AASHTOWare Pavement-ME Design Software: Materials Library by Murugaiyah Piratheepan Western Regional Superpave Center – WRSC University of Nevada Reno – UNR Nevada Transportation Conference April 10, 2013

Outline Background M-E design process Statistical evaluation of laboratory test data Calibration of performance models Distress data collection Acknowledgement

Background AASHTO 93 design guide 1950’s AASHTO road test 10-20 times less traffic One geographical location One type of subgrade and HMA Short duration of the test Refined in 1986 and 1993 Based on structural number (SN) Relates the thickness of surface layer to serviceability.

Background Need for transition Incorporate higher traffic level Climatic variations New materials (HMA, WMA, PM binder) and their properties into the design. Higher emphasis on performance Higher emphasis on rehabilitation Budgetary constraints and environmental concerns

Background M-E design- Determines pavement responses through mathematical models and relates them to observed performance. LTPP since 1987 Uses significant material properties Considers month by month aging of materials Can provide limitations for distresses Reduce Life Cycle costs

Why Pavement-ME for Nevada? Nevada use of Polymer modified asphalt binder National performance models are calibrated based on neat asphalt binder only Using the national models will show an early failure compared to the true performance of PM binders This may result in unneeded thicker pavement section Pavement-ME need to be locally calibrated to NV conditions

M-E design process Stage 1: Input Values Stage 2: Structural/Performance Analysis Stage 3: Engineering and Life Cycle Cost Analysis

M-E design process (Cont..) Stage 1: Input Values Level 1: Highest accuracy, requires laboratory or field testing and site specific data Level 2: Intermediate accuracy, user selected data from database or estimated correlations Including new materials such as WMA, RAP, and bio asphalt Level 3: Lowest accuracy, typical averages or default values

M-E design process (Cont..) Stage 1: Input Values Materials Traffic Climate 68 sections

Stage 1: Input Values-Materials M-E design process (Cont..) Stage 1: Input Values-Materials 17 binders (14 PG64-28 & 3 PG76-22) Dynamic Shear Rheometer (DSR) Complex Shear Modulus (G*) and phase angle (δ) Binder viscosity temperature relationship (A-VTS)

Stage 1: Input Values-Materials M-E design process (Cont..) Stage 1: Input Values-Materials 26 Contracts (16 PG64-28 & 10 PG76-22) for Dynamic Modulus Sinusoidal axial compressive stress Recoverable axial strain response Run at 4 temp (40,70,100,130°F) & 6 freq (25,10,5,1,0.5,0.1 Hz) Master Curve using time-temperature superposition Master curve coefficients Time Stress Strain time shift = /  = 0sin(ωt)  = 0sin(ωt-) 0 0

Stage 1: Input Values-Materials M-E design process (Cont..) Stage 1: Input Values-Materials 17 Contracts (13 PG64-28 & 4 PG76-22) for Repeated Load Triaxial (RLT) test – Permanent Deformation Model Pulse load (0.1 s loading and 0.6 s rest period) Deviatory stress of 45psi and confinement of 30psi Permanent & resilient strain with cyclic stress 3 different temperatures (104, 114.8, & 136.4°F)

Stage 1: Input Values-Materials M-E design process (Cont..) Stage 1: Input Values-Materials 8 Contracts (3 Pg64-28 & 5 PG76-22 )for beam fatigue test – Fatigue Performance Model Cyclic haversine load at 10Hz 50% reduction in initial stiffness 3 temperatures (55,70, & 85°F) 3 strain levels

Stage 2: Structural/Performance Analysis M-E design process (Cont..) Stage 2: Structural/Performance Analysis  

Stage 2: Structural/Performance Analysis M-E design process (Cont..) Stage 2: Structural/Performance Analysis  

Statistical evaluation of laboratory test data Groupings created to provide NDOT with appropriate Pavement ME design inputs Binder viscosity Dynamic modulus Rutting model coefficients Fatigue model coefficients

Statistical evaluation of laboratory test data Each measured parameter was checked for possible groupings based on: Nevada as a whole Districts Binder Grade (PG64-28 & PG76-22) Binder Source (7 total) Aggregate Source (17 total)

Statistical evaluation of laboratory test data Binder Viscosity Groupings District 3 PG76-22 binders Binder Sources Paramount and Valero Average A-VTS Average G* and δ for each assigned group Groupings A VTS PG76-22NV 7.2797 -2.3295 District 3 8.3611 -2.7339 Binder Source Valero 8.2024 -2.6731 Paramount 7.9968 -2.6005

Statistical evaluation of laboratory test data Dynamic Modulus Groupings Districts 2 & 3 binder sources Aggregate sources Average E* values provided for each group Paramount Grouping Temp. (°F) Mixture |E*|, ksi 25 Hz 10 Hz 5 Hz 1 Hz 0.5 Hz 0.1 Hz 40 1737.1 1558.4 1416.3 1077.5 934.7 632.1 70 705.5 549.4 446.4 261.0 203.4 111.6 100 176.5 125.3 96.5 53.4 42.0 25.4 130 50.0 36.7 29.4 18.8 15.9 11.6

Statistical evaluation of laboratory test data Rutting model Groupings District 3 Aggregate Source Binder Sources Average regression coefficients provided for each group Paramount Grouping K1 K2 K3 -4.9933 2.4577 0.3276  

Statistical evaluation of laboratory test data Fatigue model Groupings NV as a whole PG76-22 mixtures Average regression coefficients provided for each group PG76-22 Grouping K1 K2 K3 C 214.176 5.028 2.307 0.001  

Calibration of performance models Need to incorporate polymer modified binders Need to consider new materials that performs better to local environmental conditions Need to incorporate preservation and maintenance, construction for rehabilitation and new construction Local calibration allows for factors in that area to be factored into the pavement ME indirectly

Calibration of performance models Rutting model

Calibration of performance models

Distress data collection Challenges in collecting measured distresses Convert PMS data to pavement ME measuring units New versus Rehabilitated sections Properties for base, sub-baselayers

Calibration of performance models

Acknowledgement Tireless support from NDOT is gratefully acknowledged. Professors and graduate students in Pavements/Materials program at UNR are thanked for the accomplishment.

Thank you