharacterizedasanSMAcomposedprimarily of Calera limestone. Figure 35 shows the design gradation of the four asphaltmixgroups.BauxiteisnotincludedbecauseitisaHFST.

Figure35MixGradationsforTestTrackSectionGroups

ThefieldresultswererankedbasedonaverageSN40RvaluesforeachgroupasshowninFigure36.Thefrictionvalueat5.3millionESALsforbauxitesectionE3wasveryhigh(68.0)comparedtosectionE2(63.8).Tojustifyremovingthisdatapoint,thedifferenceinSN40RvaluesbetweensectionE3andsectionE2wereexaminedandthedifferenceat5.3millionESALswasmorethantwo standard deviations away from the average difference between the two test sections,indicatingtherewassomeerrorresultinginthemeasurement.Thiswaslikelyattributedtotheskidtrailertestingoutsideofthewheelpath,whichwouldresultinahigherskidnumberthanif

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themeasurementweretakeninsidethewheelpath.Therefore,bauxitesectionE2SN40Rvaluewasused(circledinredinthefigure)asopposedtotakinganaverageofthetwotestsections.

Figure36AverageofFieldSN40RDataBasedonMixType

6.1.1 DefiningTerminalFriction

Agencies generally focus on long-term friction properties, which are commonly called theterminal friction. Figure36 showsan initial decline in field friction values, particularly shownwiththeCaleralimestonesurfacemix.ThefieldfrictiondatashowedtherateoffrictionlossforthismixreducedataroundtwomillionESALs.AttwomillionESALs,alltestsectionsapproachedapointatwhichtheSN40Rvaluesweretrendingtoaterminalfrictioncondition.Forthisstudy,theSN40RatsevenmillionESALswasconsideredtheendpointoftheterminal frictiontrendforallfieldtestsections.Arelativelyflatlineartrendisappropriatetoportrayterminalfriction.Alineartrend-linewasappliedtothefrictiondatafromtwotosevenmillionESALs.ThelineartrendequationsshowninFigure37wereusedtocalculateaverageskidnumbersfromtwotosevenmillionESALsforcomparisonwiththeAIMS2angularityandAIMS2textureindexes.

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Figure37SN40RTerminalFrictionforEachTestTrackGroup

6.2 ComparingAIMS2AggregateAngularitytoFieldFrictionPerformance

AIMS2CAAand FAAwere compared separatelywith the field frictionperformance. The skidnumberobtainedfromthefieldbeforethetestsectionswereopenedtotraffic(0ESALs)wascomparedwithpreconditionedAIMS2indexes.

6.2.1 AIMS2CoarseAggregateAngularityandSN40R

During conditioning, four measurement intervals were selected at specific points in time toevaluate the correlation between the AIMS2 results and field friction. The first intervalcomparedthefieldSN40RwiththeAIMS2priortoconditioningfromtrafficinthefieldortheMicro-Deval inthelab.LabresultspresentedinChapter4showedalineardeclinefrom20to100minutesforAIMS2CAA.Thefieldfrictionperformancedatawerebasedonthelineartrendfrom2to7millionESALs.Theendofthesignificantdecline,AIMS2CAAat20minutesandthefield SN40R at 2 million ESALs were selected as the second interval of comparison. A thirdintervalcomparedpointsinthemiddleofthelineardecline,whichincludedAIMS2CAAat60minutes and SN40R at 4 million ESALs. AIMS2 CAA at 100minutes was compared with theSN40Rat6millionESALsasthefourthinterval.Labtestingwascarriedoutto100minutesofconditioning as a terminal value. It was anticipated that coarse aggregates would reach aterminalvaluebasedonastudybyMoavenietal.,whichfoundtheAIMS2indexessignificantlyreducedafter105minutesofMicro-Devalconditioning(15).Theresultsofthecomparisonareshown in Figure 38. Bauxite is not included in the figure because there were no coarseaggregatetotest.

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Figure38ComparisonofAIMS2CoarseAggregateAngularitywithFieldFriction

Theresultsshowedinconsistenciesamongthedata.Inthefield,theCaleralimestoneshowedbarely any reduction in the SN40R values after the initial twomillion ESALs of conditioning.However, in the laboratory, theAIMS2 angularity continued to decrease fromapproximately2,400to2,200afterinitialconditioningto20minutes.ThesteepslopeoftheCaleralimestonereflectsthechangeinlaboratorypropertieswhilefieldfrictionremainedreasonablyconstant.Asimilar trendwas seenwhencomparing theColumbusgraniteAIMS2angularity indexeswiththefieldfrictionofthetwomixescontainingColumbusgranite.ComparingthelinearportionoftheAIMS2CAAdatawiththefieldfrictionlineartrendprovidedmoreconsistentresults.

InFigure39,trend-lineequationsweredeterminedforthelabvaluesfrom20to100minutestocalculate the average value to be used for comparison after conditioning at 20, 60, and 100minutes. InTable15,theAIMS2CAAtrendvaluesandSN40Rtrendvalueswereusedtoranktheaggregatesandmixtures.Theaggregatesandmixtureswererankedfrom1to3,where1denotes the aggregate ormixture having the highest AIMS2 index or SN40R. The AIMS2 labvaluesat20,60,and100minuteswereplottedagainstthefieldSN40Rfrictionvaluesat2,4,and6millionESALsinFigure40.

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Figure39LinearTrendinAIMS2CoarseAggregateAngularity

Table15RankingAIMS2CoarseAggregateAngularityandFieldSN40RFieldMixID Pre-conditioned 2MESALs 4MESALs 6MESALs

OGFCLaGrangeGranite 1 1 1 1SMA/FDGColumbusGranite 3/1 2 2 2

SMACaleraLimestone 2 3 3 3No.4Aggregate Pre-conditioned 20minutes 60minutes 100minutesLaGrangeGranite 2 2 2 2ColumbusGranite 1 1 1 1CaleraLimestone 3 3 3 3

Figure40ComparingTrend-linesforFieldFrictionandAIMS2CoarseAggregateAngularity

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ThepreconditionedrankingforfieldfrictioninTable15doesnotmatchtherankingbasedontheterminaltrend-line.Withtheexceptionofthepre-conditionedrankings,therankingsofthemix types were identical regardless of which Columbus granite surface mix was used. Thissuggests the mix type had minimal impact on field friction performance. The No. 4 Caleralimestone consistently ranked last after it was subjected to conditioning and the field mixcomposedprimarilyofCaleralimestoneconsistentlyrankedlastafterbeingsubjectedtotrafficpolishing.

Figure40showsapositiverelationshipbetweentheAIMS2CAAandthefieldSN40Rforeachmatchedaggregateandmixture.AdecreaseintheCAAcomparedtoadecreaseinSN40Rofthefieldsurfacemix.Unfortunately,agoodcorrelationcombiningtheAIMS2CAAresultsandthefield SN40R values could not be established. This poor correlation brings to question thecapabilityof theAIMS2/Micro-Deval testprotocol to relate to field frictionperformance.TheAIMS2labresultsshowtheColumbusgranitetobetheaggregatecharacterizedbythehighestCAA,butthefieldresultsshowthemixcontainingprimarilyLaGrangegraniteasexhibitingthebestfieldfrictionperformance. Inthe lab,theLaGrangegraniteexhibitedAIMS2CAAindexesthat were closer to the Calera limestone, an aggregate known to yield poor frictionperformanceinthefield.

6.2.2 AIMS2FineAggregateAngularityandSN40R

AsimilarprocesswascarriedoutforevaluatingthecorrelationbetweenAIMS2FAAoftheNo.16aggregateparticlesand theSN40Rof the field surfacemixtures.TheAIMS2FAAbegan toexhibitalineartrendat10minutes.Alineartrend-linewasappliedtothedatafrom10to30minutesasshowninFigure41.TheequationswereusedtocomputeaverageAIMS2FAAvaluestoranktheaggregatesinTable16andtocomparewiththefieldSN40RfrictionvaluesinFigure42.

Figure41LinearTrendinAIMS2FineAggregateAngularity

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Table16RankingAIMS2FineAggregateAngularityandFieldSN40RFieldMixID Pre-conditioned 2MESALs 4MESALs 6MESALsHFSTBauxite 1 1 1 1

OGFCLaGrangeGranite 2 2 2 2SMA/FDGColumbusGranite 4/2 3 3 3

SMACaleraLimestone 3 4 4 4No.16Aggregate Pre-conditioned 10minutes 20minutes 30minutes

Bauxite 4 3 3 3LaGrangeGranite 2 2 2 1ColumbusGranite 1 1 1 2CaleraLimestone 3 4 4 4

Figure42ComparingTrend-linesforFieldFrictionandAIMS2FineAggregateAngularity

Figure 42 shows a positive relationship between theAIMS2 FAA and field SN40R friction foreachmatchedaggregateandmixture.AdecreaseintheFAAcomparedtoadecreaseinSN40Rof the field surfacemix. The figure indicatesno correlationbetween theAIMS2FAAand thefieldSN40Rvalues.TheCalera limestoneconsistentlyexhibitedthe lowestFAA,whichagreeswith the SN40R results. However, the bauxite, which measured the highest field friction,measured the same FAA as the Calera limestone. If the bauxite results were removed, apossible correlation couldbemadebetweenAIMS2FAAand field frictionamong thegranitesourcesandCaleralimestone.TheAIMS2maynotbesuitableinevaluatingaggregatesourceswithhighpolishresistance,suchasthebauxite.

6.3 ComparingAIMS2TexturetoFieldFrictionPerformance

TheAIMS2coarseaggregatetextureresultsexhibitedalineartrendfrom20to100minutesofconditioningtimesimilartotheAIMS2CAA.Lineartrend-lineswereapplied,asshowninFigure43,andthesamefourincrementswereselectedforcomparison.TheAIMS2labresultsat0,20,

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60, and 100minuteswere comparedwith the field’s SN40R at 0, 2, 4, and 6million ESALs.Figure43plotsthevaluesandTable17ranksthevalues.

Figure43LinearTrendforAIMS2CoarseAggregateTexture

Table17RankingAIMS2CoarseAggregateTextureandFieldSN40RMixID Pre-conditioned 2MESALs 4MESALs 6MESALs

OGFCLaGrangeGranite 1 1 1 1SMA/FDGColumbusGranite 3/1 2 2 2

SMACaleraLimestone 2 3 3 3No.4Aggregate Pre-conditioned 20minutes 60minutes 100minutesLaGrangeGranite 3 3 3 3ColumbusGranite 1 1 1 1CaleraLimestone 2 2 2 2

Figure44showsthattheAIMS2coarseaggregatetexturemeasurementsdidnotcorrelatewiththe field SN40R frictionmeasurements. The AIMS2 texture indexes for the LaGrange granitewereconsistentlyrankedthe lowest,buttheLaGrangegranitesurfacemixexhibitedthebestfieldfrictionperformanceofthemixesstudied.TheLaGrangegranitemeasuredAIMS2textureindexesthatweresimilartotheCaleralimestonetextureindexes,whichwasnotexpected.

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Figure44ComparingTrend-linesforAIMS2CoarseAggregateTextureandFieldSN40R

6.4 ComparingMicro-DevalMassLosstoFieldFrictionPerformance

The cumulative percentage of the aggregate mass loss after Micro-Deval conditioning wascomparedtothefieldSN40Rdata.Table18showstherankingsofthefieldSN40Rvalues,themass loss for the coarseaggregatesafter100minutesof conditioning, andmass loss for thefine aggregates after 30 minutes of conditioning. An aggregate ranking of 1 indicates theaggregate yielded the least mass loss at the end of Micro-Deval conditioning. The rankingsystemforthecoarseaggregatemasslossstartswith2tobeconsistentwiththerankingofthefieldand fineaggregatemass loss, as coarseaggregatebauxitewasnot tested in the lab.Asnotedearlier,thefieldmixtypescontainingColumbusgranitedidnotaffecttheSN40Rrankingafterconditioningandwerecombinedinthetableforsimplicity.

Table18RankingFieldFriction,CoarseAggregateMassLoss,FineAggregateMassLossAggregate FieldSN40R CoarseAggregateMassLoss FineAggregateMassLossBauxite 1 N/A 1

ColumbusGranite 3 4 4LaGrangeGranite 2 2 3CaleraLimestone 4 3 2

Table18showsthatthegoodperformanceofthebauxitemasslossinthelabagreedwiththebauxitefrictionperformanceinthefield.However,theColumbusgraniteandLaGrangegraniteperformedwellinthefieldbutexhibitedasignificantlyhighercumulativemasslosscomparedtotheCalera limestone,whichexhibitedverypoorfieldperformance.Acorrelationcouldnotbemadebetween laboratorycumulativemass lossand field friction.Figure18andFigure19show that cumulative mass loss for both the coarse and fine aggregates failed to reach aterminalvalue,whichmayhaveinfluencedthiscorrelation.

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6.5 SummaryofComparisonResults

The results suggest a possible correlation could be established betweenAIMS2 FAA and theSN40Rdatawhenanalyzingbyaggregatetype.However,acorrelationcouldnotbeconfirmedwith the results obtained from this research study. The AIMS2 CAA and FAAmeasurementsrecognized the Calera limestone to be a poor aggregate for use in a surface mix. The fieldfrictionperformanceofthesurfacemixturecomposedprimarilyofCaleralimestonerankedlastandperformedverypoorlyinthefield.ThecorrelationfailedwhencomparingtheAIMS2FAAof thebauxiteand theSN40R from the field. The lab results revealedbauxite to consistentlyhavethelowestAIMS2FAA,whereasthesurfacetreatmentinthefieldyieldedthebestfrictionperformance.

TheresultsfailedtoshowcorrelationbetweenAIMS2coarseaggregatetextureresultswiththeSN40R measurements. The AIMS2 revealed that the Columbus granite measured a highertexturethantheotheraggregates.However,thefieldresultsshowedthattheLaGrangegranitemix measured higher friction performance, but the LaGrange AIMS2 texture index wascomparabletotheCaleralimestone.ThiswasnotexpectedbecausetheCaleralimestonewasthedominantaggregateusedinapoorperformingmixfromafrictionstandpoint.

TheAIMS2CAA,coarseaggregate texture,andFAA indexescouldnotbecorrelatedwith thefield SN40R results. These results only reflect the findings of this research study. Additionalresearch is needed to confirm these results and identify another method to establish arelationshipbetweenAIMS2indexesandSN40R.

A correlation between the Micro-Deval mass loss and field SN40R values could not beidentified.Therankingbetweenthecoarseaggregatemassloss,fineaggregatemassloss,andSN40Rwerenot consistentbetweenall aggregates testedwith theexceptionof thebauxite.Thelabresultsshowedthecumulativemasslossforcoarseandfineaggregatesdidnotreachaterminalvalueandshowednoindicationofwhenthatvaluecouldbereached.

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CHAPTER7 CONCLUSIONANDRECOMMENDATIONS

AtestingprotocolusingtheAIMS2devicetomeasureaggregatesurfacecharacteristicsandtheMicro-Devalaggregateconditioningdevicewasevaluatedtodetermineifacorrelationcouldbeestablishedwiththelocked-wheelskidtrailerSN40Rmeasurements.Basedontheresults,thefollowingconclusionsandrecommendationsweremade.TheAIMS2CAAandFAAdecreasedwithincreasedMicro-Devalconditioning.TheAIMS2deviceiscapableofdetectingchanges inCAAandFAAwhentheaggregate ispolished intheMicro-Deval. The conditioning time for evaluating the friction of coarse aggregates should beextendedbeyond100minutestoensurethatterminalvaluesarereached.Similarly,theAIMS2FAAfailedtoreachterminalvaluesafter30minutesofMicro-Devalconditioning,whichismorethantwicetheamountofconditioningstatedinASTMD7428.FuturefrictionresearchusingtheMicro-Devalshouldconsiderconditioningfineaggregateparticlesformorethan30minutes.

TheAIMS2devicedelineatedbetweenaggregatetypeswhenanalyzingtheCAA indexesafterconditioning. The two granite sources retained higher AIMS2 CAA than the two limestonesources.However,theAIMS2FAAshowednocleardelineationbetweenaggregatetypes.TheOpelika limestone exhibited the highest AIMS2 FAA,whereas the Calera limestone exhibitedthe lowest.Thehighfieldfrictionbauxiteyieldedthesecond lowestAIMS2FAA indexesafterconditioning.

TheAIMS2CAAshowedsimilardecreasingtrendswithadecreaseintheSN40Rofthemixtureinthefield.However,agoodcorrelationbetweentheAIMS2CAAandthefieldSN40Rcouldnotbeestablished.TheAIMS2FAAdecreasedastheSN40Rdecreased,butagoodcorrelationwasnotestablishedbetweenthetwo.TheAIMS2FAAforbauxitewaslowcomparedtoitshighfieldfrictionperformance.

TheAIMS2devicewascapableofquantifyingchangesinaggregatetexturewhensubjectedtopolishing in theMicro-Deval. The AIMS2 coarse aggregate texture decreased asMicro-Devalconditioning increased.Micro-Deval conditioning of 100minutes appeared to be a sufficientamountoftimefortheaggregatetexturetoreachaterminalcondition.Theconditioningtimecouldbeextendedbeyond100minutestoconfirmthatterminalconditionsarereached.

The AIMS2 coarse aggregate texture consistently ranked the aggregates throughout eachconditioningcycle.TheColumbusgraniteyieldedthehighestAIMS2coarseaggregatetextureindex,whereastheOpelikalimestoneyieldedthelowest.Limestoneaggregateswereexpectedto yield lower indexes because they tend to polish faster in pavement surface mixtures.However, theCalera limestonerankedthesecondhighest in the labbutperformedpoorly inthefieldinregardstofrictionperformance.

TherewasnocorrelationbetweenAIMS2coarseaggregatetextureandfieldSN40Rresults.TheLaGrangegraniteexhibited lowAIMS2coarseaggregatetexture indexes,butthefield frictionresultsweregood.

Micro-Devalmasslossdidnotreachaterminalconditionduringtestingforeithercoarseorfineaggregates. Therefore, a correlationwith the field SN40Rcouldnotbeassessed. Thebauxiteyielded the least amount ofmass loss compared to the other fine aggregates tested, which

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agreedwiththeSN40Rrankingofthebauxiteinthefield.TheCaleralimestoneexhibitedlessmasslossthanotheraggregateswithbetterfieldfrictionperformance.

The AIMS2 in conjunction with the Micro-Deval was unsuccessful in providing a goodcorrelation of aggregate characteristics to field friction results. Only four aggregate sourceswereavailableforexaminingacorrelationbetweenAIMS2labresultsandfieldfriction.Futurefrictionresearchshouldconsidertestingmoreaggregatesources.Onlytwoaggregateparticlesizeswere tested in the labstudy,and field frictionperformance isdependentonmore thanjust thecontributionofa single-sizedparticle.TheAIMS2device is capableofmeasuring thesurfaceof apavement core less than35mm thick. It is recommended that a research studyevaluatethecapabilityofusingtheAIMS2devicetomeasuresurfacepropertiesofapavementcoreanddetermineifacorrelationwiththeSN40Rofthepavementmixtureinthefieldcanbeestablished.

Using the AIMS2 device to analyze single-sized particles was a useful first step towarddetermining if the AIMS2 and Micro-Deval could be used as a laboratory test method foridentifyingfrictionaggregate.Additionalresearchisneededtodevelopastrongerrelationshipbetweenthelabaggregatepropertiesandfieldfrictionperformance.

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