Conclusions• ACsurfaceproducedmarkedlyhighersurfacetemperatures

thanPCC

• Despitehighersurfacetemperatures,anACoverlayreduced

thermalgradientsinaPCClayer

• EICMsimulationsquantitativelyreproducedtheinsulat-

ingeffect,whichmaycontributetolongevityandimproved

PCCperformance

• Thermalpropertyinputscanhavesignificanteffectonpave-

mentperformancepredictions,andtheseinputsshouldbe

adjustedaspartofalocalcalibrationprocess

• EICMsimulationsproducedtemperaturedistributions

smallerthanthemeasureddistributionswhentheMEPDG

defaultkvaluewasused(1.25BTU/hr-ft-˚F)

• Severalkvaluesweretested,thebestagreementbetween

measuredandmodeleddatawask=0.94BTU/hr-ft-˚F

Acknowledgements• FHWATPF-5(149)Partners

-FederalHighwayAdministration

-MN,CA,andWADepts.ofTransportation

-MinnesotaLocalRoadResearchBoard

• Prof.RandalBarnes,UniversityofMinnesota

MEPDG and EICM Sensitivity to Thermal Conductivity• Measuredandmodeleddataplottedonacumulativefrequencydistributionchart

• ResultsindicatedtheEICMunderestimatedthetemperaturedistributionsina

PCCpavementwithdefaultkvalue,k=1.25BTU/hr-ft-˚F

-Unnecessarilyhighkvaluemaycontributeinparttounderestimation

• k=0.94BTU/hr-ft-˚Fproducedtheclosestmatchtomeasuredvalues

Figure 8a. July, k = 1.25 BTU / hr-ft-°F Figure 8b. July, k = 0.94 BTU / hr-ft -°F

Figure 9a. March, k = 1.25 BTU / hr-ft-°F Figure 9b March, k = 0.94 BTU / hr-ft-°F

• Thermalconductivity(k-value)affectsthetemperatureprofilethroughalayer

• Inputk-valueinfluencesperformancepredictions,especiallyinPCCslabs

EICM Predictions• EICMqualitativelypredictsthermalinsulatingeffect

• Visibleincumulativefrequencydistributionscomparingmeasuredandmodeleddata

• SupportthehypothesisthatanAClayersignificantlyaltersPCClayertemperaturedistributions

Objectives• Evaluatethemodelingofthermalbehaviorinconcreteand

compositepavementsbytheEnhancedIntegratedClimatic

Model(EICM)

• InvestigatebenefitsofthinACoverlaysonthermalcharac-

teristicsofPCCslabsusingMnROADData

• ValidateEICMpredictionsofthermalgradientsthrough

PCCslabs

• InvestigatetheeffectofMEPDGuserinputsforthermal

conductivityofthePCC

MnROAD DataData Collection

• Oneyearofhourlydatafromthermocouple“trees”inPCC

andAC/PCC

• OnlydifferenceinpavementswasthepresenceofAClayer

Figure 1. MnROAD Cells Used in Analysis

Data Filtering

• Subjecteddatatovariousteststoidentifymissingandinsuf-

ficientdata,sensoroutliers,datasubsetoutliers,etc.

Figure 2. Example of Filtered Data

• Suspectdatawereflaggedandexcludedfromanalysis

• Screeningallowedresearcherstomakecomparisonswith

confidence

Evaluation and Local Calibration of MEPDG EICM Model Using MnROAD DataLuke Johanneck, Derek Tompkins, Timothy Clyne, and Lev Khazanovich

MnROAD Data Analysis – Thermal Gradients in PCC LayerEffect of Albedo

• AChashighersurfacetemperature

• GreateroveralltemperaturegradientsinAC/PCCstructure

Thermal Gradients in PCC Layer

• LargerthermalgradientsincompositesystemdoesnotequatetolargerthermalgradientinPCClayer

• AClayerprovidesaninsulatingeffect

• Gradientsmostpronouncedinsummer(seefigureof2-weekdetail)

Figure 4. Thermal Gradients – Relative DepthsFigure 3. Surface Temperature – Albedo Effect

Figure 5. Thermal Gradients – Relative Depths Figure 6. Thermal Gradients – Absolute Depths

Sensor Locations

• Thermocouplesensordepthswererelativeto5”PCCslab

• Topsensorswere0.5”fromtopofPCCsurface

• Bottomsensorswere1”abovebottomofPCClayer

• Identicalverticaldistanceof3.5”

• Asimilarcomparison(Fig.6)wasdoneusingsensorsatsimilarabsolutedepthstoensurethatthediffer-

enceinthermalgradientswasduetoAClayer,andnotsensorpositioning

Legend

Red–AC/PCC

Blue–PCC

Figure 7. Effect of k on Transverse Cracking

Figure 10. Thermal Gradients – Measured vs. Modeled