1 / 49

Pavement Vehicle Interactions – Does it Matter for Virginia?

Pavement Vehicle Interactions – Does it Matter for Virginia?. Franz-Josef Ulm, Mehdi Akbarian, Arghavan Louhghalam. ACPA. Virginia Concrete Conference March 6, 2014. With the support of the VDOT Team – Thank YOU! . Motivation: Carbon Management. Pavement design and performance:

junius
Download Presentation

Pavement Vehicle Interactions – Does it Matter for Virginia?

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Pavement Vehicle Interactions – Does it Matter for Virginia? Franz-Josef Ulm, Mehdi Akbarian, ArghavanLouhghalam ACPA. Virginia Concrete Conference March 6, 2014 With the support of the VDOT Team – Thank YOU!

  2. Motivation: Carbon Management Pavement design and performance: • Fuel saving • Cost saving • GHG reduction • Strategy for reducing air pollution! non profit support group for the Route 29 Bypass

  3. OUTLINE • This is not about Concrete vs. Asphalt, this is about unleashing opportunities for Greenhouse Gas savings • Pavement-Vehicle Interaction: • Roughness/ Vehicle Dissipation • Deflection/ Pavement Dissipation • Data Application: • US Network • VA Network • Carbon Management: how to move forward

  4. Context: Rolling Resistance • Force Distribution in a passenger car vs. speed as a percentage of available power output (Beuving et al., 2004; cited in Pouget et al. 2012) Due to PVIs: Texture, Roughness and Deflection

  5. Key Drivers of Rolling Resistance • Pavement Texture: Tire industry. Critical for Safety. Tire-Pavement contact area. • Roughness/Smoothness*: • Absolute Value = Vehicle dependent. • Evolutionin 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 *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.

  6. ROUGHNESS / IRI: Dissipated Energy VEHICLE–SPECIFIC ENERGY DISSIPATION & EXCESS FUEL CONSUMPTION • Quarter-Car Model* • Mechanistic/PSD**: with: IRI • HDM-4 Model***: Reference IRI-Value Vehicle Specific (**) Sun et al. (2001). J. Transp. Engrg., 127(2), 105-111. (***) Zaabar I., Chatti K. (2010) TRB, No. 2155, 105-116. IRI measured at c=80 km/h = 50 mph = Damping of Suspension System (Vehicle Specific) (*) Sayers et al. (1986). World Bank Technical paper 46

  7. ROUGHNESS: HDM-4 MODEL • Zaaber & Chatti (2010) • Input: • Measured IRI (t) • Reference IRI, • Vehicle Type • Traffic Volume (AADT, AADTT) • Truck Traffic Distribution • Output: • Excess Fuel Consumption due to Roughness • For vehicle type and total *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.

  8. MIT Model Gen II: Viscoelastic Top Layer c P Consideration of Top-Layer Viscoelastic behavior, including temperature shift factor: Relaxation Time • Bituminous Materials* • Cementitious Materials**: s s k Temperature dependence h = tE E Winkler Length Speed Dependence * Pouget et al. (2012); William, Landel, Ferry (1980) ** Bazant (1995)

  9. Calibration/Validation | Asphalt Lit. Data Calibration c=100 km/h • Model-Based Simulations Vehicle speed ton truck (distribution of loads according to HS 20-44) m (lane width) 40,264 MPa, 35 MPa/m, 0.22 m s Validation c=50 km/h

  10. New Feature: Temperature and Speed Dependence 68 Deg. F Gen I 50 Deg. F (Example taken from Pouget et al. (2012)

  11. Can we do better? – Yes, we can! PVI Impact 2011 MIT-Model MEPDG Structure and Material

  12. LCA “plus”: MOVING LCA IN THE DESIGN SPACE • INPUT: • Structure • Materials • Traffic • Climate • Design Criteria • OUTPUT: • Comparative Design • Design Alternatives MEPDG Sustainable Design Structurally Sound Design • OUTPUT: • Fuel Con. • GHG • Costs • OUTPUT: • E(t) • IRI(t) • Maintenance • Traffic-evolution LCA/LCCA Embodied + Use

  13. Network Application US and VA

  14. FHWA/LTPP General Pavement Study sections (GPS) Data: Roughness • IRI (Year) • Traffic • Location • Pavement type Deflection: • Top layer modulus E • Subgrade modulus k • Top layer thickness h • Other layer properties GPS1: AC on Granular Base GPS6: AC Overlay of AC Pavement GPS2: AC on Bound Base GPS7: AC Overlay of PCC AC PCC Com GPS3: Jointed Plain CP (JPCP) GPS4: Jointed Reinforced CP (JRCP) GPS9: PCC Overlay of PCC GPS5: Continuously Reinf. CP (CRCP)

  15. VA Interstate: Road Classification

  16. VA Interstate: Data Overview • Data: • 15 interstates, 2 direction • Years: 2007-2013 • Section ID • Section milepost • AADT, AADTT • Layer thicknesses • Material properties (2007) • IRI (t) Pavement Type AC Com PCC

  17. Annual Average Daily Truck Traffic (AADTT) AADTT

  18. Deflection -Induced PVI

  19. Temperature and Speed Sensitivity: AC in VA Asphalt Concrete (BIT) Asphalt Concrete (BIT) Speed Sensitivity half order of magnitude higher dissipation ( vs. 60 mph) Temperature sensitivity one order of magnitude higher dissipation (T= 50 vs. 65 F) tons (3 axles); mph; s; VA Interstate database for distributions of of AC tons (3 axles); ; s

  20. Temperature Sensitivity: PCC in VA Concrete (JRCP, CRCP) Concrete (JRCP, CRCP) Speed Sensitivity Small Temperature sensitivity Small! [For pure comparison, assume same as for asphalt] tons (3 axles); mph; s; VA interstate database for distributions of of PCC tons (3 axles); ; s

  21. Would this matter for VA? Order of magnitude difference PCC Temperature sensitivity 10 Deg. can entail halforder of magnitude of higher energy dissipation; thus fuel consumption. BIT/AC Temperature sensitivity 10 Deg. can entail one order of magnitude of higher energy dissipation; thus fuel consumption. Assume: Bit @ 95%. P=37 tons (3 axles); τ0=0.015s Assume: PCC @ 95%. P=37 tons (3 axles); τ0=0.015s * Temp data from National Oceanic and Atmospheric Administration (esrl.noaa.gov)

  22. VA Network: PVI Deflection – Truck c= 100 km/h=62.6 mph; T= 16 C/61 F Excess fuel consumption due to PVI deflection is 10 times higher on bituminous pavements

  23. Annual Excess Fuel Consumption: PVI Deflection c= 100 km/h=62.6 mph; T= 16 C/61 F *2013 data FC (gallon/mile)

  24. Summary | For Discussion • 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…

  25. IRI-Induced PVI

  26. IRI: US Network – VA Data Comparison IRI distribution of Virginia and the US network are very similar.

  27. VA – Roughness Asphalt and composite pavements are maintained equally. Not concrete *2013 data

  28. IRI depends on pavement maintenance VA (2013) MN (2011)

  29. Pavement Roughness (IRI) IRI (in/mile) *2013 data

  30. Excess Fuel Consumption: PVI Roughness *2013 data FC (gallon/mile)

  31. Annual Expenditure on all Pavements in VA • Cost aggregated for: • Interstate pavement • Primary pavement • Secondary pavement • Deficient pavementIRI: • Poor: 140-199 • Very poor: >200 Deficient lane miles due to ride quality by pavement type – Interstate *VDOT. State of The Pavement 2012. http://www.virginiadot.org/info/resources/State_of_the_Pavement_2012.pdf

  32. SUMMARY: IRI-induced PVI • IRI is vehicle specific • Concrete pavements are under-maintained • Difference between pavement systems is IRI-development and pavement aging. Data not consistent with national analyses • Model Development: Reference in/mile = Political decision Higher value of reduces the number of roads contributing to excess fuel consumption.

  33. Total PVI Impact

  34. Network: Annual PVI Truck*–excess FC per mile c= 100 km/h=62.6 mph; T= 16 C/61 F *2013 data Impact Reduction through enhanced pavement design and management

  35. Network: Annual PVI Truck –Total FC c= 100 km/h=62.6 mph; T= 16 C/61 F

  36. PVI Total Impact: Roughness and Deflection c= 100 km/h=62.6 mph; T= 16 C/61 F *2013 data: Trucks FC (gallon/mile)

  37. CARBON MANAGEMENT = Pavement Performance! • ENGINEERING • 100% • PVIs contribute highly to pavement induced fuel consumption and GHG emissions • Concrete pavements not utilized to same performance as in other roadway networks • High deficient lane-miles • Older pavements • Room for GHG reduction! Moving tire (top view) is on slope = Deflection induced eXtra-Fuel Consumption

  38. CARBON MANAGEMENT = Cost – Benefit! ECONOMICS = LINGUA FRANCA OF IMPLEMENTATION • LCCA is tool for supporting design decisions • Analyses typically occur after design process is complete • Standard practice does not account for uncertainty • FHWA does not provide guidance on characterizing inputs and uncertainty • ECONOMICS • 100%

  39. LCCA VALUE PROPOSITION • Context: $ 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. • ECONOMICS • Decision Makers (local, national, and beyond) * Swei, Gregory & Kirchain (2013) IMPLEMENTATION@ State Level: Case Study

  40. Uncertainty is pervasive in pavement LCCA Decisions long before construction Uncertainty & Risk Long life-cycle Uncertainty in unit construction costs Uncertainty in material price evolution Cash Flow Construction Operation CSHub approach characterizes uncertainty for all three areas Uncertainty in timing of M&R activities

  41. CSHub LCCA methodology is integrated with pavement design process Relative risk MEPDG Output Present Is the difference significant? Future LCCA Model Characterize drivers of uncertainty FHWA guidance is limited

  42. IMPLEMENTATION: LCCA – Why does it matter? Translating price volatility into value proposition for Decision Makers • ECONOMICS = LINGUA FRANCA OF IMPLEMENTATION • ECONOMICS • 100% Minimizing Risk Gambling with Cost overrun

  43. What’s next? Analysis: • LCCA & PVI • Pavement maintenance and PVI • Impacts from pavement age Data needs: • Longer timeframe (7 years doesn’t cover full pavement lifecycle) • Pavement maintenances and activity • More PCC data (i.e. I-295) Implementation: • Let’s see where this can take us … TOGETHER !

  44. We seek your input!Thank you. • References: • Louhghalam, A.; Akbarian, M., Ulm, F-J. (2013) Fluegge'sConjecture: 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/

  45. Predicting the future? • Beyond my pay grade, but… • CARBON MANAGEMENT is a vehicle of INFRASTUCTURE MANAGEMENT • Quantitative Sustainability • Together, let’s make it a reality…

  46. : Main distresses of PCC pavements Pavement IRI is a function of pavement maintenance

  47. Comparison: Gen 1 – Gen 2 Model GPS-2: AC on Treated Base GPS-1: AC on Granular Base Vehicle speed tons (on 3 axles) m (lane width) (GPS 1, 2 - LTPP Network) s Temperature That is, Gen I model is a lower bound. Gen II is more accurate for local response, but requires (at least) one more parameter. Gen 1 INPUT Gen 2 INPUT

  48. Viscoelastic Modeling | Master Curve Temperature Simplified 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) Load Frequency (Speed)

  49. Principle of Viscoelastic Model Fitting (Using Master Curve) Simplified Maxwell model along with the WLF law accounts for the temperature dependency. complicated viscoelastic model Simplified (Maxwell) viscoelastic model Fit for the entire frequency range Fit for applicable frequency range Find t and E Frequency range of interest Maxwell model with temperature dependency

More Related