evaluation and local calibration of mepdg eicm model using

Conclusions • AC surface produced markedly higher surface temperatures than PCC • Despite higher surface temperatures, an AC overlay reduced thermal gradients in a PCC layer • EICM simulations quantitatively reproduced the insulating effect, which may contribute to longevity and improved PCC performance • Thermal property inputs can have significant effect on pavement performance predictions, and these inputs should be adjusted as part of a local calibration process • EICM simulations produced temperature distributions smaller than the measured distributions when the MEPDG default k value was used (1.25 BTU / hr-ft-˚F) • Several k values were tested, the best agreement between measured and modeled data was k = 0.94 BTU / hr-ft-˚F Acknowledgements • FHWA TPF-5(149) Partners - Federal Highway Administration - MN, CA, and WA Depts. of Transportation - Minnesota Local Road Research Board • Prof. Randal Barnes, University of Minnesota MEPDG and EICM Sensitivity to Thermal Conductivity • Measured and modeled data plotted on a cumulative frequency distribution chart • Results indicated the EICM underestimated the temperature distributions in a PCC pavement with default k value, k = 1.25 BTU / hr-ft-˚F - Unnecessarily high k value may contribute in part to underestimation • k = 0.94 BTU / hr-ft-˚F produced the closest match to measured values 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 • Thermal conductivity (k-value) affects the temperature profile through a layer • Input k-value influences performance predictions, especially in PCC slabs EICM Predictions • EICM qualitatively predicts thermal insulating effect • Visible in cumulative frequency distributions comparing measured and modeled data • Support the hypothesis that an AC layer significantly alters PCC layer temperature distributions Objectives • Evaluate the modeling of thermal behavior in concrete and composite pavements by the Enhanced Integrated Climatic Model (EICM) • Investigate benefits of thin AC overlays on thermal charac- teristics of PCC slabs using MnROAD Data • Validate EICM predictions of thermal gradients through PCC slabs • Investigate the effect of MEPDG user inputs for thermal conductivity of the PCC MnROAD Data Data Collection • One year of hourly data from thermocouple “trees” in PCC and AC/PCC • Only difference in pavements was the presence of AC layer Figure 1. MnROAD Cells Used in Analysis Data Filtering • Subjected data to various tests to identify missing and insuf- ficient data, sensor outliers, data subset outliers, etc. Figure 2. Example of Filtered Data • Suspect data were flagged and excluded from analysis • Screening allowed researchers to make comparisons with confidence Evaluation and Local Calibration of MEPDG EICM Model Using MnROAD Data Luke Johanneck, Derek Tompkins, Timothy Clyne, and Lev Khazanovich MnROAD Data Analysis – Thermal Gradients in PCC Layer Effect of Albedo • AC has higher surface temperature • Greater overall temperature gradients in AC/PCC structure Thermal Gradients in PCC Layer • Larger thermal gradients in composite system does not equate to larger thermal gradient in PCC layer • AC layer provides an insulating effect • Gradients most pronounced in summer (see figure of 2-week detail) Figure 4. Thermal Gradients – Relative Depths Figure 3. Surface Temperature – Albedo Effect Figure 5. Thermal Gradients – Relative Depths Figure 6. Thermal Gradients – Absolute Depths Sensor Locations • Thermocouple sensor depths were relative to 5” PCC slab • Top sensors were 0.5” from top of PCC surface • Bottom sensors were 1” above bottom of PCC layer • Identical vertical distance of 3.5” • A similar comparison (Fig. 6) was done using sensors at similar absolute depths to ensure that the difference in thermal gradients was due to AC layer, and not sensor positioning Legend Red – AC/PCC Blue – PCC Figure 7. Effect of k on Transverse Cracking Figure 10. Thermal Gradients – Measured vs. Modeled

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Page 1: Evaluation and Local Calibration of MEPDG EICM Model Using

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

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