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Publication Number:  FHWA-HRT-17-090    Date:  January 2018
Publication Number: FHWA-HRT-17-090
Date: January 2018


Guidelines for Informing Decisionmaking to Affect Pavement Performance Measures: Final Report

CHAPTER 4. Treatments Affecting Performance Measures

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:

  1. Assign data into metric condition combinations (G-F-F, G-G-P, etc.).

  2. Determine effect of M&R treatment by comparing overall condition of last survey prior to treatment to overall condition for first survey after treatment as no change, worse, or improved.

  3. Calculate percentage that M&R treatment improves condition for each metric condition combination.

  4. Evaluate M&R treatments that show improvement while considering drivers of metric grouping.

  5. Develop list of potential M&R treatments.

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.

This figure presents a flowchart. The flowchart consists of five boxes. The first four boxes read from top to bottom as follows: “Metric combinations,” “Effect of M&R treatments,” “Probability of condition improvement,” and “Treatment and drivers evaluation.” Finally, the four boxes flow into a double box at the bottom labeled “Develop list of potential M&R treatments.”

Source: FHWA.
Figure 9. Flowchart. Development of treatments that affect performance measures.

This figure presents a flowchart. The flowchart consists of five boxes. The flowchart begins at the top with a box labeled “Pavement segments assigned G–P–P/F–P–P.” This box flows down to a box labeled “Differentiate improvements.” This box then flows down to two parallel boxes labeled “Poor to fair” and “Poor to good.” These two boxes then flow down into a double box labeled “Treatment effect understanding.”

Source: FHWA.
Figure 10. Flowchart. Evaluating poor condition improvement type.


Temporal Analysis Considerations

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:

  1. Select pavement segments with at least three surveys between construction events.

  2. Assign metric conditions and overall condition.

  3. Classify temporal category according to the trend of condition as follows:

    • Good to fair.
    • Fair to poor.
    • Good to poor.
    • Fair to good.
    • Poor to good.
    • Good—no change.
    • Fair—no change.
    • Poor—no change.

  4. Calculate the time to change condition as the time between the last survey in one condition and the first survey in a different condition or the time that the condition remained constant as the time between the last survey and the first survey after the previous construction event.

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.

This figure presents a graph. The y-axis is labeled “Survey Condition” and ranges from “Good” at the bottom to “Fair” in the middle and “Poor” at the top. The x-axis is labeled “Time (years)” and ranges from 0 to 15. The legend shows three different types of measurements labeled “Roughness,” “Rutting,” and “Cracking,” which are represented by a circle, diamond, and triangle, respectively. The measurement data points are plotted and begin in the Good region and increase following a linear trend over time. At time “t,” the first grouping of each of the three measurements has a dashed-line rectangle around it and a label that states “Within 1 year.” The fifth grouping has a dashed–dotted-line rectangle around it that crosses a horizontal dashed line at Fair. An arrow spanning the time from the first rectangle grouping to the second rectangle grouping is labeled “Time to condition change.” The sixth and final grouping occurs at 15 years. An arrow spanning the time from the first rectangle grouping to this last grouping is labeled “Time span considered.” A final arrow spanning the time from the beginning (time equals 0 years) until the time of the last grouping (time equals 15 years) is labeled “Time since construction.”

Source: FHWA.
Figure 11. Graph. Temporal groupings.

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.

Table 26. Temporal analysis by treatment and post-treatment trend.
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.

Potential M&R Treatments to Improve Overall Condition

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.

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