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Publication Number: FHWA-HRT-06-121
Date: November 2006

Long-Term Pavement Performance (LTPP) Data Analysis Support: National Pooled Fund Study Tpf-5(013)

Chapter 11. Application to Pavement Management

The models developed for this project can be used as a pavement management tool. Most PMSs in North America collect pavement condition data in a manner similar to that used in the LTPP program. The LTPP program collects pavement distress in more detail than most agencies do for pavement management purposes, but most agencies follow the same basic surface distress categories. Fatigue cracking, longitudinal cracking, and transverse cracking data are collected for flexible pavements and faulting, transverse, and longitudinal cracking are recorded for rigid pavements. The LTPP method also uses three severity levels that are similar to those used in PMSs. In some cases the extent conventions need to be modified, but that is fairly easily accomplished. For example, fatigue cracking is collected in square meters for LTPP purposes, but most SHAs measure the percentage of area or length of the fatigue cracking in the wheelpaths for use in their PMS. This is easily rectified because the dimensions of the LTPP test section are known, and fatigue cracking is assumed as occurring in the wheelpaths, so quantities can be converted to percentages in terms of wheelpath area.

In consideration of PMS application, several of the models developed in this study were based on LTPP measurements that were converted to be consistent with PMS extant conventions. In turn, these values were extended to standard PMS deduct values so that the measured distress could be represented in terms of deduct values or a pavement condition index (i.e., 100 less the sum of deduct values for a given test section). This approach provided for the characterization of the various levels of distress severity that is consistent with common PMS practices. A complete discussion on the development of deduct value development can be found in chapter 3 of this report.

One of the more difficult exercises in implementing PMS applications is the development of a family of curves that represent the standard deterioration trends unique to an agency’s roadways and environment. Few SHAs have been able to measure pavement conditions in a consistent manner. Relating one set of measurements over a specific area to another set of measurements over that same area from year to year (as was done at the LTPP sites) has proven to be a difficult task. To overcome this, some SHAs have relied on models developed based on test sites that could be monitored with time or expert opinion, while others have used models developed based on other SHA data.

Pavement performance models are used to predict future deterioration of a highway network’s pavements based on the last set of pavement condition survey data. The models (or condition data) have a large effect on the information the PMS provides for future construction program decisions, and also influence the future pavement condition trends as well as the funding needs for that agency.

SHAs with little historical pavement condition information or limited amounts of composite indices can use the models from this study to develop a family of curves to help implement a PMS or convert a PMS from one with only composite indices available. Not all agencies have maintained their raw pavement condition survey data and only have files with the computed index for prior years. Because the location referencing system can change from year to year or the posting of those locations can change, there is often no attempt to correlate the pavement condition data taken at a specific location with future or past data taken at that same location.

Unfortunately, most SHAs use different pavement distress indices based primarily on the specific pavement management system they have adopted, and there is a wide range of systems in use throughout the United States. Deduct curves developed for South Dakota,(10) which are now in use by several other SHAs, were used to develop pavement performance trends for this study. The extent of pavement distress (i.e., area or length) for each severity level was accumulated in terms of the sum of the pavement distress deduct values. As noted in the chapter on database development, the deduct values were determined using the expressions shown in equations 7 through 10 for each severity level.

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These equations are represented in figure 79.

Figure 79. Graph. Individual distress deduct curves.

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The individual distress indices were developed so those deduct values represented desired action times for most SHAs. For example, a fatigue distress deduct accumulation level of about 35 represents about 10 to 15 percent medium severity cracking over the length or area of the wheelpath assuming each wheelpath is 1 m (3 ft) wide. For transverse cracking, an accumulation level of about 50 represents a sufficient length of medium severity transverse cracking to indicate a crack spacing of approximately 9 m (30 ft) between transverse cracks. These values represent damage levels that affect or initiate repair or rehabilitation project timing for most SHAs.(10)

Figure 79 can be used by SHAs to gain an understanding for the distress magnitudes represented by deduct values. The medium severity deduct curve can be used to get the average magnitude required to produce that value. This process can also be used to make comparisons between SHA-specific damage indices and those used in this study.

The pavement performance curves for any specific distress can be used to develop general deterioration trends to forecast future pavement conditions from current condition measurements.

As an example, the fatigue deduct values shown in figure 80 (from the environmental sensitivity study) for the no-freeze wet region were converted to a pavement condition index by subtracting the accumulated deduct values from 100. The resulting pavement deterioration trend for fatigue cracking for a pavement with the characteristics outlined in table 19 can be found in figure 81.

Figure 80. Graph. Example of fatigue cracking trends for different environments.

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Figure 81. Chart. Fatigue cracking index trend for environmental case wet no-freeze.

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This trend line represents the average trend found for a specific set of conditions used in the sensitivity study for this project. SHAs can determine a set of input parameters that represents the regional-specific conditions (environment, traffic, and pavement structure) and develop a similar deterioration trend line using the performance models from this project. A series of these trend lines can be developed for the range of conditions expected across a region and used as a tool to predict future pavement conditions. As condition data are collected on pavement structures, the average deterioration line can be updated or shifted to match the most recently collected data. For example, a highway section that matches the criteria used to develop figure 81 surveyed at year 10 exhibits a fatigue cracking index of 75. The trend in figure 81 would be shifted to the right so that it predicts a value of 75 at year 10 without altering the slope. This would result in the life of the particular highway section being extended by approximately 2 years based on the condition data collected at year 10. This is shown in figure 82.

Figure 82. Chart. Example of shifting trend line to fit index for a given location.

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Where an agency used different pavement condition indices, it would need to develop the area of medium severity fatigue cracking using equations 7 through 10 for medium pavement deterioration trend lines. The amount of medium-severity fatigue-cracking values would then be used to convert to the pavement condition deduct values the agency uses.

Where an agency uses a composite index that combines several of the distresses modeled individually in this study, the process would be more complicated. The agency would probably need to use each pavement deterioration curve to predict the future distress level for each distress and then combine the distress levels according to their combined equation into a composite index for set program dates rather than developing a trend line for the composite index.

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