TECHBRIEF Estimation of Key PCC, Base, 

Subbase, and Pavement 

Engineering Properties from 

Routine Tests and Physical 

Characteristics

FHWA Publication No.: FHWA-HRT-12-032

FHWA Contact: Larry Wiser, HRDI-30, (202) 493-3079, 

larry.wiser@dot.gov

This document is a technical summary of the Federal Highway 

Administration report, Estimation of Key PCC, Base, Subbase, and 

Pavement Engineering Properties from Routine Tests and Physical 

Characteristics (FHWA-HRT-12-030).(1)

This TechBrief presents models developed by statistical methods to 

predict key material properties for portland cement concrete (PCC), 

chemically stabilized materials, and unbound base, subbase, and 

subgrade materials. It also presents models to predict design inputs 

specific to the Mechanistic-Empirical Pavement Design Guide

(MEPDG).(2) The models were developed under the Long-Term 

Pavement Performance (LTPP) data analysis study, Estimation 

of Key PCC, Base, Subbase, and Pavement Engineering Properties 

from Routine Tests and Physical Characteristics.

(1) The predictive 

models were developed using data from the LTPP database as well 

as data generated from analyses used in the calibration of the 

MEPDG distress models under National Cooperative Highway 

Research Program Project 1-40D.(3,4)

Introduction

Material characterization is a critical component in all aspects of pavement engineering—analysis, design, construction, quality control (QC) 

and quality assurance (QA), pavement management (PM), and rehabilitation. At each stage during the life of a project, the influence 

of fundamental engineering material parameters on the long-term 

performance of the pavement can be predicted using advanced tools 

like the MEPDG.(2) Measuring the properties directly is preferable, 

but it is often not practical or possible. Consequently, there is a need 

for reliable models that can be used to estimate key parameters 

and provide more information about material properties, which is 

addressed to only a limited extent with currently available laboratory 

and field testing resources. 

Reliable correlations between material properties and a combination 

of mix design parameters, physical characteristics, and index properties offer a cost-effective alternative and are equivalent to the level 

2 inputs used in the MEPDG. The LTPP database offers an opportunity 

to develop correlation equations to estimate material properties.(3)

Research, Development, and 

Technology

Turner-Fairbank Highway 

Research Center

6300 Georgetown Pike

McLean, VA 22101-2296

http://www.fhwa.dot.gov/

research/tfhrc/programs/

infrastructure/pavements/ltpp/

The Long-Term Pavement Performance 

(LTPP) program is a large research project

for the study of in-service pavements 

across North America. Its goal is to extend 

the life of highway pavements through 

various designs of new and rehabilitated 

pavement structures, using different materials and under different loads, environments, subgrade soil, and maintenance 

practices. LTPP was established under the 

Strategic Highway Research Program and 

is now managed by the Federal Highway 

Administration.
2

Therefore, it served as the primary source of data in 

developing the models presented here. To develop 

models for design feature parameters specific to 

the MEPDG, LTPP data were combined with data 

generated from MEPDG analyses of LTPP sections. 

Material Properties Identified for 

Model Development

In selecting key material properties that required 

predictive models, the following parameters were 

considered:

•	 Input requirements for the MEPDG design 

procedure and the sensitivity of the specific 

parameter for performance prediction.

•	 Typical agency needs for determining material 

properties for QA and PM.

•	 Challenges involved in determining the material 

property of interest directly through laboratory 

testing.

•	 The specific material property’s relationship 

with material parameters, physical properties, 

routine test results, and typical index properties.

•	 The potential to develop a predictive model.

•	 Likelihood that the actual value of the material 

property would deviate from typical values or 

assumed defaults and the significance of such a 

deviation in predicting performance.

•	 Data availability in the LTPP database.

Predictive models were developed for PCC compressive strength, PCC flexural strength, PCC elastic 

modulus, PCC tensile strength, lean concrete base 

modulus, and unbound materials resilient modulus.

In addition, rigid pavement design feature input 

properties were developed using the MEPDG calibration data. These include the jointed plain concrete 

pavement (JPCP) and the continuously reinforced 

concrete pavement (CRCP) deltaT parameter, which 

is defined as the equivalent temperature differential 

that corresponds to the effective permanent curl-warp 

locked into the pavement. For all PCC material properties, multiple models were developed for use under 

different project situations.

Development of Prediction Models

In developing the models, a uniform set of statistical 

criteria were used to select independent parameters 

to define a relationship as well as to mathematically formulate prediction functions. The analyses 

examined several statistical parameters in choosing 

the optimal model and determining the predictive 

ability of the model. In general, the optimal set of 

independent variables (Mallows coefficient, Cp), 

the interaction effects (variance inflation factor), 

the significance of the variable (p-value), and the 

goodness of fit (R2

) were verified. Additionally, 

the study validated and/or refined existing models 

and developed new relationships. In the statistical 

analyses, the following tasks were accomplished:

•	 PCC compressive strength was correlated 

with mix design parameters and aggregate 

properties. It was found to increase with 

decreasing water/cementitious materials (w/c) 

ratio, increasing cementitious materials content 

(CMC), increasing curing time, increasing unit 

weight, decreasing maximum aggregate size 

for a given level of w/c ratio, and decreasing 

fineness modulus of the sand. 

•	 PCC flexural strength and compressive strength 

were correlated using a power model. While 

similar relationships are used in the industry, 

they were validated and refined using the LTPP 

data that make them appropriate for paving 

mixes. It was also correlated to the w/c ratio, unit 

weight, and CMC for predictions at different ages. 

The correlations were improved significantly 

in these new models with the inclusion of 

additional parameters. The flexural strength 

increased proportionally with all parameters 

listed except w/c ratio, with which it bears an 

inverse relationship.

•	 PCC elastic modulus, compressive strength, 

and unit weight were correlated using a power 

model, as has been done in past studies. These 

relationships were validated and modified with 

the data used in this study. Prediction models 

were also developed based on aggregate type, 

unit weight, compressive strength, and age. 

The elastic modulus increased with an increase 

in magnitude of all parameters listed in this item. 

However, the predictive ability of the elastic 

modulus models was marginal, and users are 

advised to use them with caution or verify them 

using laboratory testing for smaller samples.

•	 PCC splitting tensile strength and compressive 

strength were correlated using a power 

relationship. 

•	 The coefficient of thermal expansion (CTE) of 

PCC was most sensitive to the coarse aggregate 

type and the volumterics of the mix design. CTE 

recommendations are made for each aggregate 

type, and a correlation to mix volumetrics 

was developed.

•	 JPCP deltaT negative gradient was found to 

increase with an increase in temperature range at 

the project location for the month of construction 

and slab width and was found to decrease with 

a decrease in PCC thickness, unit weight, w/c 

ratio, and latitude of the project location.
3

•	 CRCP deltaT negative gradient was found 

to increase with an increase in maximum 

temperature at the project location for the 

month of construction and maximum 

temperature range and was found to decrease 

with the use of chert, granite, limestone, and 

quartzite. 

•	 The modulus of lean concrete base and its 

28-day compressive strength were correlated 

based on a power model. 

•	 The prediction of granular base or subgrade 

material resilient modulus was possible using 

parameters k1, k2, and k3 of the constitutive 

model as follows:

•	 The parameter k1 was found to increase 

with a decrease in percent passing the 

1

/2-inch sieve, an increase in liquid limit, 

and a decrease in optimum moisture 

content.

•	 The parameter k2 was found to increase 

with a decrease in percent passing the 

No. 80 sieve, liquid limit, and percent 

gravel and an increase in the maximum 

particle size of the smallest 10 percent of 

the soil sample.

•	 The parameter k3 was dependent on the 

soil classification (i.e., coarse-grained 

versus fine-grained materials).

Summary of Models Developed

This section provides a summary of the models. 

Refer to the corresponding main report, Estimation 

of Key PCC, Base, Subbase, and Pavement 

Engineering Properties from Routine Tests and 

Physical Characteristics, for detailed information 

about the quality of the predictive models and the 

range of data that can be applied to each model.(1)

Figure 1 through figure 5 present the PCC compressive strength models, figure 6 through figure 8 

present PCC flexural strength models, figure 9 

through figure 11 present PCC elastic modulus 

models, figure 12 presents a PCC indirect tensile 

strength model, and figure 13 and figure 14 present PCC CTE models. Note that the elastic modulus 

models yielded a correlation with a low R2 and need to 

be used with caution.

The applications for figure 1 are 28-day strength for 

design and QA.

The applications for figure 2 are design, QA, PM, and 

opening strength for ages less than 1 year.

The applications for figure 3 are design, QA, PM, and 

opening/in situ strength for ages less than 1 year.

The applications for figure 4 are design, QA, PM, and 

in situ strength at any age.

The applications for figure 5 are rehabilitation 

design and in situ strength for ages greater than 

5 years.

Where:

fc,28d = Compressive strength at 28 days.

fc,t = Compressive strength at time t, days.

fc,LT = Long-term compressive strength.

w/c = Water/cementitious materials ratio. 

CMC = Cementitious materials content, lb/ft3

. 

uw = Unit weight, lb/ft3

. 

t = Age, years. 

MAS = Maximum aggregate size, inch.

FM = Fineness modulus of fine aggregate.

fc,28d = 4,028.41841 - 3,486.3501 × w/c + 

4.02511 × CMC

Figure 1. Compressive strength model 1 for 28-day 

cylinder strength.

fc,t = 6,358.60655 + 3.53012 × CMC -

34.24312 × w/c × uw + 633.3489 × ln(t)

Figure 2. Compressive strength model 2 for shortterm cylinder strength.

fc,t = 98.92962 + 5.70412 × CMC + 28.48527 

× uw + 2,570.13151 × MAS × w/c -

199.84664 × FM + 611.30879 × ln(t)

Figure 3. Compressive strength model 3 for shortterm core strength.

fc,t = -6,022.44 - 854.46 × w/c + 4.8656 × 

CMC + 68.5337 × uw + 533.15 × ln(t)

Figure 4. Compressive strength model 4 for all ages 

core strength.

fc,LT = -3,467.3508 + 3.63452 × CMC + 

0.42362 × uw2

Figure 5. Compressive strength model 5 for longterm core strength.
4

The applications for figure 6 are design and PM when 

compressive strength at a given age is available.

The applications for figure 7 are design and PM 

when index properties are available and predicting 

for any age.

The applications for figure 8 are design and PM 

when index properties are available and predicting 

for any age.

Where:

MR = Flexural strength, psi.

MRt = Flexural strength at age t years, psi.

f ’c = Compressive strength determined at the same 

age, psi.

w/c = Water/cementitious materials ratio.

CMC = Cementitious materials content, lb/yd3

.

uw = Unit weight, lb/ft3

.

t = Pavement age, years.

The applications for figure 9 are design and 

PM when compressive strength at given age and 

aggregate type are available.

The applications for figure 10 are design and PM 

when compressive strength at a given age is available 

(predicts for any age).

The applications for figure 11 are design and PM 

when 28-day compressive strength is available 

(predicts for any age).

Where:

Ec = PCC elastic modulus, psi.

Et = Elastic modulus at age t years.

Ec,t = Elastic modulus at age t years.

f ’ct = Compressive strength at age t years.

uw = Unit weight, lb/ft3

.

f ’c = Compressive strength at same age, psi.

f ’c, 28-day = 28-day compressive strength.

t = Age at which modulus is determined, years.

Dagg = Regressed constant depending on aggregate 

type: andesite (1), basalt (0.9286), chert (1.0079), 

diabase (0.9215), dolomite (1.0254), granite (0.8333), 

limestone (1), quartzite (0.9511), and sandstone (1). 

The application for figure 12 is design when 

compressive strength is available.

Where:

ft = Indirect tensile strength of the PCC material.

f ’c = Compressive strength of the mix determined at 

the same age.

The applications for figure 13 are design, QC, and 

PM when coarse aggregate rock type is available.

MRt = 676.0159 - 1,120.31 × w/c + 4.1304 

× uw + 35.74627 × ln(t)

Figure 7. Flexural strength model 2 based on age, 

unit weight, and w/c ratio.

MRt = 24.15063 + 0.55579 × CMC + 2.96376 

× uw + 35.54463 × ln(t)

Figure 8. Flexural strength model 3 based on age, 

unit weight, and CMC.

MR = 22.7741 × f ’c

0.4082

Figure 6. Flexural strength model 1 based on 

compressive strength.

Ec = (4.499 × (uw)

2.3481 × (f ‘c)0.2429) × Dagg

Figure 9. Elastic modulus model 1 based on 

aggregate type.

 Ec,t = 59.0287 × (f ‘ct)

1.3 × (ln( t

 ))-0.2118

 0.03

Figure 10. Elastic modulus model 2 based on age 

and compressive strength.

Figure 11. Elastic modulus model 3 based on age 

and 28-day compressive strength.

 Ec,t = 375.6 × (f ‘c28-day)

1.1 × (ln( t

 ))0.00524

 

0.03

ft = 8.9068 × (f ‘c)

0.4785

Figure 12. PCC indirect tensile strength model 

based on compressive strength.

Basalt (4.86), Chert (6.9), Diabase (5.13), 

Dolomite (5.79), Gabbro (5.28), Granite (5.71), 

Limestone (5.25), Quartzite (6.18), Andesite 

(5.33), Sandstone (6.33)

Figure 13. CTE model 1 based on aggregate type.
5

The applications for figure 14 are design, QC, and 

PM when coarse aggregate rock type and mix design 

proportioning are available.

Where:

CTEPCC = CTE of the PCC material, x10-6 inch/inch/°F.

VCA = Volumetric proportion of the coarse aggregate 

(0 to 0.6).

CTECA = Constant determined for each aggregate 

type (basalt (3), chert (6.4), diabase (3.4835), dolomite

(5.1184), gabbro (3.75), granite (4.7423), limestone 

(3.2886), quartzite (6.1), andesite (3.6243), and 

sandstone (4.5)).

Figure 15 lists the deltaT model for JPCP for rigid 

pavements. Because of the limitations of the model 

developed for CRCP deltaT, this model is not 

included in this document. The applications are design 

and PM when mix design and construction weather 

information are available.

Where:

deltaT/inch = Predicted gradient in JPCP slab, °F/inch.

TR = Difference between maximum and minimum 

temperature in construction month, °F.

SW = Slab width, ft.

PCCTHK = JPCP slab thickness, inch.

uw = Unit weight of PCC used in JPCP slab, lb/ft3

.

w/c = Water/cementitious materials ratio.

latitude = Latitude of the project location, degrees.

Figure 16 shows the lean concrete base elastic 

modulus model. The applications are design and PM 

when 28-day compressive strength information is 

available.

Where:

ELCB = Elastic modulus of the lean concrete base layer.

f ‘c, 28d = 28-day compressive strength of the lean 

concrete base material.

Figure 17 shows the model developed for resilient 

modulus for coarse- and fine-grained base layers and 

soil. The applications are design, QA, and PM when 

gradation, Atterberg limits, and moisture content are 

known.

The parameters in figure 17 are defined in figure 18 

through figure 20 as follows where:

Mr = Resilient modulus, psi.

q = Bulk stress ( s1 + s2 + s3).

s1 = Major principal stress.

s2 = Intermediate principal stress = s3 for Mr test on 

cylindrical specimen.

s3 = Minor principal stress/confining pressure.

toct = Octahedral shear stress = 1/3√(s1 - s2)2 + (s1 - s3)2 + (s2 - s3)2.

Pa = Normalizing stress (atmospheric pressure).

k1, k2, and k3 = Regression constants (obtained by 

fitting Mr test data to this model form.

Where:

PCTHALF = Percent passing the 1

/2-inch sieve.

CTEPCC = CTECA × VCA + 6.4514 × (1 - VCA)

Figure 14. CTE model 2 based on mix volumetrics.

deltaT/inch = -5.27805 - 0.00794 × 

TR - 0.0826 × SW + 0.18632 × PCCTHK

+ 0.01677 × uw + 1.14008 × w/c

+ 0.01784 × latitude

Figure 15. deltaT JPCP design.

ELCB = 58,156 √f ’c,28d + 716,886

Figure 16. deltaT JPCP design. Lean concrete base 

elastic modulus model.

 Mr = k1( q )

k2

(

toct)

k3

 Pa

 Pa 

Figure 17. Resilient modulus for coarse- and finegrained base layers and soils.

k1 = 1,446.2 - 4.56764 × PCTHALF + 4.92 

× LL - 27.73 × OPTMOIST

Figure 18. Prediction model for k1.

k2 = 0.45679 - 0.00073376 × PCTNO80 -

0.00269 × LL + 0.00060555 × 

PCTGRVL + 12.97 × D10

Figure 19. Prediction model for k2.

k3 = -0.188 (for fine-grained soils)

k3 = -0.153 (for coarse-grained materials)

Figure 20. Prediction model for k3.
6

LL = Liquid limit, percent.

OPTMOIST = Optimum moisture content, percent.

PCTNO80 = Percent passing No. 80 sieve.

PCTGRVL = Percent gravel fraction (0.078- to 2.36-inch 

size).

D10 = Maximum particle size of the smallest 10 percent 

of soil sample.

Conclusions

Material characterization is critical to pavement 

analysis, design, and performance prediction. While 

the availability of the MEPDG has allowed great 

flexibility in the evaluation of as-designed and asbuilt pavements, the need for extensive input data 

can be a challenge for agencies adopting the MEPDG. 

The ability to develop prediction models for material 

properties and design features was explored under 

this study. It was found that information on mix 

constituents, their physical characteristics, and index 

properties could be used to develop correlations 

to predict material properties for PCC materials, 

stabilized materials, and unbound bases and subgrade 

materials. 

References

1. Rao, C., et al. (2012). Estimation of Key PCC, Base, 

Subbase, and Pavement Engineering Properties 

from Routine Tests and Physical Characteristics, 

Report No. FHWA-HRT-12-030, Federal Highway 

Administration, Washington, DC.

2. National Cooperative Highway Research Program. 

(2004). Mechanistic-Empirical Pavement Design 

Guide, NCHRP Project 1-37A, Transportation 

Research Board, Washington, DC.

3. Turner-Fairbank Highway Research Center. How to 

Get LTPP Data, Federal Highway Administration, 

Washington, DC. Obtained from: http://www.

fhwa.dot.gov/research/tfhrc/programs/

infrastructure/pavements/ltpp/getdata.cfm. 

4. National Cooperative Highway Research Program. 

(2006). Research Results Digest 308: Changes 

to the Mechanistic-Empirical Pavement Design 

Guide Software through Version 0.900, NCHRP 

Project 1-40D, Transportation Research Board, 

Washington, DC.

Researchers—This study was performed by Applied Research Associates, Inc., Champaign, IL.

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Availability—This TechBrief may be obtained from FHWA Product Distribution Center by e-mail to 

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dot.gov/research.

Key Words—LTPP, Material properties, MEPDG, Prediction model, Index properties, PCC, Compressive 

strength, Flexural strength, Elastic modulus, Tensile strength, Coefficient of thermal expansion (CTE), 

JPCP built-in gradient, Lean concrete base, Resilient modulus.

Notice—This document is disseminated under the sponsorship of the U.S. Department of 

Transportation in the interest of information exchange. The U.S. Government assumes no liability for 

the use of the information contained in this document. The U.S. Government does not endorse products 

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considered essential to the objective of the document.

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september 2012 FHWA-HRT-12-032

HRDi-30/09-12(700)E