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

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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