Use of LTPP Data for Implementation

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• Use of LTPP Data for Implementation
• of the M-E Design Guide

Prepared by Michael I. Darter Jag
Mallela Applied Research Associates, Inc.
February 8, 2005
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Use of Case Studies

• Analysis of Case Studies are very effective to
  help highway agencies implement the Design Guide.
• Shows that its not difficult to use the software.
• Provides experience in obtaining inputs, running
  the program, and reviewing outputs.
• Provides assessment of the ability of procedure
  to predict key distresses and smoothness.
• Helps establish databases (materials, traffic,
  others).

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Use of LTPP Data in State Implementation
Develop input data libraries Traffic Material
Climate Structure/Perf.
Evaluate the M-E PDG in State Case studies
Local calibration necessary?

• LTPP Database

Yes
Local calibration of models Distresses,
Smoothness, Reliability
No
Implement the results Manuals
Libraries Software
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Summary of LTPP Data Sources
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Summary of LTPP Data Sources
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Summary of LTPP Data Sources
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Example of Utah LTPP Sections for M-E PDG
Implementation Efforts
Case 1 49-3011, JPCP
Case 2 49-1008, HMA
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Case study 1 Performance prediction example of
an LTPP 49-3011 JPCP

• General information
• LTPP GPS-3, JPCP
• On a 4-lane divided Interstate 15
• Nephi, Utah
• Constructed in April opened in July 1986

• Info from
• LTPP database

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Case study 1 Level 1 Traffic inputs, with WIM
data for 2 years, each quarter
Volume Inputs

• Info from
• LTPP database

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Case study 1 Level 1 Traffic inputs, with WIM
data for 2 years, each quarter

• Truck volume info
• Base year 2-way AADTT 320
• Growth rate 12 percent (compound)
• Directional distribution 50 percent
• Lane distribution 100 percent

Truck growth

• Data from LTPP database

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Case study 1 Level 1 Traffic inputs, with WIM
data for 2 years, each quarter
Vehicle class distribution
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Traffic inputs, Site-specific load
spectra---Single
Single axle

• Processed using Utah WIM Data

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Traffic inputs, Site-specific load
spectra---Tandem
Tandem axle

• Processed using Utah WIM Data

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Case study 1 Level 1 Traffic inputs, Continued
Hourly distribution
Axle Configuration
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Traffic inputs, Continued
Axle per vehicle

• Processed using Utah WIM Data

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Case study 1 Climatic Data Input
Climatic Inputs
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Case study 1 JPCP Design Features

• Design information
• Permanent curl/warp -13 oF
• Random joint spacing 12-13-17-18 ft
• Tied PCC shoulder
• (long term LTE 40 )
• LCB base bonded 60 months
• Base erodibility 2 (very high strength lean
  concrete)

• Data from LTPP database

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Case study 1 Pavement Layers
Pavement Structure LTPP TST_L05B table
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Case study 1 PCC Surface
PCC general thermal materials Properties
LTPP TST_ tables

• PCC thermal properties
• CTE 7.810-6 /oF (extremely high value)

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Case study 1 PCC Surface
PCC mixture LTPP INV_ tables

• PCC material information
• Cement content 564 Ib/yd3
• Water/cement ratio 0.443
• Coarse aggregate type quartzite

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Case study 1 PCC Surface
PCC strength modulus

• Data partially from LTPP database

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PCC Flexural Strength
1.
2.
3.
MR ST/0.67 ST tensile strength
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PCC Elastic Modulus
Adjusted to 28-day strength
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Case study 1 Lean Concrete Base
LCB properties LTPP TST_ tables
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Case study 1 Subgrade
Subgrade properties LTPP TST_ tables
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Selection of Subgrade Mr

• Design Guide requires lab tested Mr at optimum
  moisture (level 2), or default Mr based on AASHTO
  soil class (level 3)
• Alternative level 2 method for existing pvt
• Backcalculate k-value using FWD deflection data
  (approach used in calibration)
• Select corresponding Mr to match backcalculated
  k-value

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Backcalculated Subgrade k-value
Selected Mr to provide average backcalculated k
value of 238 psi/in Input Mr 15,000 psi (this
would be at optimum moisture)
Mean k-value 238 psi/in
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Case study 1 M-E PDG Analysis Screen
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Case study 1 Analysis results Transverse slab
cracking
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Case study 1 Analysis results Computed
transverse joint LTE
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Case study 1 Analysis results Joint faulting
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Backcasting Initial IRI
Initial IRI 77 in/mile
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Case study 1 Analysis results SmoothnessIRI
Initial IRI 77 in/mile
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Case Study 2 LTPP 49-1008

• General information
• LTPP GPS-1, Conventional HMA
• On 4-lane divided US-89
• County Sevier, Utah
• Constructed in August 1976

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Case study 2 Pavement Structure
Existing HMAC
9.1 in
4.7-in
Soil/Aggregate (A-1-b)
Semi-infinite
Subgrade (A-4/A-6)
HMA Pavement Structure LTPP TST_L05B Table
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Case study 2 HMAC Properties

• HMA Properties
• Thickness 9.1 in
• AC grade AC-10
• Volumetric binder content 11
• Percent air voids 8.5 percent

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Case study 2 HMAC Properties
Default Creep Compliance

• HMA Properties
• Percent retained on ¾-in sieve 0
• Percent retained on ?-in sieve 12
• Percent retained on No. 4 sieve 37.5
• Percent passing No. 200 9.9

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Case study 2 Unbound Base Properties

• Aggregate Base Properties
• Thickness 4.7 in
• AASHTO class A-1-a
• Plasticity index NP (1)
• Pct passing 4 sieve 56.5
• Pct passing 200 sieve 12.2
• D60 12.4-mm

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Case study 2 Subgrade Properties

• Subgrade Properties
• AASHTO class A-4/A-6
• Plasticity index 10.0
• Pct passing 4 sieve 91.5
• Pct passing 200 sieve 61.1
• D60 0.075-mm

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Selection of Subgrade Mr

• Design Guide requires lab tested Mr at optimum
  moisture (level 2) or default Mr based on AASHTO
  soil class (level 3)
• Alternative level 2 method (existing pvt)
• Backcalculate subgrade in-situ elastic modulus
  using FWD deflection data
• Convert to in situ lab Mr
• Adjust to optimum moisture content Mr

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Case study 2 Subgrade Insitu Mr (July ER0.35)
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Case study 2 Simulation of Performance with the
M-E PDG

• Longitudinal cracking
• Fatigue (alligator) cracking
• Rutting
• Smoothness (IRI)

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Case study 2 Longitudinal Cracking
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Case study 2 Fatigue Cracking
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Case study 2 Rutting
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Case study 2 Initial IRI
Initial IRI 52 in/mile
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Case study 2 Smoothness (IRI)
Initial IRI 52 in/mile
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Better Design through the M-E PDG
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Comparison of Predicted Measured Rutting
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Limited Calibration
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Summary

• Use of LTPP sections in State and surrounding
  region can be a major help in implementation of
  the new M-E PDG (at all levels testing, design,
  mgt.)
• Data libraries can be established for State (or
  region) traffic, HMA, PCC, soils, aggregates,
  design features, key distress types, IRI, etc.

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Summary
3. Run case studies of each LTPP section. a) to
gain experience with software and with inputs.
b) to observe prediction capability over
prediction, under prediction, wide scatter,
etc.?

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Summary
4. Use results to perform limited calibration to
determine if various defaults could be adjusted
to better predict distress and IRI unbiasedly.