Quantifying life cycle cost and environmental impact of pavements

Pavement Vehicle Interactions Does it Matter for Virginia?Franz-Josef Ulm, Mehdi Akbarian, Arghavan LouhghalamACPA. Virginia Concrete ConferenceMarch 6, 2014With the support of the VDOT Team Thank YOU! Slide #Motivation: Carbon ManagementPavement design and performance:Fuel savingCost savingGHG reductionStrategy for reducing air pollution!

non profit support group for the Route 29 BypassSlide #Ternary diagram: http://geolsoc.eusa.ed.ac.uk/Useful%20Links.htm

Reading Ternary Plots: http://csmres.jmu.edu/geollab/fichter/SedRx/readternary.html. Right side is axis for performance, left side is axis for cost, bottom side is axis for environmental impact.All values must add to 100%2OUTLINE3This is not about Concrete vs. Asphalt, this is about unleashing opportunities for Greenhouse Gas savingsPavement-Vehicle Interaction: Roughness/ Vehicle DissipationDeflection/ Pavement DissipationData Application:US NetworkVA NetworkCarbon Management: how to move forward

Slide #3Force Distribution in a passenger car vs. speed as a percentage of available power output (Beuving et al., 2004; cited in Pouget et al. 2012)Context: Rolling Resistance

Due to PVIs: Texture, Roughness and Deflection Slide #Pavement Texture: Tire industry. Critical for Safety. Tire-Pavement contact area.

Roughness/Smoothness*: Absolute Value = Vehicle dependent.Evolution in Time: Material Specific

Deflection/Dissipation Induced PVI**:Critical Importance of Pavement Design Parameters: Stiffness, Thickness matters! Speed and Temperature Dependent, specifically for inter-city pavement systems

Key Drivers of Rolling Resistance

*Zaabar, I., Chatti, K. 2010. Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U.S. Conditions. Transportation Research Record: Journal of the Transportation Research Board, No. 2155. Pages 105-116.** Akbarian M., Moeini S.S., Ulm F-J, Nazzal M. 2012. Mechanistic Approach to Pavement-Vehicle Interaction and Its Impact on Life-Cycle Assessment. Transportation Research Record: Journal of the Transportation Research Board, No. 2306. Pages 171-179.Slide #5ROUGHNESS / IRI: Dissipated EnergyQuarter-Car Model*

(*) Sayers et al. (1986). World Bank Technical paper 46 (**) Sun et al. (2001). J. Transp. Engrg., 127(2), 105-111.(***) Zaabar I., Chatti K. (2010) TRB, No. 2155, 105-116.VehicleSpecificReferenceIRI-ValueVEHICLESPECIFIC ENERGY DISSIPATION & EXCESS FUEL CONSUMPTION Slide #ROUGHNESS: HDM-4 MODELZaaber & Chatti (2010)

*Zaabar, I., Chatti, K. 2010. Calibration of HDM-4 Models for Estimating the Effect of Pavement Roughness on Fuel Consumption for U.S. Conditions. Transportation Research Record: Journal of the Transportation Research Board, No. 2155. Pages 105-116.Slide #MIT Model Gen II: Viscoelastic Top LayerPkEh = tEsscSpeed DependenceTemperature dependence

* Pouget et al. (2012); William, Landel, Ferry (1980) ** Bazant (1995)Consideration of Top-Layer Viscoelastic behavior, including temperature shift factor:Slide #8Calibration/Validation | Asphalt Lit. DataModel-Based Simulations

Calibration c=100 km/hValidation c=50 km/hSlide #The model is also calibrated and validated using the data from asphalt literature.In the calibration part the base relaxation time at 10 degrees centigrad is estimated at the speed c=100, then the model is validated by comparing the dissipated energy from the model for the speed c=50 km/hr which shows a very good agreement with the results from Pouget et al.

9New Feature: Temperature and Speed Dependence

(Example taken from Pouget et al. (2012)Gen I50 Deg. F68 Deg. FSlide #\delta E = \frac{c_{cr}}{c}\times\frac{{ P^2} }{bkl_s^2} \mathcal{F}\left( \frac{c}{c_{cr}}; \zeta = \frac{ {\color{red} {\bf \tau} (T) \,} c_{cr}}{l_s} \right)

10Can we do better? Yes, we can!

Structure and MaterialMEPDG2011 MIT-ModelPVI ImpactSlide #11LCA plus: MOVING LCA IN THE DESIGN SPACEMEPDGStructurallySound DesignINPUT:StructureMaterialsTrafficClimateDesign CriteriaLCA/LCCAEmbodied + UseOUTPUT:E(t)IRI(t)MaintenanceTraffic-evolutionSustainableDesignOUTPUT:Fuel Con.GHGCosts

OUTPUT:Comparative DesignDesign AlternativesSlide #Network ApplicationUS and VASlide #

FHWA/LTPP General Pavement Study sections (GPS)Data:RoughnessIRI (Year)TrafficLocationPavement typeDeflection:Top layer modulus ESubgrade modulus kTop layer thickness hOther layer propertiesGPS1: AC on Granular BaseGPS2: AC on Bound BaseGPS3: Jointed Plain CP (JPCP)GPS4: Jointed Reinforced CP (JRCP)GPS5: Continuously Reinf. CP (CRCP)GPS6: AC Overlay of AC PavementGPS7: AC Overlay of PCCGPS9: PCC Overlay of PCCACComPCCSlide #14VA Interstate: Road ClassificationPavement type analyzedTypeLane-mileCenter-mileAsphalt (BIT)3,1311,416Concrete (CRCP, JRCP)Composite (BOC, BOJ)4901,221174459Total4,8412,050VA LabelTypeLTPP EquivalentBITBituminousGPS 1,2JRCPJointed reinforced CPGPS 4CRCPContinuously reinforced CPGPS 5BOJBituminous over JPCPGPS 6BOCBituminous over CRCPGPS 9Slide #VA Interstate: Data Overview

Data: 15 interstates, 2 directionYears: 2007-2013Section IDSection milepostAADT, AADTTLayer thicknessesMaterial properties (2007)IRI (t)ACComPCCPavement Type

Slide #Annual Average Daily Truck Traffic (AADTT)

AADTTSlide #

Deflection -Induced PVISlide #Slide #Temperature and Speed Sensitivity: AC in VA

Asphalt Concrete (BIT)Temperature sensitivityone order of magnitude higher dissipation (T= 50 vs. 65 F)

Asphalt Concrete (BIT)Slide #19Temperature Sensitivity: PCC in VA

Concrete (JRCP, CRCP)Temperature sensitivitySmall!

Speed Sensitivity SmallConcrete (JRCP, CRCP)Slide #20

Would this matter for VA? BIT/ACTemperature sensitivity10 Deg. can entail one order of magnitude of higher energy dissipation; thus fuel consumption.

Assume: Bit @ 95%. P=37 tons (3 axles); 0=0.015sAssume: PCC @ 95%. P=37 tons (3 axles); 0=0.015sPCCTemperature sensitivity10 Deg. can entail half order of magnitude of higher energy dissipation; thus fuel consumption.* Temp data from National Oceanic and Atmospheric Administration (esrl.noaa.gov)

Order of magnitude differenceSlide #21VA Network: PVI Deflection TruckExcess fuel consumption due to PVI deflection is 10 times higher on bituminous pavementsc= 100 km/h=62.6 mph; T= 16 C/61 FSlide #22Annual Excess Fuel Consumption: PVI Deflection*2013 data

FC (gallon/mile)c= 100 km/h=62.6 mph; T= 16 C/61 FSlide #PVI-model Gen II:Accounts for the effect of temperature and vehicle speed on the dissipated energy.Quantifies asphalt and concrete sensitivity to speed and temperature.Requires one material input parameter: relaxation time. So far, calibrated and validated using literature data. Link with Master Curve.Simple to use, easy to calculate fuel consumption in excel spreadsheet; thus for LCA use phase

Summary | For DiscussionSlide #IRI-Induced PVI

Slide #IRI: US Network VA Data ComparisonIRI distribution of Virginia and the US network are very similar.

Slide #Comparison of MN final IRI distribution (roughness) to the national IRI distribution.

US data from FHWA highway statistics 200826VA RoughnessAsphalt and composite pavements are maintained equally. Not concrete

*2013 dataSlide ##### Asphalt is regularly maintained (every 7-10 years), where as concrete pavements are maintained mainly through patching with no major maintenance activities throughout their 30-40 year life. Composite pavements are created at the end of life of concrete pavements. 27IRI depends on pavement maintenanceMN (2011)VA (2013)Slide #28Pavement Roughness (IRI)*2013 data

IRI (in/mile)Slide #Excess Fuel Consumption: PVI Roughness*2013 data

FC (gallon/mile)Slide #Annual Expenditure on all Pavements in VADeficient lane miles due to ride quality by pavement type Interstate Pavement Typelane-mile (% total)Deficient lane-miles (% total)*AC3,131 (65%)157 (46%)PCC490 (10%)181 (54%)Total3,621 (75%)338 (100%)Cost aggregated for:Interstate pavementPrimary pavementSecondary pavement

Deficient pavement IRI:Poor: 140-199Very poor: >200*VDOT. State of The Pavement 2012. http://www.virginiadot.org/info/resources/State_of_the_Pavement_2012.pdfSlide #SUMMARY: IRI-induced PVI

Slide #

Total PVI Impact

Slide #Slide #Network: Annual PVI Truck* excess FC per mile*2013 dataImpact Reduction through enhanced pavement design and managementc= 100 km/h=62.6 mph; T= 16 C/61 FSlide #Dividw by mile34Network: Annual PVI Truck Total FCc= 100 km/h=62.6 mph; T= 16 C/61 FSlide #35PVI Total Impact: Roughness and Deflection*2013 data: Trucks

FC (gallon/mile)c= 100 km/h=62.6 mph; T= 16 C/61 FSlide #CARBON MANAGEMENT = Pavement Performance!PVIs contribute highly to pavement induced fuel consumption and GHG emissionsConcrete pavements not utilized to same performance as in other roadway networksHigh deficient lane-milesOlder pavementsRoom for GHG reduction!

ENGINEERING100%Moving tire (top view) is on slope= Deflection induced eXtra-Fuel Consumption

Slide #CARBON MANAGEMENT = Cost Benefit!ECONOMICS = LINGUA FRANCA OF IMPLEMENTATIONLCCA is tool for supporting design decisionsAnalyses typically occur after design process is completeStandard practice does not account for uncertaintyFHWA does not provide guidance on characterizing inputs and uncertaintyECONOMICS100%Slide #LCCA VALUE PROPOSITIONContext: $ 2 Trillion Infra-structure renewal job within tightest budgetary constraints.

Problem: Volatility of construction materials pricing for a fiscally sound decision making.

Solution*: A new LCCA methodology with probabilistic cost modeling of pavement projects, so that decision-makers: Understand the risk of an investment;Select a design based on risk perspective.

ECONOMICSDecision Makers (local, national, and beyond)IMPLEMENTATION@ State Level: Case Study * Swei, Gregory & Kirchain (2013)INVEST INNOVATE INVIGORATE - IMPLEMENT Slide #Uncertainty is pervasive in pavement LCCAConstructionOperationLong life-cycleCash FlowUncertainty & RiskDecisions long before constructionUncertainty in unit construction costsUncertainty in material price evolutionUncertainty in timing of M&R activitiesCSHub approach characterizes uncertainty for all three areasSlide #Drivers of construction cost variationMaterial pricesConstruction methodsWeather and local conditionsProject drift

Drivers of operational cost variationPrices and cost changesRoadway usagePerformance degradation40CSHub LCCA methodology is integrated with pavement design processPresentFutureLCCA ModelIs the difference significant?Relative riskCharacterize drivers of uncertaintyMEPDG OutputFHWA guidance is limitedSlide #IMPLEMENTATION: LCCA Why does it matter?ECONOMICS = LINGUA FRANCA OF IMPLEMENTATIONECONOMICS100%Gambling withCost overrunMinimizing RiskTranslating price volatility into value proposition for Decision MakersSlide #Analysis:LCCA & PVI Pavement maintenance and PVIImpacts from pavement age

Data needs:Longer timeframe (7 years doesnt cover full pavement lifecycle)Pavement maintenances and activityMore PCC data (i.e. I-295)

Implementation:Lets see where this can take us TOGETHER !

Whats next?Slide #We seek your input!

Thank you.References:Louhghalam, A.; Akbarian, M., Ulm, F-J. (2013) Fluegge's Conjecture: Dissipation vs. Deflection Induced Pavement-Vehicle-Interactions (PVI); J. Engrg. Mech., ASCE.Louhghalam, A.; Akbarian, M., Ulm, F-J. (2013) Scaling relations of dissipation-induced pavement-vehicle-interactions; TRB.http://web.mit.edu/cshub/

Slide #Slide #Beyond my pay grade, but

CARBON MANAGEMENT is a vehicle of INFRASTUCTURE MANAGEMENT

Quantitative Sustainability

Together, lets make it a realityPredicting the future?Slide #JPCP Distresses (%slabs)InterstateD4D5D9Transverse Cracking11%10%0%Corner Breaks1%1%2%PCC Patching8%2%2%Asphalt Patching13%12%1%Average Pavement Roughness (in/mile) Poor 140-199JRCP IRI146128104AC IRI877388Pavement IRI is a function of pavement maintenanceSlide #46Comparison: Gen 1 Gen 2 Model GPS-1: AC on Granular BaseGPS-2: AC on Treated BaseGen 1 INPUTGen 2 INPUTThat is, Gen I model is a lower bound.Gen II is more accurate for local response, but requires (at least) one more parameter.Slide #Viscoelastic Modeling | Master Curve

Load Frequency (Speed)TemperatureSimplified approach:1 - Accounts for the load frequency effect using a simple Maxwell model in frequency range of interest.

2 - Accounts for temperature effect in the same way as asphalt literature (e.g. William Landel Ferry equation)From Pouget et al. (2012)Slide #48Principle of Viscoelastic Model Fitting (Using Master Curve)

complicated viscoelastic model Fit for the entire frequency rangeSimplified (Maxwell) viscoelastic model Fit for applicable frequency rangeFind t and ESimplified Maxwell model along with the WLF law accounts for the temperature dependency. Maxwell model with temperature dependencyFrequency rangeof interestSlide #