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Publication Number: FHWA-HRT-12-030
Date: August 2012

 

Estimation of Key PCC, Base, Subbase, and Pavement Engineering Properties From Routine Tests and Physical Characteristics

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CHAPTER 2. MATERIAL PROPERTIES FOR PREDICTIVE EQUATIONS

The primary engineering material properties considered for indepth evaluation in this study were those required for pavement analysis and design using the MEPDG. The MEPDG considers the effects of a comprehensive set of material properties in the structural design of JPCP and CRCP. Its capability to consider strength, modulus, thermal, and other materials properties is the foundation for designing for performance under traffic loads and climatic conditions. Also, the MEPDG procedure can accommodate various material types and uniquely model each material’s response to load, temperature, and moisture and predict their effects on performance. Therefore, the research approach was to develop a list of material properties that likely could be estimated based on the availability of data in the LTPP database and the needs of the MEPDG performance prediction models.

 

INPUTS FOR MEPDG

Hierarchical Inputs for MEPDG

The MEPDG adopts a hierarchical input level scheme to accommodate the designer’s knowledge of the input parameter. Inputs can be provided at three different levels. Level 1 inputs represent the greatest knowledge about the input parameter and typically are obtained from a project-specific data collection or test effort. Level 2 represents a moderate level of knowledge of the input parameter and is often calculated from correlations with other site-specific data or a less expensive measure. Level 3 represents the least knowledge of the input parameter and is based on “best-estimated” or default values. For example, level 1 data for concrete flexural strength would involve a flexural beam test, level 2 would be a flexural strength value estimated using a compressive strength test and correlation to flexural strength, and level 3 would be a default value for concrete strength used by a particular SHA. Most agencies have adequate information in their materials and construction quality databases to develop agency-specific default values for immediate implementation of the MEPDG (based on current knowledge and surveys conducted by Rao et al.).(10)

During the development of the MEPDG, the need for correlation equations to determine some input values was recognized. Many designs must be created years in advance of construction, and little is known of the exact materials that will be used. However, it is highly desirable to have the best estimates of these inputs possible based on available information. The MEPDG therefore supports the use of level 2 or 3 data in the absence of level 1 laboratory test data. This adaptability is critical to the model types developed in this study and is further discussed in chapter 5 of this report.

 

Input Categories for the MEPDG

Table 1 provides a list of major material types considered in the MEPDG. Bolded information reflects the material types that are relevant in this project. Each material type requires a variety of material inputs (not all easily available) during local calibration efforts and after the procedure is implemented. As an example, the various PCC material-related inputs considered by the MEPDG are presented in table 2 under the following three categories:

 

Table 1. Major material types for the MEPDG.(2)

Asphalt Materials:

  • Stone matrix asphalt.
  • Hot mix asphalt (HMA).
    • Dense graded.
    • Open graded asphalt.
    • Asphalt stabilized base mixes.
    • Sand asphalt mixtures.
  • Cold mix asphalt.
  • Central plant processed.
    • In-place recycled.

PCC Materials:

  • Intact slabs—PCC.
    • High-strength mixes.
    • Lean concrete mixes.
  • Fractured slabs.
    • Crack/seat.
    • Break/seat.
    • Rubblized.

Chemically Stabilized Materials:

  • Cement stabilized aggregate.
  • Soil cement.
  • Lime cement fly ash.
  • Lime fly ash.
  • Lime stabilized soils.
  • Open graded cement stabilized aggregate.

Non-Stabilized Granular Base/Subbase:

  • Granular base/subbase.
  • Sandy subbase.
  • Cold recycled asphalt (used as aggregate).
    • Recycled asphalt pavement (includes millings).
    • Pulverized in-place.
  • Cold recycled asphalt pavement (HMA plus aggregate base/subbase).

Subgrade Soils:

  • Gravelly Soils (A-1 and A-2).
  • Sandy Soils.
    • Loose sands (A-3).
    • Dense sands (A-3).
    • Silty sands (A-2-4 and A-2-5).
    • Clayey sands (A-2-6 and A-2-7).
  • Silty soils (A-4 and A-5).
    • Clayey soils, low plasticity.
    • Clays (A-6).
    • Dry-hard.
    • Moist stiff.
    • Wet/sat-soft.
  • Clayey soils, high plasticity clays (A-7)
    • Dry-hard.
    • Moist stiff.
    • Wet/sat-soft.

Bedrock:

  • Solid, massive, and continuous.
  • Highly fractured, weathered.

Note: Bolded information reflects the material types that are relevant in this project.

Table 2 reflects the additional significance of other material engineering properties beyond strength properties for PCC materials in the analysis process. As an example, while concrete modulus of rupture (MR) was the main material input for the AASHTO 1993 rigid pavement design procedure (along with the modulus of elasticity), the MEPDG allows correlations through level 2 inputs with compressive strength and requires other volumetric properties such as shrinkage, CTE, specific heat, and thermal conductivity for analysis.(12) In addition, strength parameters that are used in the analysis include compressive strength, modulus of elasticity, and tensile strength for CRCP. The modulus of elasticity has a much greater effect on performance with the MEPDG than with the AASHTO 1993 procedure. In other words, the MEPDG offers a framework to optimize mix designs to balance a range of strength, modulus, CTE, shrinkage, and other engineering properties for improved performance.(13)

 

Table 2. PCC material inputs beyond strength considered by the MEPDG for JPCP and CRCP.

Material Inputs Required for Critical Response Computations Material Inputs Required for Distress/Transfer Functions Material Inputs Required for Climatic Modeling
  • Static modulus of elasticity (E) adjusted with time.
  • Poisson’s ratio.
  • Unit weight.
  • CTE.
  • MR over time.
  • Split tensile strength (CRCP only).
  • Ultimate shrinkage.*
  • Amount of reversible shrinkage.
  • Time to achieve 50 percent of ultimate shrinkage.
  • PCC zero-stress temperature.**
  • Surface shortwave absorptivity.
  • Thermal conductivity.
  • Heat capacity.

*Estimated from compressive strength, cement type, curing type, cement content, and w/c ratio.

**Estimated from cement content and mean monthly temperatures at the project location.

 

Likewise, unbound materials are characterized by material parameters that account for the changing stress state in the material with seasonal changes in moisture conditions in a specific location. The gradation of the soil, maximum dry density, and optimum moisture content are key inputs to the procedure.

The MEPDG also requires the input of construction and field-specific parameters that are critical to performance. These construction or site features are not restrictive to a particular material, but they are associated with specific material index properties, climatic conditions, and construction practices. For example, base erodibility is a function of base material properties such as the strength of the base layer, the amount of fines, and site conditions such as the level of precipitation and traffic load repetitions.

Correlations Developed/Adapted for the MEPDG

The calibration of the rigid pavement distress models utilized several inputs from levels 2 and 3 based on the best information available from literature and LTPP databases. The following is a partial list of correlations used in the MEPDG models:

SELECTION OF MATERIAL PARAMETERS

A preliminary list of material properties was prepared for developing predictive models based on inputs required for the MEPDG and their level of significance in performance prediction as well as their importance during the design, construction, or pavement management phases. The materials are classified broadly as PCC materials, stabilized materials, and unbound materials. Note that unbound materials include both coarse-grained and fine-grained soils, which have different mechanical behavior in response to applied stress states. The preliminary material properties are listed in table 3 through table 5 for PCC, stabilized, and unbound materials. These tables list all input variables, identify the conventional source of data for SHAs, and indicate whether a predictive model can be developed for the parameter.

Table 3. PCC material properties and rigid pavement design features considered for generating predictive models.

Material Property

Constant or Time Dependent

Source

Recommended Test Protocol or Data Source

Predictive Model Possibility Yes(Y)/

No(N)

Project Stage: Design (D), QC/QA (C), or Pavement Management (PM)

Level of Significance for Performance

Rehabilitation of New PCC Slabs

Compressive strength

Time dependent

Test

AASHTO T 22(17)

Y

D, C, PM

High

Elastic modulus

Time dependent

Test

ASTM C 469(18)

Y

D, PM

High

Poisson’s ratio*

Constant

Test

ASTM C 469(18)

N

Low 

Flexural strength

Time dependent

Test

AASHTO T 97(19)

Y

D, PM

High

Indirect tensile strength (CRCP only)

Time dependent

Test

AASHTO T 198(20)

Y

D

High

Unit weight*

Constant

Test

AASHTO T 121(21)

N

 D, C

Medium or high

Air content*

Constant

Test

AASHTO T 152(22) or AASHTO T 196(23)

N

 C

Medium (affects durability) 

CTE

Constant

Test

AASHTO TP 60(24)

Y

D, PM

High

Surface shortwave absorptivity*,**

N/A

Estimate

MEPDG default(2)

N

 D

Low 

Thermal conductivity*,**

N/A

Test

ASTM E1952(25) or MEPDG default(2)

N

 D

Low

Heat capacity*,**

N/A

Test

ASTM D2766(26) or MEPDG default(2)

N

 D

Low

PCC zero-stress temperature

N/A

Estimate

MEPDG model(2)

Y

D, PM

Medium (JPCP); high (CRCP)

Ultimate shrinkage*

Time dependent

Estimate

MEPDG predictive model(2)

Y

D, PM

Medium (JPCP); high (CRCP)

deltaT in JPCP and CRCP design**

Time dependent

Estimate

MEPDG calibrated with default (-10 °F)(2)

Y

D, C, PM

High

Erosion in CRCP design**

Time dependent

Estimate

MEPDG predictive model(2)

Y

D

High

EI for JPCP design**

N/A

Estimate

MEPDG recommendation for base type(2)

Y

D

Medium

Rehabilitation of Existing PCC Slabs

Compressive strength

Time dependent

Test

AASHTO T 22(17) (extracted cores)

Y

D, C, PM

High

Elastic modulus

Time dependent

Test

ASTM C 469(18) (extracted cores)

Y

D, PM

High

ASTM D 4694(27) (NDT deflection testing)

Poisson’s ratio*

Constant

Test

ASTM C 469(18) (extracted cores)

N

 D

Low 

Flexural strength

Time dependent

Test

AASHTO T 97(19) (extracted beam)

Y

D, PM

High

N/A = Not applicable. *Parameter was not selected for model development. **Considered a design feature input to rigid pavement design process.

 

Table 4. Chemically stabilized materials properties considered for generating predictive models.

Material Type

Material Property

Constant or Time Dependent

Source

Recommended Test Protocol or Data Source

Predictive Model Possibility Yes (Y)/ No (N)

Project Stage: Design (D), QC/QA (C), or Pavement Management (PM)

Level of Significance for Performance

Lean concrete and cement-treated aggregate

Elastic modulus

Constant

Test

ASTM C 469(18)

Y

D, PM

Medium-high

Flexural strength (for HMA pavement)

Constant

Test

AASHTO T 97(19)

Y

D, PM

Medium-high

Lime-cement-fly ash

Resilient modulus

Time dependent

Estimate

MEPDG predictive model(2)

Y

D, PM

Medium-high

Resilient modulus

Time dependent

Test

AASHTO T 307(28)

Y

D, PM

Medium-high

Lime stabilized soil

Resilient modulus

Time dependent

Estimate

MEPDG predictive model(2)

Y

D, PM

Medium-high

All above material types

Unconfined compressive strength

Time dependent

Test

MDTP, AASHTO T 307(28)

Y

D, C, PM

Medium-high

 

Table 5. Unbound material properties considered for generating predictive models.

Material Property

Constant or Time Dependent

Source

Recommended Test Protocol or Data Source

Predictive Model Possibility Yes (Y)/ No (N)

Project Stage: Design (D), QC/QA (C), or Pavement Management (PM)

Level of Significance for Performance

Resilient modulus determined using two options:

Time dependent

Test

AASHTO T 307(28) or NCHRP Project 1-28A(29)

Y

D, C, PM

High

1. Regression coefficients k1, k2, and k3 for the constitutive model that defines resilient modulus as a function of stress state

Resilient modulus = f(bulk stress, major principal stresses, octahedral shear stress, normalizing stress)

2. Determine resilient modulus for expected in-place stress state from laboratory tests

Model coefficients are different for coarse-grained and fine-grained soils

N/A

Estimate

No standard test; use default values.

N

D

 Low

Maximum dry density*

Constant

Test

AASHTO T 180(30)

N

D, C

 High

Optimum moisture content*

Constant

Test

AASHTO T 180(30)

N

D

 High

Specific gravity*

Constant

Test

AASHTO T 100(31)

N

D

 Low

Saturated hydraulic conductivity*

Constant

Test

AASHTO T 215(32)

N

D

 Medium

Soil water characteristic curve parameters*

N/A

Test

AASHTO T 99, T 100, and T 180(33,31,30)

N

D

 High

Rehabilitation of Existing Pavement and Properties of Soil to Be Left In-Place

Modulus (backcalculated)

Time dependent

Test

ASTM D 4694(27) and D 5858(34)

N

D

 High

Poisson’s ratio*

Time dependent

Estimate

MEPDG default(2)

N

D

 Low

N/A = Not applicable. *Parameter was not selected for model development.

 

In selecting the material properties that require predictive models, the following factors were considered:

Accordingly, several variables in table 3 to table 5 were not selected for model development.

The selected parameters from table 3 to table 5 as well as predictive models to estimate their values were the focus of the literature review performed in this study. The literature review sought to identify independent variables (index properties) that could be used to determine the values of the selected material parameters. A list of index properties that can serve as independent variables in the prediction models is provided at the end of chapter 3.

 


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