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INDOT Implementation for theM-E Pavement Design Guide
INDOT Implementation for theM-E Pavement Design Guide
Tommy NantungKhaled Galal
Scott NewboldsShuo Li
DaeHyeon KimGhassan Chehab
Kumar Dave – INDOT Materials and Tests DivisionBill Flora – INDOT Program Development
Lee Gallivan – FHWA Indiana Division
Tommy NantungKhaled Galal
Scott NewboldsShuo Li
DaeHyeon KimGhassan Chehab
Kumar Dave – INDOT Materials and Tests DivisionBill Flora – INDOT Program Development
Lee Gallivan – FHWA Indiana Division
Research Pays Off
Environmental Module (EICM)Environmental Module (EICM)
• Inputs: – Weather station or location– Other inputs: e.g., heat capacity, thermal conductivity,
depth of water table• Output:
– Hourly temperature profile throughout the pavement– Correction factors based on moisture profile and
freeze-thaw history to adjust the optimum modulus of unbound layers
• Inputs: – Weather station or location– Other inputs: e.g., heat capacity, thermal conductivity,
depth of water table• Output:
– Hourly temperature profile throughout the pavement– Correction factors based on moisture profile and
freeze-thaw history to adjust the optimum modulus of unbound layers
Research Pays Off
Environmental Module (EICM)Environmental Module (EICM)
• Implementation Initiatives– Climatic data:
• Import data from a nearby station– Regionalization of areas
• Generate climatic data from a specific weather station– Most likely from INDOT weather stations nearby and/or the future
Indiana Mini-LTPP
• Interpolate data from stations in the M-E Design Guide database
• Implementation Initiatives– Climatic data:
• Import data from a nearby station– Regionalization of areas
• Generate climatic data from a specific weather station– Most likely from INDOT weather stations nearby and/or the future
Indiana Mini-LTPP
• Interpolate data from stations in the M-E Design Guide database
Research Pays Off
Environmental Module (EICM)Environmental Module (EICM)
• Implementation Initiatives– Project with high economic consequences
• Generate a climatic data from a specific weather station• Import data from nearby stations
– Project with medium and low economic consequences• Import data from nearby stations• Interpolations from M-E Design Guide database
• Implementation Initiatives– Project with high economic consequences
• Generate a climatic data from a specific weather station• Import data from nearby stations
– Project with medium and low economic consequences• Import data from nearby stations• Interpolations from M-E Design Guide database
Research Pays Off
Traffic Input DataTraffic Input Data
• Required input data– Basic inputs such as
AADTT, % truck, and Operation speed
– Traffic volume adjustment factors such asMonthly adjustment, class distribution, and hourly distribution
– Other general traffic inputs such asAxle numbers for single, tandem, tridem, and quad axle groups Axle load distribution, andAxle configuration
• Required input data– Basic inputs such as
AADTT, % truck, and Operation speed
– Traffic volume adjustment factors such asMonthly adjustment, class distribution, and hourly distribution
– Other general traffic inputs such asAxle numbers for single, tandem, tridem, and quad axle groups Axle load distribution, andAxle configuration
Research Pays Off
Traffic Input DataTraffic Input Data
• Input levels– Level 3: Use of regional or default classification and axle load spectra in the
M-E Design Guide– Level 2: Use of regional axle load spectra data and project-related volume or
classification data– Level 1: Use of volume or classification and axle load spectra data directly
related to the project (site specific)
• Input levels– Level 3: Use of regional or default classification and axle load spectra in the
M-E Design Guide– Level 2: Use of regional axle load spectra data and project-related volume or
classification data– Level 1: Use of volume or classification and axle load spectra data directly
related to the project (site specific)
Research Pays Off
Traffic – Region/Specific Input DataTraffic – Region/Specific Input Data
• Implementation Initiatives– Incorporate GIS to display
tabular data spatially,– Incorporate GPS
coordinates for precise locations of WIM sites
• Implementation Initiatives– Incorporate GIS to display
tabular data spatially,– Incorporate GPS
coordinates for precise locations of WIM sites
– WIM/AVC data analysisHuge data availableStandard reports created by WIM vendor software not enough for M-E Design GuideDevelop computer program to process IRD Raw Data File
– WIM/AVC data analysisHuge data availableStandard reports created by WIM vendor software not enough for M-E Design GuideDevelop computer program to process IRD Raw Data File
Research Pays Off
INDOT Readiness – Traffic DataINDOT Readiness – Traffic Data
Data Equipment Knowledge
Level 1 Some sites Some sites
Level 2 On-going study Some regions
Level 3a On-going study Some regions
Level 3bOK
Coverage countsOK
Coverage counts
Research Pays Off
Materials Input DataMaterials Input Data
• Definitions:– Level 3
• Provide the lowest level of accuracy• Use where there are minimal consequences of early failure (lower volume
road)
– Level 2• Provide an intermediate level of design• Closest to the nowadays procedures
– Level 1• Provide the highest level of accuracy• Use for designing heavily trafficked pavements or • Concern with safety or economic consequences of early failure
• Definitions:– Level 3
• Provide the lowest level of accuracy• Use where there are minimal consequences of early failure (lower volume
road)
– Level 2• Provide an intermediate level of design• Closest to the nowadays procedures
– Level 1• Provide the highest level of accuracy• Use for designing heavily trafficked pavements or • Concern with safety or economic consequences of early failure
Research Pays Off
Asphalt Materials Inputs (Summary)Asphalt Materials Inputs (Summary)
• Level 3– Simply: accept default values based on nationally
calibrated models as was presented earlier on the day• Level 2
– Simply: input all materials related properties that you have either from lab tests, field cores, or in-situ testing
• Level 1– Simply: materials properties are determined from
extensive lab tests and in-situ testing.
• Level 3– Simply: accept default values based on nationally
calibrated models as was presented earlier on the day• Level 2
– Simply: input all materials related properties that you have either from lab tests, field cores, or in-situ testing
• Level 1– Simply: materials properties are determined from
extensive lab tests and in-situ testing.
Research Pays Off
Asphalt Materials InputsAsphalt Materials Inputs
• Asphalt binder related– PG grades, G* and δ– effective binder content
• Asphalt mixtures related – Gradation, E* (Dynamic Modulus)– Volumetric Properties– Creep Compliance TABLES
• Asphalt binder related– PG grades, G* and δ– effective binder content
• Asphalt mixtures related – Gradation, E* (Dynamic Modulus)– Volumetric Properties– Creep Compliance TABLES
Research Pays Off
Asphalt Binders InputsAsphalt Binders Inputs
• Level 3:– AC Viscosity grade (AC-5, AC-10, etc.)– AC Penetration Grade (Pen 40-50, Pen 60-70 etc.)– PG Grade (PG58-34, PG64-22, etc.)
• Level 2:– Binder data for AC Viscosity grade (softening point, absolute
viscosity, kinematics viscosity, Specific gravity, penetration, brook field viscosity)
– Binder data for PG grades (G*, δ - RTFO data at different temp.)• Level 1:
– Binder data for AC Viscosity grade (same as level 2)– Binder data for PG grades (same as level 2)
• Level 3:– AC Viscosity grade (AC-5, AC-10, etc.)– AC Penetration Grade (Pen 40-50, Pen 60-70 etc.)– PG Grade (PG58-34, PG64-22, etc.)
• Level 2:– Binder data for AC Viscosity grade (softening point, absolute
viscosity, kinematics viscosity, Specific gravity, penetration, brook field viscosity)
– Binder data for PG grades (G*, δ - RTFO data at different temp.)• Level 1:
– Binder data for AC Viscosity grade (same as level 2)– Binder data for PG grades (same as level 2)
Research Pays Off
Asphalt Binders InputsAsphalt Binders Inputs
Level 3
Asphalt Binder Input
Level 1 or Level 2
Asphalt Binder Input
Research Pays Off
Asphalt Mixtures InputAsphalt Mixtures Input
• Level 3:– Cumulative % Retained on 3/4” sieve– Cumulative % Retained on 3/8” sieve– Cumulative % Retained on #4 sieve– Passing # 200 sieve %
• Level 2:– Same as level 3.
• Level 1:– Asphalt Dynamic Modulus tests (typically 5 different
temperatures and 5 different frequencies)– Creep Compliance tests
• Level 3:– Cumulative % Retained on 3/4” sieve– Cumulative % Retained on 3/8” sieve– Cumulative % Retained on #4 sieve– Passing # 200 sieve %
• Level 2:– Same as level 3.
• Level 1:– Asphalt Dynamic Modulus tests (typically 5 different
temperatures and 5 different frequencies)– Creep Compliance tests
Research Pays Off
Asphalt Mixtures InputAsphalt Mixtures Input
Level 2 or 3Mix Input
Level 1Mix Input
Research Pays Off
Asphalt General InputAsphalt General Input
Level 1, 2 or 3
Mix Volumetric Inputs
Research Pays Off
Asphalt Thermal Cracking InputAsphalt Thermal Cracking Input
Mix Creep Inputs
• Level 1– Testing
• Level 2– Testing or Default
• Level 3– Default values
• Level 1– Testing
• Level 2– Testing or Default
• Level 3– Default values
Research Pays Off
INDOT Readiness – AsphaltNew PavementINDOT Readiness – AsphaltNew Pavement
DataBinder - Mix
Equipment Knowledge
Level 1 Research Div
Level 2 Research Div
Level 3 N/A
Research Pays Off
INDOT Readiness – AsphaltPavement RehabilitationINDOT Readiness – AsphaltPavement Rehabilitation
DataBinder - Mix
Equipment Knowledge
Level 1 Research Div
Level 2 Research Div
Level 3 N/A
Research Pays Off
Asphalt MaterialsAsphalt Materials
• Implementation Initiatives– Built Database for Dynamic Modulus and Creep
Compliance. – Run design of existing roads (For example: LTTP sites,
test roads, etc.)– Compare M-E Design Guide outputs to distress data
collected on those roadways.– Calibrate models to produce the observed distress data.– Validate calibrated models using INDOT APT
• Implementation Initiatives– Built Database for Dynamic Modulus and Creep
Compliance. – Run design of existing roads (For example: LTTP sites,
test roads, etc.)– Compare M-E Design Guide outputs to distress data
collected on those roadways.– Calibrate models to produce the observed distress data.– Validate calibrated models using INDOT APT
Research Pays Off
Input Data for ConcreteInput Data for Concrete
• Inputs: – Level 3: 28-day MR or 28-day fc, curing
method, and cement type, and predictive equations
– Level 2: 7, 14, 28, 90, and 20-yr/28-day ratio for fc, and predictive equations
– Level 1: 7, 14, 28, 90, and 20-yr/28-day ratio for MR, E, fc, ft
• Output:– MR, E, fc, and ft for each month in the design
period– Shrinkage
• Inputs: – Level 3: 28-day MR or 28-day fc, curing
method, and cement type, and predictive equations
– Level 2: 7, 14, 28, 90, and 20-yr/28-day ratio for fc, and predictive equations
– Level 1: 7, 14, 28, 90, and 20-yr/28-day ratio for MR, E, fc, ft
• Output:– MR, E, fc, and ft for each month in the design
period– Shrinkage
Research Pays Off
Level 3 ConcreteLevel 3 Concrete
• New Pavement– Typical value from INDOT (a single 28-day value) and
a standard strength gain curve• Pavement Rehabilitation (Intact Slabs)
– Same as new pavement• Pavement Rehabilitation (Fractured Slabs)
– Determine nominal fractured slab size– Get modulus of elasticity from 200X Guide
• New Pavement– Typical value from INDOT (a single 28-day value) and
a standard strength gain curve• Pavement Rehabilitation (Intact Slabs)
– Same as new pavement• Pavement Rehabilitation (Fractured Slabs)
– Determine nominal fractured slab size– Get modulus of elasticity from 200X Guide
Research Pays Off
Level 2 ConcreteLevel 2 Concrete
• New Pavement– Correlations between fc, Ec, and fr
– 14, 28, and 90-day values– Long Term Ratio for fc (20-years/28-days)
• Pavement Rehabilitation (Intact Slabs)– Same as new pavement
• Pavement Rehabilitation (Fractured Slabs)– Slab condition– Tabular Ec from 2002 Guides
• New Pavement– Correlations between fc, Ec, and fr
– 14, 28, and 90-day values– Long Term Ratio for fc (20-years/28-days)
• Pavement Rehabilitation (Intact Slabs)– Same as new pavement
• Pavement Rehabilitation (Fractured Slabs)– Slab condition– Tabular Ec from 2002 Guides
Research Pays Off
Level 1 ConcreteLevel 1 Concrete
• New Pavement– Lab values of fc, Ec, and fr at 7, 14, 28, and 90 days– Long Term Ratio (fc/Ec at 20 years divided by fc/Ec at 28 days).
• Pavement Rehabilitation (Intact Slabs)– FWD back-calculation (Ec) or– Coring and lab testing for fc, Ec, and fr values
• Pavement Rehabilitation (Fractured Slabs)– FWD back-calculation (Ec)
• New Pavement– Lab values of fc, Ec, and fr at 7, 14, 28, and 90 days– Long Term Ratio (fc/Ec at 20 years divided by fc/Ec at 28 days).
• Pavement Rehabilitation (Intact Slabs)– FWD back-calculation (Ec) or– Coring and lab testing for fc, Ec, and fr values
• Pavement Rehabilitation (Fractured Slabs)– FWD back-calculation (Ec)
Research Pays Off
Concrete MaterialsConcrete Materials
• Implementation Initiative– Sensitivity Analysis – Over 25 different concrete pavement
inputs– Various material inputs
• Strength, • Elastic modulus,• Thermal properties• Mixture parameters
– Design features• Joint spacing,• Slab thickness
– Effect on distresses over design life• IRI, Faulting, and Percent Slabs Cracked
– Over 100 trials have been run
• Implementation Initiative– Sensitivity Analysis – Over 25 different concrete pavement
inputs– Various material inputs
• Strength, • Elastic modulus,• Thermal properties• Mixture parameters
– Design features• Joint spacing,• Slab thickness
– Effect on distresses over design life• IRI, Faulting, and Percent Slabs Cracked
– Over 100 trials have been run
0
50
100
150
200
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300
350
400
400 600 800 1000 1200 1400
28d Flexural Strength
IRI (
in/m
i)
Level 1 Level 3 Level 1 Reliability Level 3 Reliability
0
50
100
150
200
250
300
350
400
400 600 800 1000 1200 1400
28d Flexural Strength
IRI (
in/m
i)
Level 1 Level 3 Level 1 Reliability Level 3 Reliability
0
50
100
150
200
250
300
350
400
0 2000 4000 6000 8000 10000
28d Compressive Strength
IRI (
in/m
i)Level 2 Level 3 Level 2 Reliability Level 3 Reliability
0
50
100
150
200
250
300
350
400
0 2000 4000 6000 8000 10000
28d Compressive Strength
IRI (
in/m
i)Level 2 Level 3 Level 2 Reliability Level 3 Reliability
Research Pays Off
INDOT Readiness – ConcreteNew PavementINDOT Readiness – ConcreteNew Pavement
Data Equipment Knowledge
Level 1 Some sitesOn-going study
Yes(Research & M-T)
Level 2 On-going study Yes(Research & M-T)
Level 3 On-going study Yes
Research Pays Off
INDOT Readiness – ConcretePavement RehabilitationINDOT Readiness – ConcretePavement Rehabilitation
Data Equipment Knowledge
Level 1 By-demand(FWD data)
Yes(Research Div.)
Level 2 By-demand(FWD data)
Yes(Research Div.)
Level 3 By-demand(FWD data)
Yes
Research Pays Off
Unbound Materials Input DataUnbound Materials Input Data
• Inputs: – Level 1: Properties with environmental (EICM) inputs– Level 2: Seasonal monthly inputs– Level 3: Average yearly inputs
• Output:– Monthly modulus for Subgrade and unbound
base/subbase
• Inputs: – Level 1: Properties with environmental (EICM) inputs– Level 2: Seasonal monthly inputs– Level 3: Average yearly inputs
• Output:– Monthly modulus for Subgrade and unbound
base/subbase
Research Pays Off
Level 3 and 2 Unbound MaterialsNew Pavement and Pavement RehabLevel 3 and 2 Unbound MaterialsNew Pavement and Pavement Rehab
• Level 3– Soil classifications correlations (AASHTO or USCS)– Typical Resilient Modulus range
• Level 2– Correlate Mr from empirical/lab – predictive equations– Mr from CBR, R-value, Plasticity Index, Gradation, or
Pavement Layer Coefficient– Mr from CBR and Dynamic Cone Penetration test
(pavement rehabilitation)
• Level 3– Soil classifications correlations (AASHTO or USCS)– Typical Resilient Modulus range
• Level 2– Correlate Mr from empirical/lab – predictive equations– Mr from CBR, R-value, Plasticity Index, Gradation, or
Pavement Layer Coefficient– Mr from CBR and Dynamic Cone Penetration test
(pavement rehabilitation)
Research Pays Off
Level 1 Unbound MaterialsBases/Sub-bases and SubgradeLevel 1 Unbound MaterialsBases/Sub-bases and Subgrade
• New Pavement– Use k1, -k3 non-linear coefficients for MR
• Direct lab test• Estimate ki values from standard material properties• Tabular ki values versus soil classification group
• Pavement Rehabilitation– Back-calculate MR from FWD tests
• Direct FWD measurement or Tabular summary of MR back-calculated values by soil group
• New Pavement– Use k1, -k3 non-linear coefficients for MR
• Direct lab test• Estimate ki values from standard material properties• Tabular ki values versus soil classification group
• Pavement Rehabilitation– Back-calculate MR from FWD tests
• Direct FWD measurement or Tabular summary of MR back-calculated values by soil group
Research Pays Off
Unbound MaterialUnbound Material
• Implementation Initiatives– Laboratory testing for monthly variations of Mr
– Laboratory simulation of the long-term Mr
– Correlation/Calibration between Lab and In-Situ FWD Tests
• Implementation Initiatives– Laboratory testing for monthly variations of Mr
– Laboratory simulation of the long-term Mr
– Correlation/Calibration between Lab and In-Situ FWD Tests
Research Pays Off
Example of predictive modelExample of predictive model
Estimation of k1, k2 and k3 for 11 soil variables (11 Fine-Grained Soils)
log k1 = 6.660876-0.22136×OMC-0.04437×MC-0.92743× MCR -0.06133× DD +10.64862× %comp +0.328465 × SATU -0.04434×%sand -0.04349 × %SILT -0.01832× %CLAY+0.027832× LL-0.01665× PI
k2 = 3.952635-0.33897×OMC+0.076116×MC-2.45921× MCR-0.06462× DD +6.012966× %comp +1.559769 × SATU +0.020286 ×%sand +0.002321 ×%SILT +0.011056× %CLAY+0.077436× LL-0.05367× PI
k3 = 2.634084+0.124471×OMC-0.09277×MC+0.366778× MCR --0.01168× DD -1.32637× %comp +1.297904 ×SATU -0.01226×%sand -0.00512 × %SILT -0.00492× %CLAY-0.05083× LL+0.018864× PI
Research Pays Off
INDOT Readiness – Unbound MaterialNew PavementINDOT Readiness – Unbound MaterialNew Pavement
Data Equipment Knowledge
Level 1 On-going study Yes(Research Div)
Level 2 On-going study Yes(Research Div)
Level 3 Default values Yes
Research Pays Off
INDOT Readiness – Unbound MaterialPavement RehabilitationINDOT Readiness – Unbound MaterialPavement Rehabilitation
Data Equipment Knowledge
Level 1 By-demand(FWD tests)
Yes(Research Div.)
Level 2 On-going study or use FWD tests
Yes(Research Div.)
Level 3 Default values Yes