Quantifying life cycle cost and environmental impact of pavements
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: Fuel saving Cost saving GHG reduction Strategy for reducing air pollution! non profit support group for the Route 29 Bypass Slide 2 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: Carbon Management: how to move forward US Network VA Network Slide 3 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 Slide 4 Key Drivers of Rolling Resistance Pavement Texture: Tire industry. Critical for Safety. TirePavement 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 *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 5 ROUGHNESS / IRI: Dissipated Energy VEHICLESPECIFIC ENERGY DISSIPATION & EXCESS FUEL CONSUMPTION Quarter-Car Model* Mechanistic/PSD**: with: IRI HDM-4 Model***: () Vehicle Specific IRI measured at c=80 km/h = 50 mph = Damping of Suspension System (Vehicle Specific) (*) Sayers et al. (1986). World Bank Technical paper 46 Reference IRI-Value
(**) Sun et al. (2001). J. Transp. Engrg., 127(2), 105-111. (***) Zaabar I., Chatti K. (2010) TRB, No. 2155, 105-116. Slide 6 ROUGHNESS: HDM-4 MODEL =% 0 IRI 0 Input: Zaaber & Chatti (2010) 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. Slide 7 MIT Model Gen II: Viscoelastic Top Layer Consideration of Top-Layer Viscoelastic behavior, including temperature shift factor: P c Relaxation Time s Bituminous Materials* E s k Temperature dependence Cementitious Materials**: Winkler Length h = = = tE = (/)1/ 2 * Pouget et al. (2012); William, Landel, Ferry (1980)
** Bazant (1995) Speed Dependence Slide 8 Calibration/Validation | Asphalt Lit. Data 1.4 c= 100 km/h Calibration c=100 km/h DISSIPATED ENERGY [MJ/km] 1.2 Model-Based Simulations 1 0.8 0.6 Pouget et al. (2012) MIT Model 0.4 0.2 0 0 10 20
30 40 50 60 70 TEMPERATURE [Deg.C] 1.6 DISSIPATED ENERGY [MJ/km] c= 50 km/h Validation c=50 km/h 1.4 1.2 1 2 ( ) = ; =
2 ( ( )= 0 ( 0 ) ( ) 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 0.8 Pouget et al. (2012) MIT Model 0.6 0.4 0.2 0 0 10 20 30 40 TEMPERATURE [Deg.C]
50 60 ) 70 Slide 9 New Feature: Temperature and Speed Dependence 0.35 DISSIPATED ENERGY [MJ/km] 0.3 0.25 0.2 0.15 68 Deg. F Gen I 0.1 50 Deg. F 0.05 0 0
20 40 60 80 100 120 SPEED [km/h] (Example taken from Pouget et al. (2012) Slide 10 Can we do better? Yes, we can! 2011 MIT-Model PVI Impact MEPDG Structure and Material Slide 11 LCA plus: MOVING LCA IN THE DESIGN SPACE INPUT: - Structure - Materials - Traffic - Climate - Design Criteria
Slide 13 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 AC PCC Com GPS1: AC on Granular Base GPS3: Jointed Plain CP (JPCP) GPS6: AC Overlay of AC Pavement GPS2: AC on Bound Base GPS4: Jointed Reinforced CP (JRCP) GPS7: AC Overlay of PCC
GPS5: Continuously Reinf. CP (CRCP) GPS9: PCC Overlay of PCC Slide 14 VA Interstate: Road Classification BIT BOC BOJ CRCP JRCP VA Label Type LTPP Equivalent BIT JRCP CRCP BOJ BOC Bituminous Jointed reinforced CP Continuously reinforced CP Bituminous over JPCP Bituminous over CRCP GPS 1,2
2,050 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 Slide 16 Annual Average Daily Truck Traffic (AADTT) AADTT Slide 17
Deflection -Induced PVI Slide 18 Temperature and Speed Sensitivity: AC in VA ( ) = = ; = 2 2 ( ) Asphalt Concrete (BIT) Asphalt Concrete (BIT) 12 12 T=10C/50F T=20C/65F
Dissipated Energy [MJ/km] 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 10 Dissipated Energy [MJ/km] Speed Sensitivity half order of magnitude higher dissipation ( vs. 60 mph) tons (3 axles); ; s Slide 19 Temperature Sensitivity: PCC in VA ( ) = = ; = 2
c=20 mph c=60 mph 0.6 1 0.4 0.5 0.2 0 0 0.01 0.1 0 0 Dissipated Energy [MJ/km] 0.01 0.1 Dissipated Energy [MJ/km] 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 Slide 20 1 Would this matter for VA? Order of magnitude difference BIT/AC PCC Temperature sensitivity 10 Deg. can entail one order of magnitude of higher energy dissipation; thus fuel consumption. Temperature sensitivity 10 Deg. can entail half order of magnitude of higher energy dissipation; thus fuel consumption.
Assume: Bit @ 95%. P=37 tons (3 axles); 0=0.015s Assume: PCC @ 95%. P=37 tons (3 axles); 0=0.015s * Temp data from National Oceanic and Atmospheric Administration (esrl.noaa.gov) Slide 21 VA Network: PVI Deflection Truck c= 100 km/h=62.6 mph; T= 16 C/61 F 1.6 Bituminous PDF/1 1.4 1.2 1 0.8 0.6 0.4 0.2 0 0 0 0.01 Excess Fuel Consumption (gal/mile)
Excess fuel consumption due to PVI deflection is 10 times higher on bituminous pavements Slide 22 Annual Excess Fuel Consumption: PVI Deflection *2013 data c= 100 km/h=62.6 mph; T= 16 C/61 F FC (gallon/mile) Slide 23 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 Slide 24 IRI-Induced PVI Slide 25 Frequency
IRI: US Network VA Data Comparison 0.6 0.5 0.4 0.3 VA Network US Network 0.2 0.1 0 <60 60-94 95-119 120-144 145-170 171-194 195-220 > 220 IRI (in/mile) <60 1.2
60-94 95-119 120-144 145-170 171-194 195-220 > 220 1 0.8 0.6 VA Network US Network 0.4 0.2 0 IRI distribution of Virginia and the US network are very similar. Slide 26 VA Roughness Frequency *2013 data
0.7 0.6 0.5 0.4 VA Concrete VA Asphalt VA Composite 0.3 0.2 0.1 0 <60 60-94 95-119 120-144 145-170 171-194 195-220 > 220 IRI (in/mile) <60 1.2 60-94
95-119 120-144 145-170 171-194 195-220 > 220 1 0.8 0.6 0.4 Concrete Asphalt Composite 0.2 0 Asphalt and composite pavements are maintained equally. Not concrete Slide 27 IRI depends on pavement maintenance <60 1.2 60-94
IRI (in/mile) Slide 29 Excess Fuel Consumption: PVI Roughness *2013 data FC (gallon/mile) Slide 30 P a v e m e n t E x p e n d i t u r e ( M i l l io n s o f $ ) Annual Expenditure on all Pavements in VA Cost aggregated for: - Interstate pavement - Primary pavement - Secondary pavement $400 Deficient pavement IRI: - Poor: 140-199 - Very poor: >200 Concrete Pavement Asphalt Pavement $350 $300 $250 $200
$150 $100 $50 $0 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Year Deficient lane miles due to ride quality by pavement type Interstate Pavement Type AC PCC Total lane-mile (% total) 3,131 (65%) 490 (10%) 3,621 (75%) Deficient lane-miles (% total)* 157 (46%) 181 (54%) 338 (100%) *VDOT. State of The Pavement 2012. http://www.virginiadot.org/info/resources/State_of_the_Pavement_2012.pdf Slide 31 SUMMARY: IRI-induced PVI IRI is vehicle specific Concrete pavements are under-maintained Difference between pavement systems is IRIdevelopment and pavement aging. Data not consistent with national analyses Model Development:
=% 0 IRI 0 Reference in/mile = Political decision Higher value of reduces the number of roads contributing to excess fuel consumption. Slide 32 Total PVI Impact Slide 33 A n n u a l E x c e s s C O 2 e E m is s io n s (t o n s / m A n n u a l E x c e s s F u e l C o n s u m p ti o n ( G a l / Network: Annual PVI Truck* excess FC per mile c= 100 km/h=62.6 mph; T= 16 C/61 F *2013 data 16000 Deflection a 14000 Roughness 160 140
20,000 1,000,000 10,000 0 0 1 2008 2009 Annual Truck FC Roughness 2010 2011 2012 Annual Truck FC Deflection 2013 a Slide 35 E x c e s s C O 2 e E m is s i o n s ( t o n s )
E x c e s s F u e l C o n s u m p ti o n (G a ll o n s ) Network: Annual PVI Truck Total FC PVI Total Impact: Roughness and Deflection *2013 data: Trucks c= 100 km/h=62.6 mph; T= 16 C/61 F FC (gallon/mile) Slide 36 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 Slide 37
CARBON MANAGEMENT = Cost Benefit! ECONOMICS 100% 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 Slide 38 LC C A VA LU E P RO P O S I T I O N Context: $ 2 Trillion Infra-structure renewal job within tightest budgetary constraints. Problem: Volatility of construction materials pricing for a fiscally sound decision making. ECONOMICS Decision Makers (local, national, and beyond) 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.
* Swei, Gregory & Kirchain (2013) I M P L E M E N TAT I O N @ State Level: Case Study I N V E S T I N N O VAT E I N V I G O R AT E - I M P L E M E N T Slide 39 Uncertainty is pervasive in pavement LCCA Cash Flow Decisions long before construction Uncertainty in unit construction costs Construction CSHub approach characterizes uncertainty for all three areas Uncertainty & Risk Long life-cycle Uncertainty in material price evolution
O p e ra t i o n Uncertainty in timing of M&R activities Slide 40 CSHub LCCA methodology is integrated with pavement design process Present Relative risk MEPDG Output Is the difference significant? Future LCCA Model Characterize drivers o uncertainty FHWA guidance is limited Slide 41 IMPLEMENTATION: LCCA Why does it matter? Translating price volatility into value proposition for Decision Makers
C u m u la tiv e P ro b a b ility ECONOMICS = LINGUA FRANCA OF IMPLEMENTATION ECONOMICS 100% Minimizing Risk 100% 90% 80% 70% 60% Gambling with Cost overrun 50% 40% Des ign A 30% 20% 10% 0% 26.8 26.9 27.0 27.1 27.2 27.3 27.4 27.5 27.6 27.7 27.8 NPV (Millions of $'s) Slide 42
Whats next? Analysis: LCCA & PVI Pavement maintenance and PVI Impacts from pavement age Data needs: Longer timeframe (7 years doesnt cover full pavement lifecycle) Pavement maintenances and activity More PCC data (i.e. I-295) Implementation: Lets see where this can take us TOGETHER ! Slide 43 We seek your input! Thank you. References: Louhghalam, A.; Akbarian, M., Ulm, F-J. (2013) Fluegge's Conjecture: Dissipation vs. Deflection Induced PavementVehicle-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 44 Predicting the future? Beyond my pay grade, but CARBON MANAGEMENT is a vehicle of INFRASTUCTURE MANAGEMENT
Quantitative Sustainability Together, lets make it a reality Slide 45 : Main distresses of PCC pavements Transverse Cracking Interstate D4 D5 D9 11% 10% 0% Corner Breaks 1% 1% 2% PCC Patching 8% 2% 2% Asphalt Patching
13% 12% 1% Average Pavement Roughness (in/mile) Poor 140-199 JRCP IRI 146 AC IRI 87 128 73 104 88 JPCP Distresses (%slabs) Pavement IRI is a function of pavement maintenance Slide 46 Comparison: Gen 1 Gen 2 Model GPS-2: AC on Treated Base GPS-1: AC on Granular Base 0.5 0.5
0 0 0.01 0.1 1 10 Vehicle speed tons (on 3 axles) m (lane width) (GPS 1, 2 - LTPP Network) s Temperature Gen 2 INPUT Gen 1 INPUT DISSIPATED ENERGY [Ltr/100km] That is, Gen I model is a lower bound. Gen II is more accurate for local response, but requires (at least) one more parameter. Slide 47 Viscoelastic Modeling | Master Curve
Temperature =exp ( 1 ( ) 2 +( ) ) 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) Slide 48 Principle of Viscoelastic Model Fitting (Using Master Curve) 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 Simplified Maxwell model along with the WLF law accounts for the temperature dependency. Maxwell model with temperature dependency Slide 49
"Frogs and snails and puppy dog tails, that's what boys are made of!" ... Teachers are fragile beings, and are here to help us. We often take this for granted. A mutual relationship of respect needs to be present for...
Least-Squares Regression Method Least-Squares Regression Method Cost Estimation Methods Regression Analysis Cost Estimation Methods Regression Analysis Cost Estimation Methods Regression Analysis Simple Regression Analysis Example Simple Regression Analysis Example The Contribution Format The Contribution Format End of Session 3 Let's...
Negative behaviours decreased from time 1 to time 3. Negative Verbal responses saw marginal change, significant change for negative Non-Verbal behaviours. Practicing interviews also helps to decrease negatives as some improvement from time 1 to time 2. Positive behaviours increased.
Unit A - Human Activity and Biodiversity. 4.1 Reduction of Biological Diversity. Outcomes. ... Example: Swift Fox used to be common in Canada but by 1928 it was extirpated from Canada. ... can help endangered species.
reportedly have TIVO or some other recording device and (64%) of these households . reportedly skip past TV commercials every time or often. These figures were 49% and 66% in 2009. Younger folks are more likely to have a TIVO-like...
Goal of queuing analysis is to minimize the sum of two costs Customer waiting costs Service capacity costs Waiting lines are non-value added occurrences Implications of Waiting Lines Cost to provide waiting space Loss of business Customers leaving Customers refusing...
OFERTA DE POSGRADO ESTADOS UNIDOS ECOLE NATIONALE SUPÉRIEURE DES ARTS ET INDUSTRIES TEXTILES www.ensait.fr AREAS: Todas las Científicas y Tecnológicas Contacto: Mauricio Camargo ENSAIT 9 Rue de L´ Ermitage BP 30329 F-59056 Roubaix Cedex 01 FR.
Full equitable title is assumed, giving farmer essentially most ownership rights. Owner-seller has more assurance. FSA now guarantees land contracts. ... A share lease is a farm rental arrangement where the landowner and farm-tenant share costs of production and revenue...
Ready to download the document? Go ahead and hit continue!