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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 4. DATA ASSEMBLY AND MODELING CONSIDERATIONS

Data extraction and assembly are key steps to any model development exercise. Depending on the specific types of investigation involved or the particular needs of the analyses involved, the data extraction and assembly process could control the efficiency of data manipulations that are performed during analyses. Model development is an iterative process and involves stepwise evaluation of the significance of several parameters individually, in combination, and their interactive effects. Therefore, assembling data in a versatile manner that is amenable to model revisions and multiple evaluations is of paramount importance.

The relationships developed in this study were based primarily on data from the LTPP database. The key steps in developing the statistical models were as follows:

 

  1. Identify and assemble all relevant data from the LTPP database.
  2. Evaluate the quality of the assembled data by reviewing the assembled data for inconsistencies and possible errors, while also identifying missing/suspect data items.
  3. Develop methods and procedures for estimating important missing data elements and clean data by resolving anomalies and outliers in a consistent manner.
  4. Select the appropriate prediction model form and identify variables that emerge as significant variables.
  5. Evaluate the reasonableness of the model formulated and verify whether the predicted correlations are meaningful from an engineering standpoint.
  6. Ascertain statistical correctness and suggest tentative prediction models.
  7. Perform sensitivity analysis to validate the tentative models. If validation is not satisfactory, revise the model in step 4.
  8. Confirm final prediction model(s).

 

This approach has been used successfully in previous studies and has resulted in practical prediction models. Note that steps 4 through 8 are an iterative process; therefore, the data extraction and assembly process should allow multiple revisions to the selected variables and model forms.

Data Selection for Current Study

Data in the LTPP program exist for two complementary experiments, General Pavement Studies (GPS) and SPS. GPS experiments usually exist in service pavements incorporated into the LTPP program, while the SPS experiments are mostly newly constructed or rehabilitated pavements or pavements subjected to various maintenance activities at the time they were added to the LTPP program.

The GPS and SPS experiments are as follows:

Each GPS test site consists of a single 500-ft test section over which all factors remain constant. SPS-1 and SPS-2 sites usually consist of a series of adjacent 500-ft test sections with different design and material characteristics or maintenance treatments and rehabilitation strategies. The test section layouts and the material data formats in the various LTPP data tables are reported elsewhere.(141)

The GPS and SPS experimental sections have different data availability. Relative to SPS sections, GPS sections are older pavement projects and were not intentionally built for collecting data under the LTPP program. SPS sections, in contrast, offer more detailed information on materials and construction. Further, the materials used in GPS sections are not necessarily representative of current materials, as several elements of the material manufacturing process and material specifications have changed. With the likelihood that GPS and SPS section data could produce different models, the data extraction and data assembly processes were tailored to analyze these data in two separate groups. Data for the various models by material type were taken from the GPS and SPS sections as shown in table 9.

Table 9. LTPP sections selected to review data for each category.

Material Category

Selected SPS Sections

Selected GPS Sections

PCC materials

SPS-2, SPS-7, and SPS-8

GPS-3, GPS-4, GPS-5, GPS-7, and GPS-9

Stabilized materials

All

All

Unbound materials

All

All

 

The following additional points offer reasons for the selection of materials data collected for PCC models:

Table 10 shows the LTPP data tables that were queried to obtain the data necessary for developing the prediction models. The table also lists the data elements that were obtained from each table.

 

Table 10. LTPP data tables queried to obtain data for review and future analyses.

Data Category

LTPP Data Table

Material or Index Properties

PCC materials

TST_PC01

Compressive strength of cores and cylinders and test date

TST_PC02

Tensile strength and test date

TST_PC03

CTE, aggregate type, and test date

TST_PC04

Elastic modulus, Poisson’s ratio, unit weight, and test date

TST_PC09

MR and test date

SPS2_PCC_MIXTURE_DATA

Mix design, cement type, admixture type and quantity, aggregate type, and gradation for SPS-2

SPS2_PCC_PLACEMENT_DATA

Construction date and curing method for SPS-2

RHB_PCCO_AGGR

Aggregate type for SPS-7

RHB_PCCO_MIXTURE

Mix design, cement type, admixture type and quantity, and aggregate type for SPS-7

RHB_PCCO_CONSTRUCTION

Air temperature at time of construction, curing method, and date for SPS-7

SPS8_PCC_MIXTURE_DATA

Mix design, cement type, admixture type and quantity, and aggregate type for SPS-8

SPS8_PCC_PLACEMENT_DATA

Curing method and construction date for SPS-8

INV_PCC_MIXTURE

Mix design, cement type and content, entrained air content, and curing method for GPS

INV_AGE

Construction date for GPS

Stabilized materials

TST_TB02

Compressive strength

Unbound materials

TST_SS01_UG01_UG02

Gradation

TST_SS02_UG03

Hydrometer analysis

TST_SS04_UG08

AASHTO soil classification

TST_SS11 TST_UG09

Hydraulic conductivity

TST_UG04_SS03

Atterberg's limits

TST_UG05_SS05

Maximum laboratory dry density

TST_UG05_SS05

Optimum laboratory moisture content

TST_UG07_SS07_WKSHT_SUM

Average resilient modulus

Climate data

CLW_VWS_HUMIDITY_ANNUAL

Local precipitation, humidity, and temperature for all sections in the MEPDG calibration files

CLW_VWS_HUMIDITY_ANNUAL

CLW_VWS_HUMIDITY_ANNUAL

Data Review

The data review process sought to verify data availability for the predictive models based on the dependent and independent variables identified in chapters 2 and 3 of this report. The assembled data were reviewed to identify anomalies and missing data elements. The review evaluated whether the following occurred:

To enable such an evaluation, data were assembled for each 500-ft section in the database. As explained earlier, the data for each GPS section correspond to the test site. However, for SPS sections, it was reasonable to use the index properties of a given material from each site to correlate the data to the material properties from multiple test sections that were constructed using the same material. The treatment of PCC data from SPS-2 sections are discussed in detail in the PCC model development section in chapter 5 of this report.

Also included in developing the reference system were layer number, layer type, and construction number.

Key Findings From Data Review

A detailed unpublished report summarizing the results of the data review process was completed in phase I of this study and submitted to FHWA. The following conclusions were presented:

All data available were of adequate quality and completeness for use in model development. However, some missing data and data anomalies were also identified. The specific issues encountered and the methods used to overcome them is discussed in chapter 5 for each material type and material property. A summary of data availability is presented in table 11 for each material type and in table 12 for models to predict rigid pavement design features.

 

Table 11. Summary of data availability for material property predictive models.

Material Type

Material Property

Number of Sections Available for Model

Comment

GPS

SPS*

PCC materials

Compressive strength

250

SPS-2 - LS

84

Does not include supplementary sections for SPS-2

SPS-2 - HS

83

SPS-7

26

SPS-8

10

Total

203

Elastic modulus

344

SPS-2 - LS

77

SPS-2 - HS

76

SPS-7

11

SPS-8

8

Total

172

Flexural strength

349

SPS-2 - LS

50

SPS-2 - HS

51

SPS-7

15

SPS-8

5

Total

121

Tensile strength

95

15

Only for CRCP, SPS data for SPS-7

CTE

214

SPS-2 - LS

4

No comments

SPS-2 - HS

7

SPS-7

2

SPS-8

2

Total

15

Ultimate shrinkage

258

N/A

Only independent variables are in LTPP database. Predictive model uses MEPDG calibration data. See

table 12 discussion in chapter 5

PCC materials (rigid pavement design features)

Zero-stress temperature

245

N/A

EI for JPCP design

N/A

N/A

Erosion in CRCP design

N/A

N/A

deltaT for JPCP and CRCP designs

N/A

N/A

Stabilized materials

N/A**

N/A**

57

Includes only LCB in SPS-2

Unbound materials

Resilient modulus

1,416

712

No comments

N/A = Not applicable; LS = Low strength; HS = High strength.

*SPS data have been summarized by experiment type. SPS-2-LS and SPS-2-HS refer to SPS-2 sections that have been built with low-strength and high-strength concrete mixes.

**Stabilized material data contain only compressive strength data and elastic modulus data for LCB materials.

 

Table 12. Summary of MEPDG calibration sections to develop predictive models for rigid pavement design features.

Calibration Sections

Number of CRCP Sections

Number of JPCP Sections

Non-LTPP sections

33

15

LTPP sections

71

285

Total

104

300

 


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The Federal Highway Administration (FHWA) is a part of the U.S. Department of Transportation and is headquartered in Washington, D.C., with field offices across the United States. is a major agency of the U.S. Department of Transportation (DOT). Provide leadership and technology for the delivery of long life pavements that meet our customers needs and are safe, cost effective, and can be effectively maintained. Federal Highway Administration's (FHWA) R&T Web site portal, which provides access to or information about the Agency’s R&T program, projects, partnerships, publications, and results.
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