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37 are not considered). Therefore, one of the important These evaluations are referred to as Case Studies 1 through steps in the analysis setup process is the definition of the 4. Also, the LTPP data for the maintenance effectiveness specific treatment application ages that will be consid- experiments--available on the DataPave 3.0 software--are ered in the analysis. The primary result of the analysis presented as Case Study 5. This section describes the details is identifying the most effective treatment application of the validation process. age from among those considered. The purpose of the evaluation was not to identify the opti- Define do-nothing curves for each included condition mal time to perform preventive maintenance using an agency's indicator--The user must define, for the do-nothing data, but to demonstrate the methodology's and the OPTime option, the expected performance curves for each con- analytical tool's ability to use actual data. The following case dition indicator included in the analysis. These relation- studies indicate how actual data are handled in the analytical ships represent the expected pavement performance in approach, the types of assumptions that are made to use the the absence of any preventive maintenance or rehabili- methodology, and how the absence of certain types of infor- tation activities. The relationships should, however, con- mation preclude the successful use of the methodology. sider any routine or reactive maintenance that is typical for a given pavement type. Define post-preventive maintenance performance Case Study 1--Arizona relationships--In order to compute benefit for a given performance indicator, the user defines how the pave- Introduction ment will perform in the prediction mode (i.e., condition indicator versus time relationships) after a preventive The Arizona Department of Transportation's (ADOT) data maintenance treatment is applied. Because these perfor- for seal coat treatments on flexible pavements were analyzed. mance relationships depend on pavement age (or pave- Data summaries and performance models were obtained from ment) condition at treatment application, a separate per- a 1999 report (24, 25). formance relationship is required for each application age (timing scenario) included within the analysis. Define cost types and values--The user has the option Condition Indicators to include any or all cost types (i.e., preventive mainte- nance treatment costs, user delay-related costs, rehabili- The effectiveness of seal coats was measured by examin- tation costs, and the cost of additional routine or reactive ing their effect on the following three condition indicators: maintenance activities) in the LCCA. Included costs are used to make up the cost streams associated with each Roughness (IRI) individual preventive maintenance timing scenario. Friction (measured with a Mu-meter) Define benefit weighting factors--Each considered Cracking (measured in terms of percent area) condition indicator is assigned an integer weighting fac- tor between 0 and 100, where all the entered weighting An earlier study (24, 25) determined that roughness was by far factors must add up to 100. The selected weighting fac- the most useful performance indicator to distinguish between tors are used to combine the individual benefit values different materials and different circumstances. Table 21 sum- into an overall benefit value for each timing scenario. marizes subjective ratings associated with ranges of each of these condition indicators. Data Interpretation TABLE 21 Condition indicator ranges and their associated The analysis tool includes a number of summary tables subjective ratings and charts that illustrate the results of the analysis. Specifi- Condition cally, the results summarize the benefit values, EUACs, B/C Condition Indicator Value/Range Category ratios, and effectiveness indices determined for all timing Roughness (IRI) <1.47 m/km (93 in./mi) Low scenarios. The timing scenario with the largest B/C ratio is 1.47 to 2.26 m/km (93 to identified as the scenario representing optimal timing. 143 in./mi) Medium >2.26 m/km (143 in./mi) High Friction (Mu-meter <35 Low VALIDATION OF THE ANALYSIS reading) 35 to 42 Medium METHODOLOGY 43 to 99 High An evaluation of the analysis methodology and the analysis Cracking (percent of area) <10 Low 10 to 30 Medium tool was undertaken using actual data provided by four state >30 High agencies--Arizona, Kansas, Michigan, and North Carolina.

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38 Do-Nothing Performance Curves 62 61 y = -0.22x + 60.76 Friction (Mu-meter A series of linear do-nothing performance curves are avail- 60 59 results) able for the selected roughness, friction, and cracking condi- 58 tion indicators for pavements in all roadway classes, envi- 57 ronmental regions, and traffic levels (24, 25). Table 22 lists 56 55 the do-nothing condition indicator versus age relationships 54 selected for this demonstration; these relationships are shown 53 in Figures 13, 14, and 15, respectively. 0 5 10 15 20 25 30 Pavement Age, years Benefit Cutoff Values Figure 14. Assumed friction do-nothing curve for Case Study 1--Arizona. Benefit cutoff values are determined by analyzing the expected regression equations over the condition indicator ranges listed in Table 21. Details of this analysis are presented Cracking--Since cracking increases with time, an upper as follows: cracking benefit cutoff value is required for the analy- sis. A value of 10% was selected, as it indicates the tran- Roughness--Because IRI increases with time, an upper sition from low to medium cracking. According to the IRI benefit cutoff value is required. A value of 1.47 m/km cracking regression equation, this value corresponds to (93 in./mi) was chosen because it indicates the transition an age of 28.5 years. The lower benefit cutoff value was from a low roughness level to a tolerable roughness level. conservatively set to a value of 0% cracking. According to the roughness regression equation, this value is predicted at an age of 27.8 years. The lower ben- efit cutoff value was set to a value of 0 m/km (0 in./mi). Post-Preventive Maintenance Performance Friction--As Mu-meter values typically decrease over Relationships time, a lower benefit cutoff value is required for the analysis. A comparison of the regression equation to the A series of linear post-treatment performance curves for the condition ranges listed in Table 21 finds that friction val- selected roughness, friction, and cracking condition indicators ues of 35 and 43 correlate to ages of 117.1 and 80.7 years, are available (24, 25); these relationships are listed in Table 23 respectively. Because these are extremely high ages, a and plotted for roughness, friction, and cracking in Figures 16, friction value of 55 was chosen, corresponding to a pre- 17, and 18, respectively. However, these relationships are pre- dicted age of 26.2 years. An upper benefit cutoff value sented as functions of the treatment age (TAGE), not as func- was conservatively set to a value of 100. tions of the pavement's age when the treatment was applied. TABLE 22 Do-nothing performance equations 12 Cracking, percent area 10 y = 0.33x + 0.6 Condition Indicator Regression Equation Roughness (IRI), m/km* IRI = 0.0207 AGE + 0.89 8 Friction (Mu-meter results) Friction = 0.22 AGE + 60.76 6 Cracking (% of 1,000 sf area at Cracking = 0.33 AGE + 0.6 4 each milepost), % 2 *1 m/km = 63.4 in./mi. 0 0 5 10 15 20 25 30 1.60 Pavement Age, years Roughness (IRI), m/km 1.40 IRI = 0.0207* Age + 0.89 1.20 Figure 15. Assumed cracking do-nothing curve for Case 1.00 Study 1--Arizona. 0.80 0.60 TABLE 23 Post-treatment performance equations 0.40 associated with a seal coat treatment 0.20 0.00 Condition Indicator Regression Equation 0 5 10 15 20 25 30 Roughness (IRI), m/km IRI = 0.0273 TAGE + 1.52 Pavement Age, years Friction (Mu-meter results) Friction = 0.54 TAGE + 69.2 Figure 13. Assumed roughness do-nothing curve for Case Cracking (% of 1000 sf area at Cracking = 0.7 TAGE + 3.2 Study 1--Arizona. each milepost), %

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39 2.50 30 Roughness (IRI), m/km Cracking, percent area 25 Cracking = 0.7 * TAGE + 3.2 2.00 20 1.50 15 1.00 IRI = 0.0273 * TAGE + 1.52 10 0.50 5 0.00 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 Treatment Age, years Treatment Age, years Figure 16. Assumed post-treatment seal coat roughness Figure 18. Assumed post-treatment seal coat cracking relationship for Case Study 1--Arizona. relationship for Case Study 1--Arizona. 80 Analysis Setup 70 Friction (Mu-meter 60 The analysis tool is used to analyze the interpreted perfor- 50 mance data. Specifically, the following inputs define the analy- results) 40 sis session for this case study: 30 20 Friction = -0.54 * TAGE + 69.2 Analysis Type--A detailed analysis type is selected 10 because actual data are being analyzed. 0 Condition Indicators--Three condition indicators are 0 5 10 15 20 25 30 used in this analysis: roughness, friction, and cracking. Treatment Age, years Preventive Maintenance Treatment Selection--A seal Figure 17. Assumed post-treatment seal coat friction coat applied at 1, 4, 7, 10, and 13 years is investigated. No relationship for Case Study 1--Arizona. routine/reactive maintenance costs are included. TABLE 24 Analysis results for Case Study 1--Arizona Output Data Pavement Surface Type: HMA Treatment Type: Seal Coat Application Years: 1, 4, 7, 10, 13 Expected Do-Nothing Service Life (yrs): 26.2 Benefit Summary Individual Benefit Summary Benefit Ranking Factors => 25 15 60 Nonload- Application Related Roughness/ Age, yrs Total Benefit Cracking Smoothness Friction 1 0.04 -0.68 -0.84 0.57 4 0.23 -0.49 -0.75 0.77 7 0.39 -0.32 -0.67 0.95 10 0.53 -0.17 -0.59 1.11 13 0.65 -0.05 -0.52 1.24 Cost Summary Other Application Treatment User Cost, PW Maintenance Rehab. Cost, Total Present Age, yrs Cost, PW $ $ Cost, PW $ PW $ Worth, $ EUAC, $ 1 $24,423 n/a n/a n/a $24,423 $2,847 4 $21,712 n/a n/a n/a $21,712 $2,088 7 $19,302 n/a n/a n/a $19,302 $1,606 10 $17,159 n/a n/a n/a $17,159 $1,275 13 $15,255 n/a n/a n/a $15,255 $1,035 Effectiveness Summary Expected Application Effectiveness Expected Life, Extension of Age, yrs Index Total Benefit EUAC, $ yrs Life, yrs 1 2.38 0.04 $2,847 10.7 -15.5 4 17.32 0.23 $2,088 13.7 -12.5 7 38.66 0.39 $1,606 16.7 -9.5 10 66.30 0.53 $1,275 19.7 -6.5 13 100.00 0.65 $1,035 22.7 -3.5

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40 Effectiveness Index vs. Application Timing Effectiveness Index 100 80 60 40 20 0 0 2 4 6 8 10 12 14 Timing of First PM Application, years Extension of Life vs. Application Timing EUAC ($) vs. Application Timing 0.0 $3,000 Extension of Life, $2,500 EUAC ($) -5.0 $2,000 years $1,500 -10.0 $1,000 -15.0 $500 $ -20.0 0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14 Timing of First PM Application, years Timing of First PM Application, years Figure 19. Results of the analysis for Case Study 1--Arizona. Performance Relationships--Models are already pro- (i.e., for the entire treatment application. A discount rate vided. The do-nothing performance relationships are of 4.0 percent is used in the analysis. defined in Table 22 and the post-treatment performance Benefit Weighting Factors--Benefit weighting factors relationships are defined in Table 23. are needed for three condition indicators; they were arbi- Project Definition--The project size is defined as 16,723 trarily chosen as 15, 60, and 25 percent for roughness, fric- m2 (20,000 yd2). tion, and cracking, respectively. Cost Data--Only treatment costs are included in the cost analysis (i.e., rehabilitation, user, and routine maintenance Analysis Results costs are excluded). The in-place unit cost of a seal coat The output results are summarized in Table 24 and Fig- application is $1.52/m2 ($1.27/yd2) as reported by ADOT ure 19. These results show that of the five investigated appli- 12 10 Nonload-Related Cracking 8 6 4 2 0 0 5 10 15 20 25 Age Do Nothing The Best Treatment Application Lower Benefit Cutoff Value Upper Benefit Cutoff Value Figure 20. Cracking versus age for the most appropriate application age of 13 years for Case Study 1--Arizona.

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41 2.5 2 Roughness 1.5 1 0.5 0 0 5 10 15 20 25 Age Do Nothing The Best Treatment Application Lower Benefit Cutoff Value Upper Benefit Cutoff Value Figure 21. Roughness versus age for the most appropriate application age of 13 years for Case Study 1--Arizona. cation ages, the most cost-effective option is applying the curve crosses the do-nothing curve and the areas above the treatment at age 13 (indicated by an EI of 100). Because the do-nothing and post-treatment curves bound by the upper same equation is used for all application ages, it is not unex- benefit cutoff value of 10 percent appear to be similar. pected that the largest benefit is associated with the latest However, the results provided in Table 24 show an indi- application. This is because the benefit increases and treat- vidual benefit value of -0.05 (for an application age of 13 ment cost decreases with application age. The actual condi- years) that indicates a slightly greater benefit area for the tion versus age plots for the three included condition indica- do-nothing case than for the post-treatment case. The neg- tors are illustrated in as Figures 20, 21, and 22. ative benefit values associated with roughness occurred Note that all of the individual benefits associated with because, according to the IRI equations, the application of cracking and roughness are computed as negative values. the treatment resulted in an increased pavement roughness As illustrated in Figure 20, the post-treatment cracking as shown in Figure 21. 120 100 80 Friction 60 40 20 0 0 5 10 15 20 25 Age Do Nothing The Best Treatment Application Lower Benefit Cutoff Value Upper Benefit Cutoff Value Figure 22. Friction versus age for the most appropriate application age of 13 years for Case Study 1--Arizona.