U.S. Department of Transportation
Federal Highway Administration
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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
| REPORT |
| This report is an archived publication and may contain dated technical, contact, and link information |
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| Publication Number: FHWA-HRT-17-090 Date: January 2018 |
Publication Number: FHWA-HRT-17-090 Date: January 2018 |
The previous chapter explained how to determine the effects of M&R treatments at the metric level and on the overall condition. This chapter presents how to combine the findings from chapters 2 and 3 of these guidelines—the performance measure drivers and the effects of M&R treatments—to develop a list of potential M&R treatments to improve overall pavement condition (and hence performance measures) from poor to fair, fair to good, or poor to good.
Chapter 3 of these guidelines presented the effects of M&R treatments on overall condition and showed that many treatments do not affect the overall condition. However, it is important to consider the metric condition combinations that make up the overall condition. The development of the performance measure drivers considered the makeup of the overall condition and emphasized the borderline condition. Figure 9 presents a flowchart for the major components of the process presented in this chapter. The steps in figure 9 include the following:
Further understanding regarding how M&R treatments affect the overall condition is necessary, especially for those cases where the condition prior to treatment is poor (e.g., G-P-P/F-P-P). Since the overall condition is poor, an improvement can result in either fair or good condition. This is a critical component in forecasting (i.e., condition prediction) when considering the performance measures. Figure 10 presents the flowchart for evaluating poor condition improvement.
The process illustrated in the five steps for developing treatments that affect the performance measures combines the effect of M&R treatments and the driver analysis by assessing how the M&R treatment affects the overall condition based on the original metric condition combination and driver. For the process presented in this section, LTPP pavement sections that were evaluated in chapter 3 of these guidelines for the effect of M&R treatments were grouped based on the metric groupings. The trend of the overall condition was characterized as no change, worse, or improved.
The drivers of each grouping as shown in table 11 are as follows:
The final steps are to develop a list of potential M&R treatments based on the metric grouping, the driver of the grouping, and the M&R treatments that improve the condition of that grouping. Based on the values presented in table 22, crack seal, joint seal, and patching should be removed from consideration for improving the overall condition for AC pavements.
Note: This should not be interpreted as stating that these treatments should not be used, but rather that they do not immediately improve overall pavement condition. There is a benefit to using treatments to improve a metric condition or prevent or slow deterioration, such as pavement preservation treatments.
Grinding is also removed due to the small sample size and the inability to draw accurate conclusions. The remaining treatments are mill and overlay, overlay, and surface treatment. Surface treatments show the most improvement for the G-G-F and G-G-P groupings. They are most effective at improving cracking condition (see table 16). For the G-G-P grouping, cracking is the driver. Although rutting is the driver of the G-G-F grouping, surface treatments did improve rutting condition 13 percent of the time based on table 16. However, for the pavement segments where surface treatments improved the overall condition of the G-G-F grouping, cracking was more likely the metric in fair condition. As stated previously, although rutting is the performance measure driver, there are occurrences when cracking is the metric in fair condition. It is these occurrences mostly where the surface treatments improved the overall condition. As shown in table 22, mill and overlay improves the condition between 79 and 93 percent of the time, while overlays improve the condition between 69 and 85 percent of the time with the greatest effectiveness on the G-F-P grouping, where cracking and rutting are the drivers.
In the case that the overall condition is poor due to two metrics being poor, the drivers according to table 11 are cracking and rutting. Table 23 presents the effect of M&R treatments on the G-P-P and F-P-P groupings for the treatments that improve condition. It is important to consider how these treatments affect the overall condition. The analysis investigated the improvement in condition and classified the improvement as poor to fair or poor to good. The results of the analysis show that mill and overlay and overlay are more likely to improve the overall condition from poor to good, whereas surface treatments and patching are more likely to improve the condition from poor to fair.
| M&R Treatment | No Change (%) | P-F (%) | P-G (%) |
|---|---|---|---|
| Mill and overlay | 5 | 37 | 58 |
| Overlay | 0 | 43 | 57 |
| Patch | 0 | 100 | 0 |
| Surface | 0 | 100 | 0 |
The drivers of each grouping according to table 11 are as follows:
The final steps are to develop a list of potential M&R treatments based on the metric grouping, the driver of the grouping, and the M&R treatments that improve the condition of that grouping. Based on the values in table 22, grinding emerges as the most effective treatment. Grinding improves both the G-G-F grouping where roughness is the driver and the G-F-P grouping where roughness and cracking are the drivers.
| M&R Treatment | G-F (%) |
|---|---|
| Patch | 0 |
| PCC overlay | 100 |
Note: There were not sufficient data to perform the “poor” analysis for the LTPP JPCC or CRCP datasets.
Many agencies consider the expected lives for M&R treatments within their PMS. However, the expected lives are likely not tied to the performance measures required by the Final Rule. The temporal analysis documented in the research report that led to these guidelines showed that the performance measures are stable over time.(4) Although these guidelines have shown that M&R treatments can affect the overall condition of a pavement, it is important that repairs are strategic in nature to have the desired effect on the performance measures. Agencies should consider the expected life of treatments with respect to the metric groupings and overall condition ratings. The analysis should consider the pre-treatment condition of the pavement, the treatment type, and the type of change in condition. The data should be analyzed from the first survey after treatment until the time where the overall condition changes or noted if the overall condition remains constant. The following steps should be followed to conduct the temporal analysis:
Note: Ideally, the time to change would be calculated using surveys done immediately after construction and immediately after change of condition from good to fair or fair to poor. However, the actual calculated time to change will be dependent on the actual timing of the surveys available.
Figure 11 depicts the various timespans referenced in this analysis and the meaning of each. For analysis of the LTPP data, a survey grouping was only formed provided each of the individual metric measurements was taken within 1 year of the others. As a result, many times there were surveys between the time of last construction (as designated by time equal to zero in figure 11) and the first grouping considered in the analysis. This often created a time lag between the time of construction and the time the first grouping was considered. Figure 11 shows the time the first grouping was considered as time “t,” which changes for each pavement section. This time between construction and first survey grouping considered in this analysis was on average 1.5 years for AC pavements, which is a conservative estimate for JPCC pavements and CRCPs as documented in the research report that led to these guidelines.(4) Although this time was calculated by considering only sections that had a first grouping in good condition, this time between last construction and first grouping should not be added to the timespan considered, since the actual condition is not known but was assumed to be good over the timespan. This results in the timespans calculated under this analysis being conservative. For State DOTs that collect all data metrics concurrently using a single data collection vehicle, it is less likely for there to be measurements taken between the date of last construction and the first grouping, because there should not be a difference in time of measurement for the various metrics (i.e., the first survey measurements are available for all metrics). The timespan between last construction and first survey grouping will be dependent on State DOT data collection cycles and practices.
The timespan considered under this analysis was from the time of the first grouping until the time of the last grouping, which in figure 11 is shown to be from time “t” to 15 years. The time to change was calculated as the time between the first grouping and the first grouping where condition changes. For example, in figure 11, the first grouping (at time “t”) is in good condition, while the fifth grouping (prior to grouping at 15 years) is the first grouping to become fair, since the roughness survey is beyond the fair threshold. Therefore, the time between the first grouping and the fifth grouping is considered the time to change.
For example, an AC pavement section in fair condition is treated with a surface treatment. The overall condition after treatment is good. The time until the condition changes to fair is the time it takes to change from good back to fair condition. Similarly, if the overall condition remains fair after the treatment, this is also important, as it may have been the objective of the treatment, such as a preservation slowing the deterioration. Table 26 presents the average time to change from good to fair or that remained fair for AC pavement sections that received treatments when the overall condition was fair prior to treatment. The table shows that the average time in each category (e.g., good to fair and fair—no change) for surface treatments is less than both overlay and mill and overlay. Temporal considerations and expected lives of treatments are a key factor in a PMS.
| Treatment Type | Good to Fair (Years) | Remained Fair (Years) |
|---|---|---|
| Mill and overlay | 4.6 | 8.5 |
| Overlay | 4.4 | 10.0 |
| Surface | 3.1 | 4.9 |
State DOTs should conduct similar temporal analyses for the treatments and data maintained in their PMS. The temporal analyses produce the average time a treatment maintains a condition or the time until the condition changes after a treatment. These findings can be a key component of PMS forecasting and, ultimately, decisionmaking.
Based on the assessments presented in this chapter, the following presents a list of M&R treatments that improved the overall condition for the LTPP sections used in this study:
Agencies should follow the procedures provided in these guidelines to develop, using their data, a similar list of treatments that improve the overall condition.
