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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations

This report is an archived publication and may contain dated technical, contact, and link information
Publication Number: FHWA-RD-03-049
Date: November 2005

Improving Pavements With Long-Term Pavement Performance: Products for Today and Tomorrow

Paper 1. The Use of The Long-Term Pavement Performance Database in The Pavement Engineering Curriculum At Michigan State University

Neeraj Buch1 and Karim Chatti 2


The authors describe the inclusion of the Long-Term Pavement Performance (LTPP) data in the pavement engineering curriculum at Michigan State University (MSU) using two examples: one from an undergraduate course on pavement rehabilitation, and one from a graduate course on pavement analysis and design. The design examples illustrate the use of LTPP data in computing pavement responses, predicting traffic, developing rehabilitation strategies, and predicting pavement performance for both rigid and flexible pavements.


Pavement engineering curriculum in the Department of Civil and Environmental Engineering (CEE) at Michigan State University (MSU) consists of a suite of five courses, two at the undergraduate level (4XX series senior level) and three at the graduate level (8XX series). First-year graduate students are allowed to enroll in the 4XX design courses if they lack the necessary background in pavement analysis and design. The two undergraduate courses are titled “CE431-Pavement Design and Analysis-I,” and “CE432-Pavement Rehabilitation.” The three graduate courses are titled “CE831-Pavement Design and Analysis-II,” “CE835-Pavement Management,”and “CE837-Infrastructure Materials.” Three pavement engineering faculty members share the load of teaching these five courses.

1Assistant Professor, Michigan State University, Department of Civil and Environmental Engineering, East Lansing, MI 48824 (517) 355-0012, Fax: 517-432-1827, buch@egr.msu.edu

2Assistant Professor, Michigan State University, Department of Civil and Environmental Engineering, East Lansing, MI 48824, (517) 355-6534, Fax: 517-432-1827, chatti@egi.msu.edu


Course CE431 is offered twice a year (fall and summer semesters). The average fall enrollment is 30 and 15 during the summer. The prerequisites for this course are a required junior-level course in construction materials and a course in soil mechanics. The course description reads as follows: “The students will be exposed to pavement structural design, evaluation of performance measures, failure mechanisms, thickness design procedures (state-of-the-practice), and design considerations for surface friction, pavement joints, and drainage.” The assessment is based on homework assignments (20 percent of the grade), two design projects (30 percent), two exams (30 percent) and weekly quizzes (20 percent). The text is Pavement Analysis and Design, by Yang H. Huang (1993).

Course CE432 is offered once a year (spring semester), with average enrollment of 25. The course description reads as follows: “The students will be exposed to techniques in pavement evaluation, distress identification, pavement rehabilitation strategies, life cycle cost analysis, and strategy selection.” The assessment is based on homework assignments (10 percent of the grade), two design projects (40 percent), two exams (40 percent), and weekly quizzes (10 percent). The course uses the Techniques for Pavement Rehabilitation (Reference Manual), Federal Highway Administration, as a textbook.


Course CE831 is offered once a year during the spring semester and uses the Huang (1993) text. The average enrollment is seven people. The course description reads as follows, “This course deals with advanced pavement analysis and design. The students will be exposed to theoretical models, numerical models, performance characterization and damage models for pavements.” The main objectives of the course are to expose students to advanced pavement analysis techniques. As such, they learn about the different pavement response and performance prediction models. The course is divided into two parts dealing with flexible and rigid pavements, respectively. The, students learn to use pavement analysis programs such as KENLAYER (Huang, 1993), MICHPAVE, MICHBACK, (Buch, et al., 1999) and Stet 2000 (ERES Consultants, 2000). The assessment is based on homework assignments (40 percent of the grade), two exams (50 percent) and weekly quizzes (10 percent). The homework assignments were divided into two categories: Type I comprises the conventional assignment where questions are directly related to the lectures, assigned reading, and class notes, purpose being to measure “short-term transfer” of knowledge. Type II assignments consist of open-ended questions and required students to access data from the DataPave 3.0 software to conduct the analysis.

The use of DataPave 3.0 in CE835 and CE837 is under development; hence, these courses will not be discussed further in this paper.



Traditionally, this course consists of two design projects, one for rigid pavement and one for flexible pavement rehabilitation. During the first few offerings of this course, the distress data were obtained from local and county roads; the pavement cross sections and traffic distributions were assumed; and little or no deflection information was available. Because the sites selected were from local and county roads, the distress types were very restricted. The disadvantage of these projects was that many assumptions had to be made to complete the analysis. Moreover, the absence of time-series data did not adequately demonstrate the idea of pavement deterioration. With these shortcomings in mind, the instructors decided to explore the use of DataPave 2.0/3.0 as a source for extracting real-time series pavement distress and deflection data. The database also provides information on traffic growth (in terms of average daily traffic (ADT), equivalent standard axle loads in thousands (KESALS), and axle distribution), pavement inventory, and climate.

It was envisioned that after completing the project the students would:

  • Be familiar with the LTPP database.
  • Be able to extract information from the database.
  • Be able to synthesize traffic, distress and deflection data.
  • Be able to use the synthesized data for pavement analysis and determine remaining life of the pavement.
  • Be able to develop preventative and rehabilitation strategies for the repair of distressed pavements.

The class received entire project statement with the tasks and the data on the first day. Because the class was large and students had varying pavement experience backgrounds, the instructors extracted the raw data from the master database and gave it to the groups rather than providing access to the entire database. Students were introduced to typical data, definitions of terms, and data layout through a series of tutorial sessions held after regular class hours. As part of the project deliverables, each group was to develop:

  • Graphical relationships among time, distress, and severity level for the assigned LTPP section. Graphical relationships between time and traffic (for some groups this was time versus ADT, for some it was time versus KESALs, and for others it was time versus axle growth) for the assigned LTPP section.
  • Graphical relation between International Roughness Index (IRI) and distress deterioration (if any).
  • Analysis of the deflection data to quantify load transfer efficiency (LTE) for rigid pavements, backcalculated layer parameters (for both pavement types), void potential and lateral support (for rigid pavements), and to relate this analytical data to the observed pavement performance.
  • The rehabilitation strategy/strategies to "fix" the problems and extend pavement life, selection of final strategy would be based on engineering and economic considerations.
  • Sketch of the pavement section indicating the distress type, severity, and locations.

Reports were graded on format, technical content, and group interviews.

Inventory Data

To illustrate the deliverables (for rigid pavements), the authors have chosen examples from Strategic Highway Research Program (SHRP) ID 1-4084-1, General Pavement Study-4 (GPS) Jefferson County, AL. This section was assigned to a group of three students. The original surface layer is 266.7 millimeters (mm) (10.5 inches) of portland cement concrete (PCC) jointed reinforced concrete pavement (JRCP); the base layer is 142.24 mm (5.6 inches) of gravel (uncrushed); the subbase layer is 347.98 mm (13.7 inches) of soil aggregate mixture (predominantly coarse grained); and the subgrade layer is clayey sand. The original construction date of the pavement is June 1, 1970. The inside and outside shoulder is asphalt. There is no subsurface drainage. The average joint spacing is 17.575 meters (m) (57.5 feet (ft)). Round dowels were used for load transfer and the longitudinal steel content is 0.1 percent. The freezing index is -2.94 degrees Celsius (oC) (27.3 degrees Fahrenheit (oF)) days, and the climatic region is wet-no freeze. This region experiences 375 m (1476.4 inches) of precipitation, and 63 days above 36.25 oC (90 oF). Climatic data was available for 27 years. In 1995, the annual average daily traffic (AADT) was 13,057, and the average daily truck traffic (ADTT) was 639. The pavement inventory and cross section information is summarized in figure 1, which is a screen capture from DataPave 3.0.

Pavement inventory and cross section information for SHRP ID 1-4084-1

Figure 1. Pavement inventory and cross section information for SHRP ID 1-4084-1

Distress Evaluation

The distress data were extracted from DataPave 3.0 tables MON_DIS_JPCC_REV and MON_DIS_JPCC_REV. In summary, the distresses included medium-to-high-severity faulting, low-to-medium-severity transverse cracks, low-to-high-severity spalling, sealant damage, polished aggregates, scaling, and map cracking. Figures 2 and 3 illustrate the magnitude and severity of distresses as a function of time and location (where available).

Distress progression as a function of time

Figure 2. Distress progression as a function of time

Progression of distress as a function of time

Figure 3. Progression of distress as a function of time

The other distresses observed for this SHRP ID are summarized in table 1.

Table 1. Other distresses found in this section


Year Low-Severity Longitudinal Joint Seal Damage (m) Low-Severity Longitudinal Joint Spalling (m) Scaling(square meter (m2)) Polished aggregate (m2) Map Cracking (m2)
1991 - - 557.4 - -
1993 305 10 - 366 559.6
1997 - - - 305 -

Dashes in cells represent “no data available” or “zero” distress.
1 m = 3.28 ft
1 m2 = 10.8 ft2

A review of the relationship between pavement roughness and distress shows that as the distresses increase in magnitude the pavement appears to get rougher. Interestingly, it can be hypothesized that the roughness precedes the manifestation of distress. These relationships are summarized in figures 4 and 5.

Relationship between IRI and joint and crack faulting

1 meter per kilometer (m/km) = 63.36 inches per mil (inches/mi)
1 mm = 0.039 inches

Figure 4. Relationship between IRI and joint and crack faulting

 Relationship between IRI and transverse cracking

Figure 5. Relationship between IRI and transverse cracking

Functional Evaluation

The functionality of a pavement can be described in many forms, such as the International Roughness Index (IRI), (as reported in DataPave 3.0) and the Present Serviceability Index (PSI) as characterized by American Association of State Highway and Transportation Officials (AASHTO). Using the relationship between PSI and IRI reported by Hall and Correa (1999), the PSI was calculated to be between 2.0 to 2.5.

Structural Evaluation

For the structural evaluation, the design groups extracted deflection and temperature gradient information from the data tables labeled MON_DEFL_DROP DATA, MON_TEMP_DEPTH DATA, and MON_TEMP_VALUES_ DATA.

The deflection at the midslab (J1) was used to compute the modulus of elasticity of concrete (Ec) and the modulus of subgrade reaction (k). Moreover, the data were used to subdivide the project into three distinct subsections based on the magnitudes of the deflections. The variation in deflection as a function of project length is summarized in figure 6. Deflection data at the corner of the slab (J2) were used for calculating the void potential underneath the corner of PCC slabs; the deflection from the slab edge (J3) was used to compute the lateral support provided by the shoulder; and the data from positions J4 and J5 were used to compute approach and leave load transfer efficiencies (LTE) respectively. Figure 7 illustrates the various falling weight deflectometer (FWD) test locations.

Deflection profile as function of distance

Figure 6. Deflection profile as function of distance

LTPP FWD positions

Figure 7. LTPP FWD positions

The backcalculated layer parameters for this example section are summarized in figure 8.

Backcalculated layer parameters

Figure 8. Backcalculated layer parameters

The deflection ratio (D-ratio), which is a good indicator of lateral support along the edge of the slab, was calculated. The results are summarized in figure 9. If the slab has a uniform adequate support, this ratio should be close to 1. The lack of lateral support results in D-ratio values significantly greater than 1.

D-ratio versus point location for years 1990, 1994, and 1999

Figure 9. D-ratio versus point location for years 1990, 1994, and 1999

Figure 10 shows the LTE calculated from J4 for the 3 years at each point location.

LTE versus point location (J4)

Figure 10. LTE versus point location (J4)

The group further investigated the relationship between void potential and load transfer efficiency. The results from this investigation are summarized in figure 11.

Relationship between LTE and void ratio

Figure 11. Relationship between LTE and void ratio

The traffic data for this section were available as annual KESALs between 1976 and 1989. Based on this information the group computed the growth rate and subsequently was able to predict future ESALs for the year 2011. Figure 12 summarizes the ESAL information for this project.

Measured ESAL and predicted ESAL

Figure 12. Measured ESAL and predicted ESAL

Once the individual pieces of the project were analyzed, the next task was to synthesize this information, rank the distresses, and suggest rehabilitation strategies. The ranking was based on backcalculated layer parameters, severity levels of distresses, magnitude of void potential, and LTE magnitudes. Table 2 summarizes ranking information, and figure 13 illustrates the distress map.

Table 2. Ranking based on distress and computed responses

At Cracks and Joints At mid Slab and Edge
Point Location (m) Average Edge Faulting LTE (J5)% LTE (J4)% Void Potential Spall Point Location J1 (m) Peak Deflection Average K Average E D ratio
18 15 15 16 - 5.8 7 5 9 11
14 9 10 11 L 15.2 13 12 11 4
18 1 4 1 L,M 22.9 9 19 4 3
17 7 6 2 L 28.3 16 3 20 1
18 - - - L


- - - -
18 3 1 3 L 40.8 13 13 8 7
13 8 5 9 L 44.8 4 17 3 17
18 16 18 19 L 50.9 7 4 16 12
18 18 12 12 - 57.6 12 6 14 2
10 5 7 5 - 62.5 1 15 1 16
10 20 14 7 L 66.8 2 1 17 9
18 - - - L,H


- - - -
18 14 17 6 L 76.5 4 20 2 20
18 - - - L


- - - -
16 12 13 16 L 83.2 2 2 15 5
17 - - - L,M


- - - -
3 4 3 4 L,M,H 93.0 6 14 5 19
18 10 9 15


98.5 18 16 12 10
6 19 19 18 L 107.0 20 8 19 14
6 6 8 13


115.2 18 9 18 13
3 - - - L - - - - -
6 - - -


- - - - -
2 11 20 10 L 128.0 13 10 13 18
12 - - -


-   - - -
1 13 16 13 L 136.2 10 11 6 6
14 - - - L


- - - -
6 17 11 - L 144.5 11 7 10 8
3 2 2 7


150.3 17 18 7 15

1 m = 3.28 ft

Example of a distress map

1 m = 3.28 ft

Figure 13. Example of a distress map

Based on the overall distress condition and the ride quality of the pavement section, the group recommended the construction of an unbounded concrete overlay, whose design was done in accordance with the AASHTO 1993 procedure. It was also suggested that pre-overlay repairs be conducted prior to the construction of the overlay.

Similar design projects were done by other student groups, but space limitations prohibit presenting flexible pavement rehabilitations projects. The subsequent sections will describe the use of DataPave 3.0 in CE831 and the example(s) described deal with flexible pavements.


Traditionally, the course includes several assignments dealing with pavement analysis and a comprehensive design project using the mechanistic-empirical approach. A main shortcoming of the assignments and project was the lack of “real” performance data that could be used to evaluate the accuracy of the mechanistic predictions. Accordingly, the instructors decided to explore the use of the LTPP DataPave 3.0 data as a source for extracting “real” pavement response and performance data, which the students could use to evaluate existing performance prediction models. The database also provides information on traffic growth (in terms of ADT, KESALS, and axle distribution), pavement inventory, and climate.

The reports were graded according to similar criteria to those in the CE432 class. These criteria were handed out to the students along with the problem statement.

The overall objectives of the LTPP-based assignment are similar to those in CE432, with the specific objectives being:

  • To select 3 sections from the assigned SPS-1 sites.
  • To synthesize the inventory, deflection, roughness, distress and traffic data.
  • To investigate the relationships between pavement performance and response
  • To provide an engineering discussion summarizing the findings.

Each student had to select three sections from the assigned SPS-1 site, each representing a dense-graded aggregate base (DGAB), asphalt-treated base (ATB) and permeable-asphalt-treated base (PATB) with external drainage. To illustrate the deliverables for flexible pavements, the authors have chosen an example from the SPS-1 site in the State of Louisiana (State Code 22).

The following tasks were assigned as a starting point to assist students in satisfying the assignment objectives:

TASK 1: Selection of Sections from SPS-1 Sites

Each SPS-1 site consists of 12 sections, with varying asphalt concrete (AC) thickness, base thickness, and base type. The last four sections are provided with some drainage to study drainage impact on pavement performance. All the 12 SPS-1 sections for the State of Louisiana were examined for the following characteristics:

  • The pavement cross-section, including the subgrade.
  • The type of base: whether granular, cement treated, or asphalt treated.
  • The most important aspect of this data inspection was to observe the amount and quality of the performance data available in LTPP.

Three sections were selected for this example, as shown in table 3.

Table 3. The LTPP section report


Section No. Layer Thicknesses (inches) Drainage Type
AC Layers: 1.4+8.1 = 9.5
Crushed Gravel, Granular Base (GB): 11.Granular Subbase (GS): 12Fine-grained soil, lean inorganic clay
AC Layers: 2+2.8 +11 = 15.8
Granular Subbase (GS): 18
Fine-grained soil, lean inorganic clay
AC Layers: 1.3+5.9 +10.6+3.2 = 21
Granular Subbase (GS): 30
Fine-grained soil, lean inorganic clay
Blanket w/long drains

1 inch = 25.4 millimeters

An example of the layer cross section is illustrated in the screen capture in figure 14.

The pavement structure details for Section 22-0114

Figure 14. The pavement structure details for Section 22-0114

TASK 2: Data for Selected SPS-1 Sites

The relevant data for the selected sites in the assignment were:

  • Various performance measures (fatigue cracking, transverse cracking, rutting and IRI).
  • Environmental data (temperatures, etc.).
  • Inventory data (layer thicknesses, base type, and drainage, etc.).
  • Traffic data with time (axle load spectrum for various axle types, etc.).

During the search for traffic data for SPS-1 sites in Louisiana, no traffic data were found for enough number of years to ascertain the growth rate and cumulative ESALs and axle load repetitions. Therefore, traffic data available for the GPS sections in Louisiana from 1991 to 1993 were used. The load spectra for single, tandem, and tridem, axle loads for the selected sites were extracted and used in the analysis to calculate ESALs. Figure 15 shows an example of load spectra distribution for tandem axles.

Tandem axle load spectrum

Figure 15. Tandem axle load spectrum

Because actual KESAL data were available for more years (1991-1996) in the monitoring data for the same GPS section, these data were used to ascertain the traffic growth rate. A growth rate of 9 percent was assumed based on the past trend of the traffic data.

Figure 16 shows the actual monitored trend of the KESAL on this road section for the past 6 years and predicted ESALs for future years based on 9 percent growth rate. The details of ESALs and growth rate calculation are not provided in this paper. Because the three sections are adjacent to each other, the same traffic is assumed for all of them. From the given traffic data, it was found that the sections have sustained about 3.5 million ESALs between 1991 and 2002.

Actual and predicted ESALs

Figure 16: Actual and predicted ESALs

TASK 3: Pavement Performance and Response for Selected SPS-1 Sites

This task shows the actual versus predicted pavement performance for the three selected sections, which have different characteristics but are subjected to the same environmental and loading conditions. The following sections summarize the analysis conducted.

Material Characterization

In situ layer moduli for different layers were backcalculated by using the FWD deflection data for each section. Three representative deflection basins (one for each section) were selected. The new version of MICHBACK (MFPDS) software was used for the backcalculation. The students also investigated the presence of a stiff layer.

The backcalculation of the layer moduli is very sensitive to the presence of a stiff layer below the roadbed. A simple equation based on Boussinesq’s equation for a point load was used to estimate the modulus from the surface deflections.

E = P * (1 - µ2)/[p * r * do(r)]

where P is the applied load and do (r) is the surface deflection at distance r from the center of the load.

The above equation was used to calculate the moduli for the various deflections as a check on the linearity of the subgrade. The results are shown in figure 17. Based on this plot, it was concluded that there is no stiff layer or ground water table close to the surface of the pavement.

The average surface moduli plot with depth for three selected sections

Figure 17. The average surface moduli plot with depth for three selected sections

Various AC layers (surface course, binder course, and ATB course) and granular materials (base and subbase) were combined to eliminate complications with the backcalculation. The summary of the backcalculated layer moduli for different sections is given in table 4 below. Details of the results and the deflection profile for each section are not shown in this paper.

Table 4. Summary results for material properties based on backcalculation, September, 1998


Section Number Backcalculated Moduli, psi
Asphalt Layers Granular Material Layers Subgrade
315,321 46,658 20,562
841,822 116,570 28,100
629,161 68,053 31,880

1 psi = 145 MPa

Pavement Response

The layer thickness data along with the backcalculated moduli of the various layers were used for the analysis of the layered system for the selected section using the KENLAYER computer program. The summary results are presented in table 5 below:

Table 5. Summary of the pavement response


Section Number Pavement Response
Deflection1(mils) Tensile Strain2(microns) Vertical Strain3(microns) Vertical Stress4(psi)
9.10 113.0 83.0 2.210
3.75 23.6 28.7 1.210
3.50 20.5 23.0 0.764

1 = Surface deflection. 1 mils = .001 inch
2 = Tensile strain at the bottom of asphalt layer. 1 psi = 145 MPa
3 = Vertical strain at the top of subgrade.
4 = Vertical stress at the top of subgrade.

The analysis was based on a dual wheel load of 4086 kg (9000 pounds); the critical response was calculated at the center of the tire, edge of the tire, and between the wheels. The maximum response was found between the wheels, which were subsequently used in the performance models. Given the low stress levels, a linear analysis was used to calculate the pavement response. The seasons were considered in the analysis were fall (August, September, October; 3 months); winter (November-March; 5 months); and spring and summer (April-July; 4 months).

The seasonal analysis was carried out by assuming various material properties and average ESALs in a particular season.

Pavement Performance

Three performance measures were analyzed:

  • Fatigue Cracking. Figure 18 shows the fatigue cracking observed in the field since the opening of these sections (from 1991-2000). Seven models were used to calculate the allowable number of ESALs (Nf) for the selected sections.

 Example of observed and predicted fatigue cracking

Figure 18. Example of observed and predicted fatigue cracking

All models predicted a sufficient remaining life for fatigue (n/N ‹0.01), except for the Belgian Road Research Center (BRRC) model for section 22-0114-1. Hence, the prediction by the majority of these models was deemed as representative of the actual field performance data.

  • Rutting in the wheel path. Rutting can be defined as the permanent deformation in the wheel path in transverse plane along the direction of the traffic. Rutting is a load-associated distress and can be caused in the subgrade (wide rut channel), base, or subbase layers and asphalt layers only (narrow rut channel). The field performance data for the selected section show some signs of rut, as shown in figure 19.

Example of observed and predicted rutting

Figure 19. Example of observed and predicted rutting

All the above models predicted a low rutting damage (n/N). The results from the MSU rut models are shown in figure 20.

Predicted rut depth for section 116

1 inch = 25.4 millimeters

Figure 20. Predicted rut depth for section 116

  • Transverse cracking. No transverse cracking was observed.
TASK 4: Engineering Discussion and Summary of Findings

Most of the analysis and evaluation presented in tasks 1 to 3 can be summarized as follows:

  • Three sections from LTPP SPS-1 sites for Louisiana were selected based on the various base types and drainage characteristics.
  • Various critical distresses for these selected sections along with the inventory, material, traffic, and environmental data were extracted from the LTPP DataPave 3.0.
  • If any missing or insufficient data were found in SPS-1 sites, then reasonable data were extracted for GPS sites within the same state in the close vicinity of these SPS-1 sites. In the worst case, i.e., no required data available, reasonable data were assumed.
  • The actual critical distresses extracted were plotted against the predicted distresses. It was found that the predicted distress levels agreed reasonably with the actual levels. However, there were some discrepancies; these can be attributed to the empirical nature of these models and data assumptions.
  • From the traffic analysis (load spectrum), it was found that single and tandem axles load repetitions occupy about 40 percent and 59 percent shares, whereas tridem axles only have 1-2 percent of the share. This trend is consistent over time.
  • The fatigue analysis for all sections shows that there should be no sign of this distress for the next 5-10 years.
  • The fatigue life for the section with ATB and drainage was found to be infinite.
  • The only load-associated distress observed in the selected pavement sections was rutting; the MSU rut model seems to predict this distress with reasonable accuracy considering overall rutting in all pavement layers.
  • All subgrade-strain based models were predicting a high number of repetitions to rut failure; therefore, it can be assumed that the rutting in these pavement sections pertains to permanent deformations within the pavement structure.
  • The analysis showed that a simple analysis with appropriate material properties and traffic estimates could be used to give reasonable predictions of the performance of new flexible pavement structures.


In the author’s opinion, the use of DataPave 3.0 as a source of “real” pavement data has considerably enhanced the quality of the pavement rehabilitation, design, and analysis courses. The initial offerings proved to be a challenge both for the instructors and for the students, because the learning curve is rather steep. As instructors, the authors had to commit considerable time to prepare the project statements, hold tutorials, and respond to questions on the use of the database. The hope is that the time commitment will diminish after multiple offerings as the instructors become more comfortable with the database. It is hoped that the LTPP database, and more specifically the DataPave 3.0 (and subsequent future versions), will be incorporated into the other pavement management and material courses at Michigan State University.


Buch, N., Baladi, G.Y., Harichandran, R.S., Park D.Y., and Kim, H. (1999). Calibration of MICHPAVE'S Rut and Fatigue Distress Models and Development of an Overlay Design Procedure in MICHBACK, Final Report. Michigan Department of Transportation, Project No. 61-9445.

ERES Consultants (2002) ISLAB 2000®, Finite Element Analysis Program for Rigid and Composite Pavements, Champaign, IL.

Hall, K., and Correa, M. (1999). “Estimation of Present Serviceability Index from International Roughness Index,” Paper No. 991508 presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC.

Huang, Y.H. (1993). Pavement Analysis and Design. Prentice Hall, Englewood Cliffs, NJ.

Techniques for Pavement Rehabilitation (Reference Manual). (1998), (FHWA-HI-98-033), U.S. Department of Transportation, Federal Highway Administration, Washington, DC.


The paper presents results obtained by student groups and does not represent the opinions of the authors or Michigan State University. The sole purpose of this paper is to demonstrate the use of DataPave 3.0; it does not constitute a standard or specification.

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