Pavement Performance Measures and Forecasting and The Effects of Maintenance and Rehabilitation Strategy on Treatment Effectiveness (Revised)

CHAPTER 2. LITERATURE REVIEW

The research team conducted an extensive literature review in support of this study. The review focused on various topics, including the following:

• Pavement distress severity levels.
  
  
• Threshold values.
  
  
• Definitions and methodologies used to determine good, fair, and poor pavement conditions.
  
  
• Effectiveness of various pavement maintenance and rehabilitation treatments and their predicted and measured performance.
  
  
• Selection of pavement preservation and rehabilitation strategies and their impacts on the pavement service life.
  
  
• Advantages and shortcomings of pavement treatment benefits, including the RSL concept.
  
  
• LTPP experimental design and the in-place pavement sections.
  
  
• Research findings from previous studies of the LTPP data.

PAVEMENT DISTRESS SEVERITY LEVELS

The LTPP and the majority of State transportation department pavement distress data are collected based on three severity levels: low, medium, and high. The distress severity rating can be problematic because it is a function of the judgment of the surveyor who is observing the pavement or, in the case of many State transportation departments, the surveyor who is reviewing and digitizing the electronic pavement surface images. Such judgment is a function of the degree of training and experience of the surveyors. Further, the same pavement segment may not be reviewed by the same surveyor each year or each data collection cycle. In addition, the crack severity level is a function of the crack opening, which is a function of the pavement temperature at the time of data collection. Thus, a crack may be labeled “high severity” in one year and medium severity the next year or vice versa. Figure 1 and figure 2 depict an example of the time-series data for each transverse crack severity level for LTPP test section A330 of the SPS-3 experiment in California.

1 ft = 0.305 m.

Figure 1. Graph. Time-series transverse cracking data for each severity level and the sum of all severity levels for LTTP SPS-3 test section A330 in California.

1 ft = 0.305 m.

Figure 2. Graph. Cumulative time-series transverse cracking data showing individual transverse crack severity level and the sum of all severity levels for LTTP SPS-3 test section A330 in California.

The data in the two figures indicate the following:

• The length of transverse cracks for any given severity level changes from one year to the next without the application of any pavement treatment. To illustrate, the length of the low severity transverse crack in figure 1 is about 197 ft (60 m) in year 1, 82 ft (25 m) in year 5, 49 ft (15 m) in year 7, more than 328 ft (100 m) in year 11, and 197 ft (60 m) in year 13. The medium severity crack length is approximately 33 ft (10 m) in year 1,131 ft (40 m) in year 7, only about 16 ft (5 m) in year 11, and about 82 ft (25 m) in year 13. Finally, the length of the high severity transverse cracks is about 230 ft (70 m) in year 6, 164 ft (50 m) in year 8, 427 ft (130 m) in year 11, and 377 ft (115 m) in year 13.
  
  
• The variability of the crack lengths of the three severity levels could be attributed to several reasons, the three most important of which are the following:
  
  
  • The pavement temperature at the time of data collection influences the data. Higher temperature causes the crack width to decrease, resulting in an observed change in severity level. This problem cannot be addressed unless the pavement temperature is measured during the survey, and an accurate temperature-dependent crack width model is developed. Note that the LTPP surveyors do collect pavement temperature data during a survey while most State transportation departments collect temperature data only on a limited basis.
    
    
  • The pavement surveyor judges and labels some cracks as low severity in one survey year and medium or high severity in subsequent survey year. This inconsistency could be addressed through computerized crack-rating quality control and/or enhanced observer training.
    
    
  • The high variability of the individual severity levels does not allow accurate modeling of the crack propagation over time. In fact, the data indicate that the medium and high-severity transverse crack lengths decrease and then increase over time without any pavement treatment. A previous study sponsored by the FHWA expressed the pavement cracking data as the sum of the three severity levels.⁽²⁾ This yielded much less data variability, as evidenced by the exponential model of the total transverse crack length shown in figure 1.

The crack severity level data could be used to roughly estimate the amount of work needed to preserve the pavement section. For example, cracks in the medium- and high-severity levels need to be sealed or patched. Low-severity cracks are typically not sealed or patched. For rigid pavements, low-severity transverse cracks may be subjected to dowel bar retrofit, while medium-and high-severity cracks typically are not.⁽³⁾ Similar patterns can be found in the State transportation departments’ cracking data as shown in figure 3 and figure 4 for locations along a portion of Highway 24 in Colorado.

1 ft = 0.305 m.

Figure 3. Graph. Time-series transverse cracking data for each severity level and the sum of all severity levels, Highway 24, direction 2, BMP 329.9, in Colorado.

1 ft = 0.305 m.

Figure 4. Graph. Cumulative time-series transverse cracking data showing individual transverse crack severity level and the sum of all severity levels, Highway 24, direction 2, BMP 329.9, in Colorado.

ENGINEERING THRESHOLDS (CRITERIA)

Some State transportation departments express pavement conditions and distresses using one or more of the following methods (see figure 5 and figure 6):^(4,5)

• A descriptive scale, such as very good, good, fair, poor, and very poor.
  
  
• A distress index based on a continuous rating scale (i.e., 0 to 10 or 0 to 100). One end of the scale defines failed pavement, and the other end defines excellent pavement condition and/or no distress, such as in the new pavement shown in figure 5. Some State transportation departments use the rating scale to calculate one distress index for each type of distress (i.e., individual distress indices), while others use a composite pavement index. A composite index is typically based on several types of distress and/or condition. Examples of composite pavement indices include Pavement Condition Index (PCI), Pavement Quality Index (PQI), overall pavement index, and so forth.
  
  
• Along the rating scale, one or more threshold values are typically established to flag pavement sections for possible treatment actions. One threshold value could be based on the need for maintenance, another on the need of preservation action, and a third could be based on rehabilitation. Depending on the functionality of the threshold value (maintenance, preservation, or rehabilitation), a distress index value below the established threshold value indicates the need to maintain, preserve, or rehabilitate the pavement section in question. The rehabilitation threshold value typically separates acceptable from nonacceptable pavement conditions.

1 mi = 1.61 km.
1 ft = 0.305 m.

Figure 5. Chart. Rating and descriptive scales and distress points.

Figure 6. Graph. Descriptive rating scale for pavement condition.

It is important to note that if the threshold value is established based on engineering criteria, the pavement condition rating will be such that the relative condition of the pavement segment is constant for a given condition. The engineering criterion should be selected based on the experience of the transportation department and should address the extent of the condition or distress at which the pavement section in question is deemed in need of repair (maintenance, preservation, or rehabilitation) within the constraints of the department. An example of engineering criterion for transverse cracking could be 600 ft (183 m) of cracking or 50 transverse cracks (crack spacing of about 10.5 ft (3.2 m) along a 0.1-mi (0.16-km)-long asphalt pavement segment). Based on the engineering criterion, distress points can be assigned to each occurrence of the distress (each transverse crack) and the rating scale threshold value. To illustrate, consider the continuous rating scale of 0 to 100 (100 indicates no transverse cracks) and its threshold value of 60 points as shown in figure 5. An engineering criterion of 50 transverse cracks per 0.1mi (0.16 km) implies that the asphalt pavement score is 60 (it loses 40 distress points) when the pavement segment accumulates 50 transverse cracks. Based on a linear accumulation of distress points, each transverse crack is worth 0.8 distress points.⁽⁴⁾ If the agency decided to change the threshold value from 40 to 50 but to maintain the engineering criterion of 50transverse cracks, then 50cracks would cause the pavement section to lose 50 distress points and each crack would be worth 1 distress point. Stated differently, the engineering criteria for establishing the threshold value should be based on the extent of the distress rather than a number on the rating scale.

Finally, the engineering criteria express the conditions of the pavement and could be based on the user or the agency. Examples of roadway user-based criteria are ride quality (IRI) and rut depth. Examples of agency-based criteria are cracking and faulting. One other factor to note is that the engineering criteria for certain distress or condition types could be global or could be established based on pavement class, traffic volume, regional needs, and so forth. Nevertheless, the methods used to develop the engineering criteria should be well documented, and the criteria should be studied and calibrated as more pavement condition and distress data become available.

Many State transportation departments, such as the LADOTD, the CDOT, the Michigan Department of Transportation, and the WSDOT, have developed engineering criteria for each distress type and severity level. Examples of such criteria for alligator cracking and the associated deduct points are listed in table 1.⁽⁶⁾

Table 1. Engineering criteria and deduct points for alligator cracking.⁽⁶⁾

PAVEMENT DISTRESS AND CONDITION INDICES

Pavement distress or condition indices are often based on one or more condition or distress types. For example, the Alligator Cracking Index (an individual index) is based on the severity levels (low, medium, and high) and the extent of the alligator cracks, whereas a combined pavement distress or condition index (such as PCI) consists of two or more condition or distress types. The combined index expresses the sum of the distress points assigned to each distress or condition type and severity level divided by the number of pavement segments (see figure 7). Hence, a combined pavement distress index expresses the average pavement condition and not the actual condition based on individual distress types.^(4,7)

Figure 7. Equation. DI.

Where:

DI = Distress index.
Σ DP = Sum of the distress points along the project.
N = Number of 0.1-mi (0.16-km)-long segments along the project (N = L/0.1).
L = Project length in mi (km).

Finally, the distress points or the pavement condition indices do not express the true nature of the pavement conditions. For example, immediately after construction, the cumulative distress points of a pavement project subjected to a 2-inch (51-mm) asphalt overlay are exactly the same as the cumulative distress points for another project subjected to a 6-inch (152-mm) asphalt overlay. The pavement surface conditions of both projects are free of distresses. Stated differently, neither the distress points nor the condition indices express the design life of the overlay or the impact of the type of pavement preservation or rehabilitation on the pavement service life. Further, the differences between the distress points and the pavement distress index before and after treatment cannot and should not be used to express the benefits of the applied pavement maintenance or rehabilitation treatments. Consider three pavement sections having the same distress points and distress index that were subjected to 2-, 4-, and 6-inch (51-, 102-, and 152‑mm) asphalt overlays, respectively. The differences in the distress points and distress index before and after treatment were exactly the same although the costs of the overlays were substantially different and so were their design service lives (DSLs) and future pavement performance. The DSL of the treatment and the pavement rate of deterioration must be accounted for in the calculation of the true benefits of the treatments.⁽²⁾

DESCRIPTIVE PAVEMENT CONDITIONS

Descriptive terms (such as good, fair, and poor) are also used to express the various categories of the pavement conditions. Although the terms hide important details, they are universal and easily communicated to legislators and the general public. The three terms are typically based on the pavement appearance and/or ride quality at the time of rating. Descriptive classifications of good, normal or fair, and poorly performing AC and portland cement concrete (PCC) pavements were previously addressed in four FHWA reports published in 1998, 1999, 2011, and 2012. (See references 8–11.) The shortcomings of the first two reports are that the descriptive terms are based on the last collected pavement condition data as shown in figure 8. Figure 9 depicts the actual time-series IRI data for three in-service pavement sections located in the State of Washington. Over the 10-year period, these sections were not subjected to any pavement treatment. Figure 9 clearly shows the following:

• Data along a 2.4-mi (3.9-km)-long pavement segment of road 2 indicate that the descriptive term changed from good to fair to poor in only 3 years. Thus, the descriptive terms do not accurately reflect the true pavement performance.
  
  
• Data along a 3.6-mi (5.8-km)-long pavement segment of road 3 changed performance descriptions over time from poor to fair (labeled normal in the reports) and then to good.
  
  
• Data along a 4.8-mi (7.7-km)-long pavement segment of road 1 show that the pavement description fluctuated between good and fair and then between fair and poor. Once again, such descriptive terms do not reflect the true in-service pavement performance over time.
  
  
• After construction, Roads 1 and 2 were considered good, and then at the elapsed time of 4years, the description of road 1 was fair while road 2 was poor. The pavement rate of deterioration was not reflected in the characterization.

The three descriptive terms could be improved to better express the pavement conditions if they were based on the pavement’s rates of deterioration.

1 inch/mi = 0.0158 m/km.

Figure 8. Graph. Pavement condition classification system.

1 inch/mi = 0.0158 m/km.

Figure 9. Graph. Shortcomings of the recommended classification system when dealing with real but good data (not the worst-case scenario).

On the other hand, the latter studies describe the terms good, fair, and poor and their potentially associated treatment categories as follows:⁽¹¹⁾

• Good: Pavement infrastructure that is free of significant defects and has a condition that does not adversely affect its performance. This level of condition typically requires only preventive maintenance activities.
  
  
• Fair: Pavement infrastructure that has isolated surface defects or functional deficiencies. This level of condition typically could be addressed through minor rehabilitation, such as overlays and patching of pavements that do not require full-depth structural improvements.
  
  
• Poor: Pavement infrastructure that is exhibiting advanced deterioration and conditions that affect structural capacity. This level of condition typically requires structural repair, rehabilitation, reconstruction, or replacement.

Once again, the significant issue with these types of definitions is that they do not consider the changes in conditions and distresses over time. The terms do convey the current conditions of a pavement well, but pavement health is not best demonstrated by a snapshot in time. The pavement conditions and distresses generally deteriorate with time, and the pavement health depends on the current conditions and the rates of deterioration over time. The specific terms could still serve their purpose, but the criteria used to assign the rating should be modified to account for the effects of time. Consideration of condition and rates of deterioration facilitates planning and pavement management, while condition alone has limited use as a tool for managing pavement unless the deterioration rate (curve) is estimated based on the available data.

PAVEMENT PERFORMANCE MODELING AND PREDICTION

The performance of a pavement segment is often illustrated by the progression of pavement condition or distress over time, as shown in figure 10. The level of performance at any given time is equivalent to the level of pavement condition or distress at that time compared with the threshold value. Therefore, the performance of a pavement segment over its service life is defined by the level of service over time or by the accumulation of damage over time.⁽¹²⁾

1 inch/mi = 0.0158 m/km.

Figure 10. Graph. A typical pavement performance curve.

Most State transportation departments collect pavement condition and distress data. Some use the data to observe the condition of the pavement, while others use the time-series pavement condition and distress data to predict future pavement conditions. The combination of both practices allows the development of current and future strategies for management of the pavement network.

Many State transportation departments have studied the effectiveness of various pavement treatments using historical pavement performance data. Based on the various studies, the minimum and maximum treatment service lives listed in table 2 were published in the various sources listed for each treatment type. These estimated averages are adequate to be used in the analysis at the network level. For project-level analysis, more accurate estimates are required. Such estimates could be based on predictions of past and future pavement conditions through the modeling of pavement condition and distress data before and after treatment to create pavement performance curves.

Table 2. Estimated and reported pavement treatment service life.

Several predictive pavement performance models have been developed to estimate the pavement performance curve based on parameters such as traffic, weather, and pavement type. These models include straight-line extrapolation, regression, polynomial constrained least squares, application of Shaped curves, use of probability distributions, and Markov chain models.⁽¹³⁾ One such example, for thin HMA overlays, is presented in figure 11. The β parameters were determined for different performance indicators (IRI, pavement condition rating (PCR), and rut depth) as well as different road types (interstate, noninterstate highway, and nonhighway).⁽¹⁴⁾

Figure 11. Equation. PI.

Where:

PI = Performance indicator for a pavement segment in a given year.
CAATT = Cumulative average annual daily truck traffic (in millions) predicted for the pavement segment from the time of treatment to the given year.
CAFDX = Cumulative annual freeze index (in thousands of degree-days) predicted from the time of treatment to the given year.
β₁, β₂, and β₃ = Statistical parameters.

The most common method for modeling pavement condition and distress data as a function of time is by ordinary least squares regression. It should be noted that a minimum of three time-series data points are required to model the nonlinear data. The method used to determine the parameters of the selected mathematical function (see figure 12 through figure 14) consists of minimizing the sum of squared errors. This method works when the data of the particular pavement segment indicate deterioration over time. If the method does not capture the progression of condition or distress over time, other models may be required.⁽¹⁵⁾

Figure 12. Equation. RD.

Figure 13. Equation. IRI

Figure 14. Equation. Crack.

Where:

RD = Rut depth.
α, β, γ, ω, k, θ, and μ = Regression parameters.
Crack = Crack length, area, or percent.
t = Elapsed time in years.

Such models can be used to estimate the time until a certain threshold value is reached. Threshold is the prespecified condition or distress level indicating unacceptable pavement condition or distress has been reached and that the pavement segment is in need of maintenance, preservation, or rehabilitation depending on the level at which the prespecified threshold value is set.

Another method of modeling pavement condition and distress data is the clusterwise regression procedure, which was introduced by Spath and later modified by others. (See references 15 and 30–35.) Clusterwise regression involves splitting the data into subgroups based on their characteristics and fitting separate models to each subgroup. The resulting pavement performance models could be more accurate because they model small subsets of data with similar trends. However, the resulting models are discrete (each model represents a certain time period).

PAVEMENT PRESERVATION BENEFITS

Most procedures for estimating pavement preservation benefits are based on the prediction of future pavement performance, comparison of the pavement performance before and after treatment, and immediate changes in the pavement conditions resulting from treatment. Although the commonality among all procedures is a prespecified threshold value, the actual value of the threshold varies from one procedure to another. Some procedures set one threshold value for each type of pavement distress and condition, while others use the combined distress index and an overall threshold value. Still others use one threshold value for maintenance, one for preservation, and one for rehabilitation and/or reconstruction. Further, some procedures use the term life to express the benefits. Such a term should not be taken separately from service life. For example, the term pavement design life used by the American Association of State Highway and Transportation Officials (AASHTO) Guide for Design of Pavement Structures, in reality, expresses the pavement DSL.⁽³⁶⁾ The pavement design itself is based on the following threshold values:

• The 1993 AASHTO Design Guide specifies a threshold value based on the Present Serviceability Index (PSI) of 2.5 (which is equivalent to an IRI of about 200inches/mi (3.16 m/km)).⁽³⁶⁾ The threshold value expresses the minimum ride quality standard. A pavement at or below a PSI of 2.5 is providing the user with substandard ride quality. The ride quality could be restored by many preservation and/or rehabilitation actions (such as thin or thick overlays). Stated differently, when the prespecified threshold is reached, it implies that the minimum standard is reached while the pavement is not in need of reconstruction. If the threshold value, on the other hand, is set at the reconstruction limit, then reconstruction is needed.
  
  
• The new AASHTO Guide for Mechanistic-Empirical Pavement Design of New and Rehabilitated Pavement Structures (referred to as the MEPDG) is based on one threshold value for ride quality (IRI), one for rut depth, one for faulting, and one for each type of cracking, similar to that listed in table 3.⁽³⁷⁾

Table 3. Threshold values that could be used in the AASHTO MEPDG.

Nevertheless, various procedures were developed for estimating treatment benefits. The following are some of these procedures:

• RSL: RSL is the estimated number of years from any given year (usually from the last condition survey year) to the date when the conditions or distresses of the pavement section reach a prespecified threshold value. It is very important to note that the prespecified threshold values could be set at the level when the pavement is in need of maintenance, at the pavement preservation level, or at the major rehabilitation or reconstruction level. It is strongly recommended that the prespecified threshold value be set by the roadway owner to support its pavement preservation system, such as those recommended by the AASHTO MEPDG or listed in table 3.⁽³⁷⁾ At these levels, a given pavement section requires major rehabilitation, and the RSL is zero. State transportation departments can also establish the RSLs or the ranges of RSL at which pavement preservation and pavement maintenance can be applied. The reason for this recommendation is that the cost of pavement maintenance, preservation, or rehabilitation at the specified ranges of RSLs could be included in the analyses. This recommendation can be illustrated as follows. Suppose the threshold value for longitudinal cracking is set at 1,056 ft/0.1 mi (200 m/0.1 km) as listed in table 3. When a pavement section reaches the threshold value, the RSL is 0 and the cost of preservation is much higher than the cost of an earlier preservation when the RSL is 5 years. Further, when the RSL is 10 years or more, the cost of preservation is much lower yet and it may be limited to the cost of the required maintenance treatment only. Thus, the transportation department could establish a continuous scale of costs versus RSLs such as that shown in figure 15.

  
  1 lane-mile = 1.61 lane-km.
  
  Figure 15. Graph. Relationship between RSL and cost of managing pavements.

   

  For each pavement section, the following steps are required for calculating the RSL:

  • Download from the database the pavement surface age and all consecutive pavement condition and distress data points collected over a time period where no treatment was applied.
    
    
  • Use the condition and distress data points and the corresponding data collection times to obtain the equation of the best fit curve using the proper mathematical function.
    
    
  • Input to the best fit equation the threshold value of the condition or distress in question and calculate the time in years between construction and the time when the pavement condition will reach the preestablished threshold value. The RSL is the difference between the calculated time and the pavement surface age.

In the case of a new pavement structure or a newly rehabilitated pavement, the estimated RSL must be positive and restricted to be less than or equal to the DSL of the pavement or the DSL of the treatment, as stated in figure 16.⁽⁴⁾ The reason is that for a few years after treatment, the pavement may or may not show any distress, and hence, the estimated RSL is very large and meaningless. The restriction could be dropped when a significant number of data points indicating pavement deterioration are available. At that time, the RSL could be greater or less than the DSL, and the information could be used as feedback to the pavement design and construction processes.



Figure 16. Equation. RSL.

 

Where:

t (PC = Th) = Time (the number of years) at which the pavement condition reaches the threshold value (Th).
SA = Pavement surface age in years.
DSL = Pavement design service life in years.

The important point is that the RSL does not advocate worst-first as perceived by a few people. It is a management tool that allows State transportation departments to manage their pavement asset based on engineered criteria and a long-term preservation program. It should be noted that the accuracy of RSL is a function of the accuracy and variability of the pavement condition and distress data. In addition, the accuracy of the estimated RSL decreases as the value of that RSL increases (i.e., predicts much further out in time).

The RSL of a given pavement network can be calculated as the weighted average RSL of the total number of pavement sections, n, within the network using figure 17:



Figure 17. Equation. RSL_network.

 

Where:

i = ith pavement segment.
n = Total number of pavement segments or sections in the network.
RSL = Remaining service life.
SL = Segment length.

It should be noted that any pavement segment that falls below the threshold value has an RSL of zero. In general, no negative RSL should be assigned to any pavement regardless of its condition. For a newly designed and constructed or rehabilitated pavement segment, its RSL is equal to the design life.⁽⁴⁾

• Remaining service interval (RSI): RSI is similar to RSL but with some differences. RSI is a new pavement performance measure, which, at the time of this report, was being analyzed on another research study sponsored by FHWA. The final algorithm of the RSI was not used in the current study because it was not available during the study.
  
  
• Service life extension (SLE): SLE is the gain in service life resulting from a pavement treatment, as shown in figure 18.⁽⁵⁾ The accuracy of SLE is a function of the accuracy of the two estimated RSLs before and after treatment. SLE is a useful tool for determining the time benefit resulting from a pavement treatment.

1 inch/mi = 0.0158 m/km.

Figure 18. Graph. Schematic of the definition of SLE.

• Treatment life (TL): TL is the time between the treatment date and the date when the pavement conditions or distresses reach the lesser of the threshold value or the before treatment pavement condition or distress, as shown in figure 19.⁽⁵⁾ TL involves the same limitations as the predicted RSL after treatment (note that the TL does not require any RSL prediction before treatment), except with a shorter prediction in time. TL is a good tool to determine the time until the before treatment conditions return. In the case of worse pavement condition or distress after treatment, the TL is taken as the negative of the time for the before-treatment conditions or distresses to reach the after-treatment conditions, as shown in figure 20.⁽⁵⁾

1 inch/mi = 0.0158 m/km.

Figure 19. Graph. Schematic of the definition of TL.

1 inch/mi = 0.0158 m/km.

Figure 20. Graph. Schematic of the definition of negative TL.

• Total benefits (TB): TB is the ratio of the benefit area to the do-nothing area, as depicted in figure 21.⁽²²⁾ TB accounts for the improved condition over a given time, the area bound by the performance curve and a threshold value; however, it has some significant flaws. TB can be misunderstood because two pavement sections can have the same area ratio but completely different performance. Stated differently, any ratio can be obtained by the division of an infinite set of two numbers such as 1 and 3, 2 and 6, 6 and 18, and so forth. The different perspective of the TB is that the benefit area is normalized relative to the do-nothing area. The do-nothing area, on the other hand, is a function of the pavement conditions and rate of deterioration before treatment.

Figure 21. Graph. Schematic of the definition of TB.⁽²²⁾

• Performance jump (PJ): PJ is the immediate improvement in the pavement condition resulting from treatment.⁽³⁸⁾ PJ indicates the temporary improvement resulting from treatment but has no way to predict the future conditions or how long the improvement will last. An example of PJ is depicted in figure 22.
  
  
• Deterioration rate reduction (DRR): DRR (see figure 22) is the change in deterioration rate from immediately before to immediately after treatment.⁽³⁸⁾ The measure is short term and therefore is not a true measure of performance.

1 inch/mi = 0.0158 m/km.

Figure 22. Graph. PJ and DRR.

PAVEMENT TREATMENT TYPES

The number of available pavement treatment types is large and ever growing as new techniques and materials are developed. Each State transportation department has a group of treatments it chooses to apply based on its experience and the results achieved over time. The selection of a particular pavement treatment from this pool of options is often specific to each State transportation department. The next section provides a discussion of the selection process.

PAVEMENT TREATMENT SELECTION

Many State transportation departments have developed plans and methodologies for selecting pavement treatments. The most common are decision trees and matrices. These are often developed from past experience and tend to focus on one or two options. An example of a decision tree is shown in figure 23 and its reference table 4, and an example of a matrix is shown in table 5. The values in table 4 indicate the trigger values corresponding to roadway functional classifications for use in figure 23. The table contains trigger values based on PQI, present serviceability rating (PSR), and surface rating (SR). These trees/matrices are rarely updated and often neglect new technology. Nonetheless, they are typically based on the following data:⁽¹⁷⁾

• Pavement surface type and/or construction history and environmental conditions.
  
  
• An indication of the functional classification and/or traffic level.
  
  
• At least one type of PCI, including distress and/or roughness. More specific information about the type of deterioration present, either in terms of an amount of load-related deterioration or the presence of a particular condition or distress type.
  
  
• Geometric data indicating whether pavement widening or shoulder repair are required.

Figure 23. Illustration. Example of decision tree for continuously reinforced concrete pavement (CRCP).

Table 4. Trigger values for functional classifications.

Table 5. Example of decision matrix.

Pavement Preservation Costs

This subsection reviews pavement preservation costs and lifecycle cost analyses (LCCAs). Owing to a lack of available cost data required for detailed analyses, no such analyses are included in this report. However, the information is provided to assist in such analyses if more data become available.

The costs of any pavement treatment can be divided into two categories: agency costs and user costs. The agency cost is the physical cost of the pavement project, including design and construction less the residual value of the pavement section at reconstruction. This is often referred to as direct costs.⁽³⁹⁾ User costs are much more difficult to estimate than agency costs because they are not based on specific monetary value but on vehicle operating costs (VOC), delay costs, and accident costs. The three types of user costs and how they relate to normal and work zone conditions are listed in table 6 and discussed in the next few subsections.⁽⁴⁰⁾

Table 6. Review of user cost components.

One problem that arises when estimating user costs is the transformation of delay, accidents, etc., to a monetary value.⁽⁴¹⁾ Some believe that user costs should be defined as “user benefit” and expressed qualitatively as improvements in performance or safety.^(42,43) The user benefits of one treatment compared with another or with the do-nothing alternative could be used to choose between treatments with similar agency costs. In other words, if two treatment options satisfy the pavement needs and have similar agency costs, then the deciding factor would be the user costs. This would greatly simplify the process, which is often considered complicated and deficient, especially when applicable data are not available for the various detailed user cost models.⁽⁴⁴⁾ However, LCCA should be completed to evaluate both the agency and user costs over the pavement lifecycle and to select the most cost-effective treatment strategy. For completion, these costs and LCCA are discussed in the next few subsections.

LCCA

Recently, State transportation departments have faced many constraints, including public demand for quality pavement and budget shortfalls. Hence, the considerations become the following:⁽⁴⁵⁾

• What pavement preservation alternatives should be used, and how often should a given pavement section be preserved?
  
  
• How many miles of each pavement class should be preserved annually?
  
  
• What is the optimum time or the optimum pavement conditions and distresses at which pavement preservation actions should be taken?
  
  
• What are the associated agency and user costs and benefits of each pavement treatment?
  
  
• What are the optimum and most cost-effective short- and long-term pavement preservation strategies that can be applied to keep the pavement network healthy in a cost-effective manner?

These questions cannot be properly and accurately answered unless LCCA is conducted. Such analysis should address the agency and the user costs and must be based on accurate and up-to-date data so that the costs and benefits of various pavement preservation alternatives can be compared.

The Need for LCCA

In general, highway pavements are designed and constructed to provide services for a limited time called the “service life.” Over time, the combined effects of traffic loads and environmental factors accelerate the pavement deterioration and reduce its level of serviceability. Maintenance, preservation, and rehabilitation treatments are designed and applied to pavement sections to slow their rates of deterioration and to extend their service lives. The application of most pavement treatments requires traffic control (lane closures and/or detours), which significantly affects traffic flow, increases travel time, and increases VOC. The costs and benefits of pavement treatments are composed of many elements, including the following:

• Agency costs of the pavement treatment, which consist of many attributes, including the following:
  • Material and contractual costs.
    
    
  • The cost of traffic control in the work zone, which is defined as an area along the highway systems where maintenance and construction operations adversely affect the number of lanes open to traffic or affect the operational characteristics of traffic flow through the work zone.⁽⁴⁶⁾
    
    
  • Quality assurance and quality control costs.
    
    
  • Costs of future treatments.
    
    
• Agency benefits, which could be measured by the service life of the treatment or the SLE of the treated pavement sections.
  
  
• User costs, which are composed of many attributes, including the following:^(47,48)
  • Time delay user costs or work zone user costs, which are defined as the associated costs of time delays due to lane closures because of roadway construction, rehabilitation, and maintenance activities.⁽⁴⁹⁾
    
    
  • Costs incurred by those highway users who cannot use the facility because of either agency or self-imposed detour requirements.⁽⁵⁰⁾
    
    
  • VOC in terms of fuel, wear and tear, and depreciation over the delay periods.
    
    
  • Accident costs.
    
    
  • Environmental costs resulting from air pollution caused by excessive use of gasoline or diesel fuel owing to lower speed and time delay, including noise pollution.
    
    
• User benefits, which are composed of improved serviceability and ride quality that would lower the VOC and improve traffic flow.

Methods of LCCA

Because LCCA considers all planned pavement treatments of a given analysis period, the service life and the value of money over time should be considered. One hundred dollars in 2014 likely bought much more than $100 will in 2024. Hence, the following two common methods are incorporated in LCCA to account for this:

• Net present worth (NPW): NPW or net present value is a common economic indicator. NPW is the monetary value of an action accounting for the transformation of the value of money over time using the discount rate (see figure 24). The use of NPW allows fair comparison of actions taken at different times by converting to a common unit of measure.⁽⁵⁰⁾

  
  
  Figure 24. Equation. NPW.

   

  Where:

  NPW = Net present worth.
  N = Total number of preservation treatments.
  i = Discount rate.
  n = Number of years into the future.
  k = Preservation action number.

  The discount rate reflects the rate of inflation adjusted to the opportunity cost to the public. The opportunity cost is often indicated by a comparison with the discount rate of the conservative U.S. Treasury bill. The historical inflation rate trend from 1999 to 2014 indicates a range of –0.35 to 3.58 percent with an average of 2.39 percent. Table 7 lists common discount rates used by State transportation departments in the 1990s. The discount rate should reflect historical trends in the nation or region where the analysis is conducted.⁽⁵⁰⁾ Alternatively, the discount rate could be determined from the Consumer Price Index (CPI). The average CPI discount rate from 2001 to 2010 was about 2.54 percent.^(45,51)

  Table 7. Historical discount rates.¹

  Year	Analysis Period (Years)
  	3	5	7	10	30
  1992	2.7	3.1	3.3	3.6	3.8
  1993	3.1	3.6	4.0	4.3	4.5
  1994	2.1	2.3	2.5	2.7	2.8
  1995	4.2	4.5	4.6	4.8	4.9
  1996	2.7	2.7	2.8	2.8	3.0
  1997	3.2	3.3	3.4	3.5	3.6
  1998	3.4	3.5	3.5	3.6	3.8
  Average	3.1	3.3	3.4	3.6	3.8
  1Effects of discount rates on $100 from 2014 to 2024 using the average CPI of 2.54 percent. One hundred dollars in 2014 has the same purchasing power as $128 in 2024.

   

• Equivalent uniform annual cost (EUAC): The EUAC method is also widely used as a common economic indicator in LCCA and is typically derived from NPW as calculated in figure 25. The use of either value allows fair comparison of actions taken at different times by converting to a common unit of measure.⁽⁵⁰⁾



Figure 25. Equation. EUAC.

 

Where:

EUAC = Equivalent uniform annual cost.
NPW = Net present worth
i = Discount rate.
n = Number of years into the future.

PAVEMENT PRESERVATION EFFECTIVENESS

In the past, some State transportation departments and almost all local road agencies allowed their pavement assets to deteriorate to levels requiring major rehabilitation or reconstruction. For many years, their treatment policies were based on a worst-first policy in which severely deteriorated pavement sections were subjected to preservation treatments while the condition of the rest of the pavement network continued to deteriorate. Recently, many State transportation departments have initiated and implemented comprehensive pavement preservation programs at the entire road network level.

The programs are based on cost-effective treatment of sections of the pavement network in relatively good condition to restore their conditions, decrease their rates of deterioration, and enhance the safety of the motorists. Over time, the preservation program becomes a part of the annual pavement treatment strategy of the State transportation department.⁽⁵²⁾ The pavement preservation program typically consists of light pavement treatments, such as crack sealing, nonstructural overlay, light rehabilitation actions, mill and fill, and so forth. The alternative to pavement preservation is the old practice of letting the pavement network deteriorate until expensive rehabilitation or reconstruction actions are necessary. Several studies have been conducted on the effectiveness of pavement preservation and are summarized in the following subsections.

The effectiveness of pavement treatments can be measured in the short term and/or the long term. Short-term benefits are defined by the immediate improvement to the pavement conditions and rates of deterioration, while long-term benefits are defined over the service life of the pavement section by the performance and extension in service life. The costs can also be short term (individual treatment) or long term (LCCA).

Pavement Preservation Cost Effectiveness at the Project Level

Pavement preservation can be applied through a series of pavement treatments over the pavement lifecycle (a treatment strategy). The alternative to pavement preservation is allowing the pavement to deteriorate until reconstruction is required, the worst-first or do-nothing scenario. The cost effectiveness of pavement preservation at the project level can be quantified using LCCA. The analysis could be conducted on the various alternative pavement preservation treatments that could be applied to a pavement section over time and on the do-nothing scenario followed by reconstruction. Comparison of the results from the various strategy analyses indicates the cost savings or extra expenditures of performing preservation over the life of the pavement segment.

The cost effectiveness of pavement preservation at the project level has been well documented. Most literature agrees that pavement preservation can be conducted at minimal cost and create major savings over the life of the pavement. One study found that the cost savings of pavement preservation was as high as $5 saved for every $1 spent on preservation.⁽⁵³⁾ Another reports savings of $4 to $10 for every $1 spent on preservation.⁽⁵⁴⁾ Other benefits include improving ride quality and creating a pavement network with consistent needs from year to year.⁽⁵⁵⁾

Pavement Preservation Effectiveness at the Network Level

The effectiveness of pavement preservation at the network level is more difficult to quantify than at the project level. Funds designated for preservation reduce the funds available for rehabilitation and reconstruction. In other words, pavement preservation is thought to decrease the lifecycle cost of a given pavement project, but the effect on the network is often unknown. In addition, the public and legislators may not understand why pavements in seemingly good condition are being treated, while others in poor conditions are not. State transportation departments should document and communicate the effects of preservation maintenance on the health of the pavement network and on the lifecycle cost in a clear and consistent manner. Educating the public and the legislature is necessary to establish and maintain a successful pavement preservation program.⁽⁵⁵⁾

The short- and long-term benefits and effectiveness of pavement preservation at the project and network levels should be quantified. Short-term benefits include improving ride quality and addressing safety issues, while long-term benefits (cost savings) are not realized until years or decades into the future. Therefore, pavement preservation strategies must be optimized through projection of needs and funds into the future. In this way, the effects of performing or deferring various pavement projects can be evaluated.^(55,56)

TREATMENT TRANSITION MATRIX (T²M)

Pavement treatment effectiveness is often described with a single value or a range of values, such as 5 to 10 years gained or an average 7-year service life. The probabilities of the various results are not typically reported. The probabilistic effectiveness of treatments can be quantified and communicated using an innovative matrix format called T²M.⁽³⁾ T²M shows the following:

• Distribution of the pavement CSs before and after treatment.
  
  
• Transitions of the pavement CSs from before treatment to after treatment resulting from the treatment.
  
  
• List of the treatment benefits.
  
  
• Long-term results of the pavement treatment.

Table 8 shows an example of a T²M that lists the results of single chip seal applications in Colorado. The cells display this information in the following convenient way:

• Columns A through D list the following before treatment information: pavement CSs (RSL bracket numbers), RSL ranges, and the number and percent of 0.1‑mi (0.16-km)-long pavement segments in each before treatment CS.
  
  
• Columns E through I list the following after treatment information: CSs (RSL bracket numbers), RSL ranges, the number of 0.1-mi (0.16-km)-long pavement segments transitioned from the given before treatment CS to each after treatment CS, and the total number of 0.1-mi (0.16-km)-long pavement segments transitioned to each after treatment CS.
  
  
• Columns J through L list the following pavement treatment benefits: average TL, SLE, and after treatment RSL of all 0.1-mi (0.16-km)-long pavement segments transitioned from a given before treatment CS to all after treatment CSs; and the overall average TL, SLE, and after treatment RSL.

Table 8. T²M for single chip seal in Colorado.

PRESERVATION TIME SELECTION

The effectiveness of pavement treatments is often determined simply based on the benefits gained, as mentioned previously. The benefits, however, do not indicate effectiveness relative to cost, which is the main constraint for all State transportation departments. Most literature agreed that treatments applied to pavements in better condition produce more benefits, and the cost of the treatment was a function of the conditions.^(38,57,58) Further, the time-value relationship of money affects the cost of the treatment and its cost effectiveness. Therefore, benefits must be compared relative to costs to determine the cost effectiveness of the treatment and to select the treatment timing.⁽⁴¹⁾ Performing pavement treatments at the optimum time provides the greatest benefit-to-cost ratio. The idea of optimum timing is not new; in fact, the concept was built into the AASHTO 1993 design guide.⁽³⁶⁾ Few methodologies for the determination of optimum treatment timing for preventive maintenance and rehabilitation actions were developed.⁽²²⁾ This methodology is designed to optimize treatment timing based on the treatment benefit (calculated by the area under the performance curve). However, the most cost-effective treatments should consider the pavement longevity and should be based on the ratio of dollars to years of service.⁽³⁾ To improve lifecycle costs, State transportation departments should base their preservation strategies on maximizing the longevity of the pavement network rather than maximizing the condition of the network.

THE LTPP PROGRAM

The LTPP Program was established under the Strategic Highway Research Program (SHRP) in 1987. Since 1991, FHWA has managed and funded the LTPP Program. The program houses two fundamental groups of experiments: SPS and GPS. The LTPP Program has addressed myriad studies of pavement-related issues ranging from validation of pavement design procedures to traffic and material variability, and pavement maintenance, preservation, and rehabilitation actions. The conclusions of these studies are documented in countless publications in the forms of research reports, product briefs, and techbriefs, which have substantially contributed to the development of advanced pavement technology and highlighted the importance of the LTPP Program and its associated database.

OBJECTIVES AND SCOPE OF THE LTPP PROGRAM

The overall objective of the LTPP Program is to collect, store, and make various data elements relating to pavement performance available to researchers, scientists, and the general public. These include the pavement structures and conditions, traffic volume and load, and environmental conditions for various pavement sections located along the existing North American highway systems. Over a more than 20-year period, the data have been used by researchers, practitioners, and other stakeholders to assess the long-term performance of pavements under various loading and environmental conditions and with different structural and material compositions. The specific established objectives of the LTPP Program include the following:⁽⁵⁹⁾

• Evaluate the existing pavement design methodologies.
  
  
• Develop improved design methodologies and strategies for the rehabilitation of existing pavements.
  
  
• Develop improved design equations for new and reconstructed pavements.
  
  
• Determine the effects of loading, environment, material properties and variability, construction quality, and maintenance levels on pavement condition, distress, and performance over time.
  
  
• Determine the effects of specific design features on pavement performance.
  
  
• Establish a national long-term pavement database with detailed information suitable for various assessments and studies.

LTPP TEST SECTIONS

The LTPP Program consists of about 2,500 500-ft (152.4-m)-long, mostly in-service test sections located in all 50 U.S. States, Puerto Rico, and 10 Canadian provinces. The test sections are divided between the two main studies, SPS and GPS. Some of the SPS test sections were reconstructed to investigate certain pavement engineering factors, while others were specially preserved to study the impacts of some preservation treatments. In contrast, the GPS test sections consist of sections of existing roads that were subjected to various typical maintenance and preservation treatments. Thus, eight types of existing in-service pavements make up the GPS and are monitored throughout North America. More details on the SPS and GPS test sections can be found throughout the remainder of this report.

SPS

SPS is a long-term program designed to study specifically constructed, maintained, or rehabilitated pavement sections incorporating controlled sets of experimental design and construction features. The main objective of the SPS experiments is to provide more detailed and complete sets of data to extend and refine the results obtained from the GPS experiments. There are nine SPS experiments grouped by the five categories listed in table 9.⁽⁵⁹⁾

Table 9. The SPS categories and experiments.

The SPS experiments involve monitoring the newly constructed pavement sections and the existing pavement sections that were subjected to maintenance or rehabilitation treatments after assignment to the SPS. The SPS is divided into various SPS experiments numbered SPS-1 through -9. Each experiment includes multiple test sites, and each test site contains between 2 and 12 pavement test sections depending on the experiment. Following the original assignment of test sections in 1992, numerous supplemental test sections were constructed by different State transportation departments to study aspects of particular interest to them.⁽⁵⁹⁾ FHWA is initiating new sites for the study of WMA (SPS-10) and is currently considering new pavement preservation experiments in addition to the existing SPS experiments.

GPS

GPS is also a long-term program designed to study a series of experiments on selected in-service pavement structures with the objective of establishing a national pavement performance database. Pavement sections believed to be built with proper materials and good engineering design were selected as part of the GPS program.⁽⁵⁹⁾

The pavement structures included in the GPS were constructed or reconstructed up to 15 years before the start of the LTPP Program. Unfortunately, detailed data were often not available for the period between the original construction time and the time when they were selected for the LTPP Program. However, it was believed that some beneficial insights might be drawn without this data. Finally, some SPS test sections have been reclassified as GPS test sections upon the application of rehabilitation treatments. Table 10 lists the titles of each of the GPS experiments.⁽⁵⁹⁾

Table 10. The GPS experiments.

Summary of Previous Findings

In this study, previously published reports regarding the LTPP Program and data analyses were scrutinized. The topics of these reports include the effects of design factors on pavement performance measures and the selection of appropriate and cost-effective treatment type. However, the findings of these reports did not adequately address the relationships between the maintenance and rehabilitation actions and the performance of the various pavement sections or the optimal timing for treatment application. Nevertheless, some of the relevant reported findings are enumerated and summarized in the following subsections.

Impacts of Pavement Treatment on Pavement Performance

This subsection summarizes reported findings related to the impacts of pavement treatments on the collected pavement condition and distress data of various LTPP experiments.

For the SPS-3 experiment, the following findings were reported:

• Thin asphalt overlay was found to be the most effective maintenance treatment followed by chip seal and slurry seal treatments in terms of roughness, rut depth, and fatigue cracking.⁽⁶⁰⁾
  
  
• Crack sealing had no significant effect on long-term roughness, rut depth, or fatigue cracking.⁽⁶⁰⁾
  
  
• Crack sealing had only marginal impact on longitudinal and transverse cracking. This is mainly because sealed cracks are counted as separate distresses in the LTPP distress survey procedures.⁽⁶¹⁾

For the SPS-4 experiment, the findings were inconsistent. Some researchers reported the following findings:

• Sealed joints performed better than unsealed joints, while other researchers reported that there were no significant differences between sealed and unsealed joints.^(61–63)
  
  
• Silicone joint sealant materials performed better than compression seals and hot pours in terms of the overall failure (adhesion loss and joint spalling).⁽²⁸⁾
  
  
• The lack of a significant quantity of data is a drawback in the analyses. Survey measurements of sealed joints/cracks were collected for 34 test sites, while undersealed test section data were available for only 10 sites.⁽⁶³⁾

For the SPS-5 experiment, the following findings were reported:

• Thick overlays performed better than thin overlays with respect to transverse and fatigue cracking.⁽⁶³⁾
  
  
• Inconsistent results were reported for longitudinal cracking, rut depth, and IRI. Some researchers reported that there was no apparent effect of thick and thin overlays on rut depth or IRI, while others reported that thicker overlays provided better IRI.^(63–65)
  
  
• Virgin and recycled HMA used in overlays were found to have no significant impact on transverse, longitudinal, or fatigue cracking, rut depth, or IRI.^(60,64) On the other hand, it was reported that recycled HMA performed better than virgin HMA with respect to fatigue and transverse cracking in dry climates and/or low traffic roadways.⁽⁶³⁾
  
  
• The type of pavement surface preparation performed before overlay had no significant effect on rut depth or IRI.^(60,64)
  
  
• Inconsistent results were reported regarding the effects of pavement surface preparation before overlay on long-term cracking performance. It was reported that intense and minimal pavement surface preparations made no significant difference in long-term cracking performance, whereas others stated that intensely prepared pavement sections performed better than minimally prepared sections with respect to fatigue and longitudinal cracking.^(61,63)

For the SPS-6 experiment, the following findings were reported:

• The 8-inch (203-mm) AC overlay was the most effective rehabilitation option followed by the 4-inch (102-mm) AC overlay and concrete pavement restoration with and without diamond grinding.⁽⁶⁰⁾ However, on the contrary, it was reported that rehabilitation strategies without AC overlays were best to mitigate the crack initiation and propagation.⁽⁶³⁾
  
  
• Pavement rut depth on composite pavement was independent of overlay thickness, pre-overlay repairs, and mixture type.⁽⁶⁰⁾
  
  
• No significant difference in long-term cracking performance was detected among the following:⁽⁶⁰⁾
  
  
  • Test sections subjected to minimal versus intensive pre-overlay preparation.
    
    
  • Test sections with and without sawed and sealed joints.
    
    
  • Test sections with 4-inch (102-mm) overlays, with sawed-and-sealed joints, and cracked and seated sections.
    
    
  • Test sections with 4- and 8-inch (102- and 203-mm) overlays.
    
    
• Fractured PCC test sections with an AC overlay performed better in roughness than those nonfractured test PCC sections subjected to the same AC overlay. Further, the nonfractured sections that were subjected to AC overlay performed better than nonfractured PCC sections that were subjected to diamond grinding and patching without AC overlay.⁽⁶⁶⁾
  
  
• Pavement roughness was independent of whether the pavement sections were subjected to sawing and sealing before the AC overlay.⁽⁶⁰⁾

Impacts of Design Variables on Pavement Performance

The findings of various studies regarding the impacts of various design factors on pavement performance are summarized in the next six subsections.

Climatic Variables

One study suggested that dowel bars should be used in JPCP to reduce joint faulting in WF climates.⁽⁸⁾ In dryer climates, the joint spacing should be reduced to decrease transverse cracking potential due to high thermal gradients. This is because the precipitation has the following two effects on the pavement material temperatures:

• Precipitation cools or heats the pavement surface relative to the subsurface temperature, thereby reducing the difference in temperature with depth.
  
  
• Water generally requires much more energy to change temperature than air, binder, and aggregate materials. Therefore, a higher water content or more frequent saturation reduces the magnitude and rate of heating and cooling of the pavement layers.

In addition, IRI was found to be higher for similar pavements located in colder and wetter climates than those in other climates. Further, higher initial roughness led to higher rates of deterioration. The researchers stated that the results should be reviewed with caution because the PCC durability was not included in the analysis, which might have affected the results.⁽⁸⁾

One study indicated that faulting in undoweled JPCP test sections in DF regions was similar to those sections in DNF regions.⁽⁶⁷⁾ The mean faulting values were 0.126, 0.079, 0.063, and 0.039inches (3.2, 2.0, 1.6, and 1.0 mm) in the WF, WNF, DF, and DNF regions, respectively. On the other hand, the doweled JPCP test sections showed no significant differences in joint faulting between the WF and the WNF regions. Doweled joint faulting occurred mostly in the DF regions, followed by the DNF and the WF regions. Test sections in the WNF regions showed the lowest faulting values.

On the other hand, an initial evaluation of SPS-2 test sections indicated that for doweled joints in rigid pavements, faulting was most prevalent in the DF region, followed by the DNF, and the WF regions.⁽⁶⁸⁾ In addition, the total longitudinal crack length was found to be longer in the DNF region, followed by the DF and the WF regions.

A strong relationship was reported between IRI and climatic conditions for flexible pavements.⁽⁶⁹⁾ Higher roughness was measured in pavement sections located in areas with higher precipitation, higher freezing index, and/or higher number of freeze-thaw cycles. The researchers also stated that adequate frost protection was an important factor for good pavement performance. In hot climates, roughness values were strongly related to the number of days with temperatures above 90 °F (32 °C). In addition, the roughness was lower for pavement sections in hot regions with higher precipitation than for those with less precipitation. The researchers related this finding to the cooling effect that precipitation may have on asphalt pavements, thereby reducing deformations resulting from high temperatures. On the other hand, rigid, jointed pavements were found to have higher roughness in climates with higher precipitation.

Roadbed Soils

One study indicated that better subgrade support (higher backcalculated modulus of subgrade reaction, k-value) resulted in fewer transverse cracks with deteriorated edges and in lower roughness (IRI) for JPCP, JRCP, and CRCP.⁽⁷⁰⁾

Another study concluded that PCC pavements constructed over fine-grained roadbed soils had higher joint faulting than those constructed on coarse-grained roadbed soils.⁽⁸⁾ This was likely due to increased soil erosion and reduced water permeability. Likewise, JPCP sections constructed on coarse-grained roadbed soils had lower IRI than those constructed on fine-grained roadbed soils. The researchers recommended that a thick layer of granular material be placed and compacted beneath the aggregate base course to improve drainage and reduce faulting, especially for undoweled pavements. The study also concluded that PCC slabs supported on strong foundations, such as stabilized bases or granular roadbed soils, often had a lower initial roughness.

A study based on the SPS-8 experiment data found that the most prevalent early pavement distress was longitudinal cracking outside the wheelpath.⁽⁷¹⁾ Further, this distress was most commonly observed in sections located in the WF region and on an active roadbed soil (frost-susceptible or swelling soils due to freeze-thaw cycles). It was also observed that flexible and rigid pavements constructed on active roadbed soils had the highest mean initial roughness values and the highest rates of deterioration compared with pavements constructed on fine and coarse-grained roadbed soils. This study were in agreement with other studies that a good working platform such as stabilized base and granular subgrade contributed to a smoother pavement after construction.^(8,70)

Joint Load Transfer

A common finding from a few studies was that the presence of dowel bars had a significant impact in reducing joint faulting.^(8,67,70) In fact, after 10 years in service, JPCP sections with dowel bars showed 50 percent less joint faulting than those without dowel bars. In wet and/or freeze climates, the use of dowel bars appeared to negate the effects of cold temperatures and increased moisture that could often lead to erosion and pumping of fines. It was also found that the use of doweled joints could have more impact on pavement performance than design features such as subdrainage, tied-concrete shoulders, and joint spacing.⁽⁶⁷⁾ Further, it was found that properly sized dowel bars could eliminate corner breaks and transverse cracking near the joints as well as minimize joint faulting.⁽⁷²⁾

In an FHWA report, the impacts of various parameters on the variability of the load transfer, as quantified by the load transfer efficiency (LTE) measured over time, were documented.⁽⁷³⁾ The researchers reported the following findings:

• For undoweled JPCP joints, the variability of LTE along a pavement section was inversely correlated to the average LTE. As the average LTE of a pavement section increased, the variability decreased.
  
  
• The LTE variability was not affected by joint spacing, base type, or shoulder type (PCC or AC).
  
  
• The LTE variability was higher in pavements with subsurface drainage systems than in pavements without subsurface drainage systems.
  
  
• The variability of the average LTE for pavements with granular roadbed soils was higher than that of pavements with silty clay roadbed soils.
  
  
• The variability of the average LTE was not affected by the amount of annual precipitation, the number of annual freeze-thaw cycles, or the average mean annual temperature.
  
  
• The variability of the average LTE decreased as the annual freezing index increased.
  
  
• No direct relationship was found between pavement age and the variability of the average LTE measurements over time.
  
  
• The variability of the average LTE was higher in pavements with tied concrete shoulders than in pavements with an asphalt shoulder.

Drainage

In a National Cooperative Highway Research Program Research Results Digest, it was stated that for properly designed doweled joints in JPCP, joint faulting was fairly low, and permeable bases had relatively small effect on reducing joint faulting.⁽⁷⁴⁾ Edge drains were found not to have a significant effect on joint faulting when dense-graded bases were used. For undoweled JPCP, joint faulting in general was higher, and permeable bases had a significant effect in reducing joint faulting. However, the permeable bases should be designed and maintained to reduce or eliminate the migration of fines from the lower materials. Similarly, slab cracking was found to be reduced in pavements constructed on asphalt-treated permeable bases. D-cracking was also found to be less prevalent in pavement sections constructed on permeable bases, likely because of reduced base saturation and introduction of water and various compounds into the concrete slab. Note that all of these findings were based on limited data.

Base Type

In separate studies sponsored by FHWA, it was reported that JPCP constructed over a stabilized base had less faulting and smoother surfaces than those constructed with an untreated aggregate base, especially in undoweled JPCP.^(8,75) JPCP with an asphalt-stabilized base or lean concrete base had significantly lower initial roughness when compared with other base materials. In addition, JPCP sections constructed with granular and asphalt-stabilized bases had significantly lower percentages of cracked sections than JPCP with cement-treated or lean concrete bases. The cracking was not associated with an increased roughness.

In an LTPP Program sponsored study using the SPS-2 experiment data, it was found that pavement sections with permeable asphalt-treated bases developed the fewest transverse and longitudinal cracks.⁽⁷⁶⁾ On the other hand, pavement sections with lean concrete base developed the most transverse and longitudinal cracks during the first 10 years of pavement service life. This confirmed earlier findings.^(8,75)

Slab Width

Two studies concluded that increasing slab width by 2 ft (0.6 m) reduced faulting of concrete pavements by reducing the critical deflections at the corner of the slab from heavy truck axles.^(8,67) The mean faulting data for undoweled sections (10 years old or less) indicated about 50 percent less faulting with a widened slab. It was stated that this outcome agreed with previous non-LTPP field performance data. No difference was found between the faulting of doweled widened slab sections and doweled conventional-width JPCP. However, JPCP sections with widened lanes did not show any transverse cracking. In addition, the initial evaluation on SPS-2 data revealed that widened slabs have less initial roughness.⁽⁷⁶⁾

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

This chapter presented a review of the state of the practice of various State transportation departments with regard to several aspects of pavement condition and distress data analyses and treatment selection. A summary of the results of previous studies conducted using the LTPP database was also included. The following topics were covered:

• Pavement distress severity levels.
  
  
• Pavement condition and distress descriptions.
  
  
• Pavement performance modeling and treatment benefit calculations.
  
  
• Treatment types and selection.
  
  
• Preservation costs and LCCA.
  
  
• Effectiveness of pavement treatments at the project and network levels.
  
  
• Pavement treatment time selection.
  
  
• LTPP Program and its objectives.
  
  
• SPS and GPS test sections of the LTPP Program.
  
  
• A summary of previous findings, including the impacts of pavement treatments and various design factors on pavement performance.

The various topics were evaluated, and the research team reached the following conclusions and made the following recommendations regarding execution of the current study:

• The pavement cracking data were typically collected and stored based on three severity levels (low, medium, and high). For most cases, the data could not be analyzed because of the high data variability from one year to the next. Therefore, it was recommended that the sum of crack lengths or crack areas of all severity levels be used in this study for time-series modeling.
  
  
• Various methods exist to rate pavement conditions based on threshold values, indices, and descriptive terms. It was recommended to rate pavement based on its conditions and rates of deterioration over time to facilitate planning and pavement management. The dual condition rating systems described in chapter 3 address this issue.
  
  
• Pavement performance could be established and estimated over time through modeling the collected condition and distress data. The performance curves could be used to estimate future conditions and the time period to reach certain threshold values. This methodology was recommended for use in this study.
  
  
• The benefits of pavement treatments could be quantified in several ways based on changes in conditions, rates of deterioration, and a combination thereof. In this study, the treatment benefits were calculated based on the dual condition rating systems detailed (see chapter3).
  
  
• Numerous pavement treatments could be selected to address the pavement conditions. The selections could be based on the conditions, the rates of deterioration, the causes of distress, or combinations thereof. It was recommended to select treatments that addressed most pavement conditions and their causes.
  
  
• Pavement treatment costs consist of agency and user costs. The costs of various pavement treatments and series of treatments (treatment strategy) could be evaluated using LCCA.
  
  
• Pavement treatment cost effectiveness could be evaluated at the project and network levels. It was recommended to perform LCCA at the project level and strategy optimization at the network level to improve the overall cost effectiveness of the pavement asset management.
  
  
• For pavement projects that received the same treatment type, T²Ms could be developed to display the distribution of the pavement conditions before and after treatment and to estimate the treatment effectiveness and the selection of the optimum treatment time. In this study, T²Ms were populated using the State data.
  
  
• The SPS and GPS experiments were designed such that pavement treatments were applied to pavement sections representing an assortment of different structures located in different environments and subjected to varying traffic levels. In this study, all treated LTPP test sections that had adequate time-series pavement condition and distress data were analyzed to evaluate the effectiveness and the impacts of the treatments on pavement performance.
  
  
• Numerous LTPP studies were evaluated that dealt with the different impacts of treatments on pavement conditions and distresses. Few studies considered pavement conditions and rates of deterioration over time. It was recommended to include the before and after treatment pavement conditions and rates of deterioration in studying the pavement performance.
  
  
• Previous LTPP studies focused on the effects of environment, roadbed soil, joint load transfer, drainage, base type, and slab width on pavement performance. Few conclusions regarding the effects of these factors were made. It was recommended to analyze the effects of the environmental regions on the effectiveness of the treatment using the before treatment and after treatment pavement condition and distress and the associated rates of deterioration.