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CHAPTER 4. RESULTS OF SMOOTHNESS DATA ANALYSIS Introduction This chapter presents the results of several analyses conducted on the effect of initial pavement smoothness and on the effect of smoothness specifications. The first part of the chapter summarizes the analyses of the effect of initial pavement smoothness on the future smoothness and life of the pavement. This is followed by a section discussing the effect of smoothness specifications on ~rutial pavement smoothness. The final part of the chapter describes a procedure for evaluating the cost effectiveness of initial smoothness levels and presents the results of an evaluation of the cost effectiveness of smoothness specifications. As discussed In chapter 3, the data for these analyses were assembled from several different sources, including existing data bases, literature surveys, and SHAs. The data from SHAs are believed to be the most representative and form the cornerstone for most of the analyses that were conducted. Analysis of the Effect of Initial Smoothness on Future Pavement Smoothness Introduction Very lithe research has been conducted on the effect of Crucial smoothness on the future smoothness of the pavement. Although He design equations developed from the AASHO Road Test (AASHO 1962) imply that initially smoother pavements maintain higher levels of rifle quality, little information is available outside of that work. However, one study reported by Janoff (1991) analyzed historical roughness data spanning 10 years and representing about 400 sections of roadway from Arizona and Pennsylvania. It was found that nutial smoothness is related to long-term roughness, as illustrated in figure 16 danoff 1991~. The smoothness values shown in figure 16 are in terms of the Mays Meter roughness Index, and it is observed that the range of values are rather small, indicating that most of the pavements used in the evaluation had not appreciably deteriorated. lanoff (1991) also determined that the annual maintenance costs were lower for those sections constructed with a higher initial smoothness, as shown in figure 17. While these findings are indications of He beneficial effects of initial pavement smoothness on the ride quality of He pavement, they are based on Innited data over a Ignited range of roughness values. Under this project, pavement projects from a wide range of data sources were used to further evaluate the effect of initial pavement smoothness on the future smoothness of the pavement. The projects used in the analyses include a myriad of pavement designs, subject to various climatic and traffic factors, and characterized by different roughness indices. A description of the data sources used in the analysis is provided In the following section. 41

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40 JO 20 10 o Long-Term Roughness (in/mi) - . - 1 . ' ' _ - - - - - - - - 0 10 20 30 40 Initial Pavement Smoothness (in/mi) Figure 16. Filial pavement smoothness versus long-term pavement roughness (Mays Meter roughness index values) Janoff 1991). 1500 1000 500 lo Average Annual Maintenance Costs ($/lane mile) / - - , .. . . - I . . . I . . , 1 10 20 Initial Pavement Smoothness finimi) 30 40 Figure 17. Initial pavement smoothness versus average annual maintenance costs Janoff 1991~. 42 1

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Overview of Data Sources Pavement projects from several of the data sources presented in chapter 3 were used to evaluate the effect of initial pavement smoothness on future smoothness. However, the vast majority of the projects analyzed toward this objective were those contributed by SHAs. Roughness data from the Alabama Pavement Roughness Study were also included with the SHA data for use as the centerpiece of the analysis. Roughness data from the various road tests and pavement performance studies were generally much less revealing than the State-furIiished data. This was largely attributed to the time period in which the studies were conducted (1950s and 1960s for most of the road tests), the inadequacy of the experimental designs (factors other than roughness [e.g., pavement design] were usually the main focus), and the lack of standard, reliable smoothness-measur~ng equipment and procedures used throughout the study. Consequently, outside of the SHA data, only data from the AASHO Road Test and the LTPP program were deemed worthy of detailed analysis and reporting. The evaluation results from these two sources are presented as a supplement to the State data evaluation results. Figure iS depicts the States contributing data used in the analysis of the effect of initial smoothness on future smoothness. In performing the analyses of roughness or ride quality over time, it was considered important that each pavement project consist of two or more adjacent "replicate" sections along a highway, as illustrated in figure 19. In this way, the effects of major factors, such as traffic and climate, are eliminated, and direct comparisons could be macle between sections with significantly different initial smoothness values. Table 9 provides a summary of the data sources used In the analysis, and indicates the number of replicate sections within each project. At He AASHO Road Test, for instance, a specific pavement design, subjected to a standard load (e.~. 6,000-Ib [26.7-kN] single-axIe truck load), was duplicated to form two replicate sections. Longer pavement projects, however, such as those from the States, provided numerous replicate sections, as they were broken down into short, uniform segments (typically 0.1 or ~ mi [0.16 or I.6 km]) defined by the available roughness testing interval. The preliminary analysis consisted of preparing time-series (or, in some instances load-series) roughness trends for each of the pavement sections from each data source. This was done by plotting the series of initial and extended roughness measurements for each replicate section within a project. The number of time-series data points varied by data source, as some sources provided sets of routine roughness measurements (e.g., Arizona, Kentucky), whereas others only provided two sets of roughness measurements (e.g., Iowa, RPPR). 43

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Description of Data Bases Agency Survey Data Bases Work on the agency survey data bases was initiated with the establishment of the NCHRP 20-7 task 53 State survey data base. The tables In this data base were structured such that States are listec! In the first column and the survey questions are listed, in order, in the column headings to the right. The State responses were then manually entered into the appropriate cells. Once He NCHRP I-31 State survey form was completed, the data base for this survey was fully structured using the same format as the NCHRP 20-7 survey data base. As State responses became available, the results were again manually entered into the appropriate cells. As part of the agency surveys, selected paving contractors were contacted in order to obtain the contractor's perspective on pavement roughness measuring procedures and practices. Both AC and PCC paving contractors were contacted. The same procedure for the clevelopment of the agency survey data base was followed for the contractor data base. Project Analysis Data Bases Work on the project analysis ciata bases began with the development of a list of anticipated data base elements. This list, shown in table 7, was prepared following the Strategic Highway Research Program (SHRP) LTPP Data Collection Guide (FHWA 1993), and contains the necessary data elements for conducting later analyses to address the following key project objectives: i. Determine the effect of initial smoothness on the ride quality of the pavement over its life and on pavement service life. 2. Determine the effect of existing smoothness specifications on He initial as- constructed smoothness. 3. Determine He cost-effectiveness of smoothness specifications, including incentives and disincentives. Sources of Data for Project Analysis Data Base Past Road Tests AASHO Road Test The AASHO Road Test represents one of the most comprehensive studies of pavement performance that has ever been conducted. Over 800 AC and PCC pavement study sections were constructed and evaluated from 1958 to 1960. 30

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Paving Project (Same design, traffic, subgrade, etc.) . . . Sect. Sect. 2 . Sect. 3 . Sect. 4 . , 1 , . . Initial Smoothness S1 S2 s3 s4 Time-Series T T T T Smoothness 1 2 3 4 Figure 19. Illustration of Replicate sections along a highway pavement project. As seen in table 9, some pavement projects were tested crucially win one particular roughness device and then tested at a later date with a different device. For those projects in which strong correlations existed between the two testing devices, conversions were made so that a single roughness index could be used to better reflect Me roughness trends. Specific examples of this occurrence include Me three {owe PCC projects (California profilograph used initially for construction acceptance, South Dakota-type profiler used later for pavement management purposes), and the five Minnesota PCC projects (GM-type profilometer used initially for construction acceptance, South Dakota-type profiler used later for pavement management). To some extent, Me ~rutial smoothness data collected represented tests performed within the first few weeks of construction. With several sources, however, such Initial data were not available, either because the construction records containing the test results had been discarded or because the ~rutial pavement smoothness was not being monitored by the SHA at that time. In those instances, pavement management smoothness measurements made within 2 to 3 years of construction were used as Initial smoothness values. Projects that fit this category include those from Illinois, Michigan, and Washington, as well as Minnesota AC and AC overlay projects. 45

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Table 9. Summary of data used to analyze the effect of ~rutial pavement smoothness on future smoothness. Number of | Basic Level of I Data Source AASHO Road Test WASHO Road Test San Diego Test Road _ Minnesota _ Investigation 183 Alabama Roughness Study RPPR LTPP GPS Arizona Georgia Illinois Iowa Kentucky Michigan Minnesota South Dakota Washington Wisconsin 1 ft = 0.305 m a For AASHO Road Test, WASHO Road Test, San Diego Road Test, Minnesota Investigation 183, RPPR, and LTPP GPS, a project is equated to a specific pavement design. Full-depth AC. c Time lapse between construction and initial measurement greater than 1 year but less than 3 years. _ Roughness Measuring Device(s) Pavement Replication a Projects Section No. of Pavement Examined Length, ft Sections Types Included Initial Future 32 flexible 1 1 on to '4n 1 2 36 rigid 10 flexible 120 to 240 300 _ 4 flexible 250 5 flexible 16 flexible 26 rigid 18 rigid 2 flexible 1 composite 3 rigid 5 rigid 3 composite 12 flexible 3 rigid 6 composite 9 flexible 37 rigid 4 composite 2 rigid 11 composite 7 flexible 6 rigid 9 flexible 5 rigid 8 flexible AC, JPC, JRC AC FDACD 700 or 1200 2,640 500 to 1,000 500 6 to 8 2 to 6 3 to 18 2 to 6 2 to 8 5250 5 to 14 5,280 528 - 5 _ _ 5,280 5 to 10 ~_ 6tol3 _ 5250 3 to l0 5230 5 to 24 _ 5,280 4 to 9 AC AC, PCC JPC, JRC, CRC AC, FDAC, JPC, CRC PSI (Chloe profiler) BPR Roughometer PSI (Chloe profiler) PSI (BPR Roughometer) . BPR Roughometer Various KJ Law Pro fit o me terC PSI (Chloe profiler) BPR Roughometer PSI (Chloe profiler) PSI (BPR Roughometer) BPR Roughometer Soup Dakota type Profiler KJ Law Profilometer JPC, AC, AC/JPC, AC/AC JPC, AC, AC/JPC, AC/AC CRC, AC/CRC AC/JPC JPC JPC, AC/JPC JRC, AC AC, _ I I I PCC-doweled 13 rigid 528 3 to 11 PCC PCC, AC, AC/AC JPC, JRC, AC CRC, AC/AC, AC/PCC 6 rigid 12 flexible 25 rigid 16 composite 30 flexible _ 5,280 3 to 9 e S;ttlO (typ) 3 to 23 Mays Meter Mays Meter BPR Roughometer California Profilograph PI (Mays Meter) GM ProfilometerC GM Profilometer (PCC) South Dakota-type Profiler (AC)C California Profilograph . PCA Roadmeter PSI (Mays Meter) Mays Meter Mays Meter South Dakota type Profiler South Dakota type Profiler RI (Mays Meter) GM Profilometer South Dakota type Profiler California Profilograph PCA Roadmeter PSI (Mays Meter) 46

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Some data sources, such as the AASHO Road Test, Kentucky, and Wisconsin, furnished serviceability-type data. These data took the form of serviceability or rideability indices, derived mostly from roughness measurements. The resulting time-series trends are a reciprocal of the roughness trends of other data sources, as lower index values represent rougher surfaces. Provided in appendix C are pavement project summary tables and time-series roughness plots of each of the sections evaluated in this study. The tables and plots are arranged according to data source. The summary tables include useful information about each pavement project, including project identification, location, design, construction year, evaluation period, and traffic estimates. The plots represent the roughness trends of the various fixed-interval sections that comprise a given pavement project. It should be noted Hat not all of the projects for which data were collected are included In the summary tables and roughness plots. Several projects were identified as having extended smoothness measurements, but no initial measurements (within 3 years of construction). This was the case for the majority of the Michigan projects, where only 15 of the 47 subject sections could be analyzed because of the lack of Initial measurements. Another reason for the exclusion of some projects was the inability to closely align (by milepost, reference point, or station) ~rutial smoothness measurements with extended smoothness measurements. This problem was encountered with some of the Minnesota and Washington projects. Overall Evaluation of Effect of Initial Smoothness Using SHA Data This section describes the results of the overall evaluation of He SHA data. The data sources described in the previous section are analyzed over He entire period for which smoothness data are available to determine whether He ~rutial smoothness of the pavement influences the future smoothness of He pavement. Evaluation Approach An examination of the t~me-series smoothness plots (contained In appendix C) indicate the following: 1. Pavement sections built smoother generally remain smoother over time (all over Wings being equal). 2. The performance curves of two pavements constructed to different initial smoothness levels but of otherwise similar design roughly "parallel" each other, with the rougher section perhaps deteriorating more rapidly due to greater dynamic loadings and/or greater variability in construction. An example chart illustrating the "parallel curve" concept is shown in figure 20. Sections I, 2, and 3 all roughly parallel one another, with section 3, constructed 47

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In a) s o Section ~ ~ - Section 2 Time or Traffic Figure 20. Example illustration of pavement performance curves for sections along a given construction project. Initially smoother, remairung smoother over time (or traffic). Section I, constructed rougher than Me other two sections, shows perhaps a slightly greater rate of deterioration. As discussed previously, pavement projects were solicited from SHAs containing multiple sections constructed to different Crucial smoothness values. Because the pavement design, cross section, subgracle support, traffic loadings, age, and climatic forces are approx~nately We same for each of the sections within Me project, the effect of these variables on the performance of all of Me sections is assumed to be constant and Me effect of nutial pavement smoothness can therefore be isolated. For each project containing multiple sections, a statistical regression analysis was conducted relating the smoothness at any time to Me initial smoothness and to the age, as expressed in the following general form: St = aO + a~Si + a2t where: SO = Pavement smoothness at time t. aO, al, a2 = Regression coefficients (at is Me regression coefficient on Si). Si = Initial pavement smoothness. t = Verne (age), years since construction or overlay to time of smoothness testing So. 48

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For the purposes of this evaluation, a multiple linear regression analysis was conducted based on observations of the trends shown in the plots. A linear mode] was selected in order to provide an Indication of the significance of the initial smoothness variable. In many cases, nonlinear regression provided a better "fit" of the data, and these improved moclels were used in the analysis on the effect of initial pavement smoothness on pavement life (described later). The output of the linear regression provides the regression coefficients and information on the statistical significance of the independent variables (in this case, initial smoothness and age) on the dependent variable (smoothness at time t). The regression coefficient al is the slope of the So versus Si regression line. If this constant is approximately 1.0, this indicates that a strong one-to-one relation exists between the initial smoothness and the future smoothness. This means that if one section is, say, 10 in/ml (0.16 m/km) smoother at initial construction Man another, then the future smoothness of that section will remain 10 Mimi (0.16 m/km) smoother than the other section over time. If the regression coefficient al is zero, this indicates that future smoothness is not dependent on initial smoothness. This concept is illustrated In figure 21. - T ._ _, Ct to In s o ~ alma /- ,/ al= 1 al=0 , . ~. _ _ _ ~ 1 , 1 , 1 , Initial Roughness Figure 21. Effect of a, regression coefficient In the relationship between Crucial smoothness and future smoothness. 49

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Another way to look at the meaning of Me a, coefficient is to consider its effect In the time-series plots of pavement roughness. Figure 22 illustrates the physical significance of the al coefficient for four different cases, assuming four sections of a pavement project with different ~rutial pavement smoothness values. In the first case, when the al coefficient is zero, the Initially smooth and initially rough sections converge at some point within the data range, and no relationship exists between the initial smoothness and the future smoothness. (It is ~rnportant to note that the slopes of the lines shown in figure 22 are indicative of the relationship between time bagel and future smoothness, and not between initial anc! future smoothness.) In case 2 of figure 22, when the al coefficient is between O and I, the initial smoothness does have an effect on the future smoothness; however, the initially smooth and initially rough sections tend toward convergence, but not within the available data range. In other words, the initially smooth and ~rutially rough sections will exhibit the same level of smoothness at some point In the future, but that future point could be 5 years or it could be 25 years. Case 3 of figure 22 shows the effect when the al coefficient is equal to 1. In this case, the smoothness of Individual sections parallels one another, with initially smooth sections remaining smoother over dine. Finally, case 4 of figure 22 illustrates that when the a, coefficient is greater than I, the initially smooth and initially rough sections tend to diverge over the available data range. This would suggest that dynamic loading effects are causing the roughness of the initially rougher sections to increase at a more rapid rate. Tests for the statistical significance of the regression coefficients of We independent variables (Si and t) are also outputs of the regression analysis. The statistical significance of Me independent variables is evaluated using the p-value, which indicates the probability that the significance of the effect of the independent variable fin this case, initial pavement smoothness) on Me dependent variable (future pavement smoothness) is due to chance alone. Obviously, the smaller the p-value, the stronger the indication that the independent variable (initial smoothness) has a truly significant effect on the dependent variable (future smoothness). For this evaluation, a significance level of 10 percent was selected, meaning that if the p-value of the nutial smoothness regression coefficient balk is less than 10 percent, the results are considered significant. An example plot showing regression results is provided in figure 23 for a PCC construction project in Georgia (~-575 NB, Cherokee and Pickens Counties). This figure shows the pavement roughness at year ~ as a function of the initial roughness. The results of the regression indicate a good correlation (R2 = 0.82) for the linear model, and Me a, value of 0.89 demonstrating a strong relationship between the initial pavement roughness at construction and Me pavement roughness at year 8. For this project, each point on the graph is a I-mi (~.6-km) section along the length of the construction project. 50

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~a) o Act 3= O G 0 ~ can Cat t~ m O=' U:) ~ 0 0 E cn ~ cad co ~ ~ t ~ ~ ~ ~ ~ ~ ~ ~ ~ . -.,\ T~ 8 = ~ ~ ~ .4pt, . ~ A\ ., ., ., ., ., .\ A\ Iw/ul 'xapul sseu46noj _ ~ ~ ~ ~ At t; ~6 ~6 lime V.. Ace bile E an lo In E o lo C~ E F U. ' ' ' ' :~o !UlIU! 'xapul ssauq6no~ ~ c ~co ~ au O t 0 O ~ O ' ~., ~ 1 O: a) u, u' cn cn ._ cn 0 _ A ~ ~ _ s ~ o Ct ~ E tt cn ~ ~ C~ C,0 O o - cn ` \ . ~ ' \ ~ ; \ he ~ -~N . e~ ;` '~ ~ . a~ ~ . '5 ~ ` ~ \; V '"~e \~\ '., C~ U) o ._. \ \ o C~ E u, a O ~ o .~ a~ ._ ~ 0 + 03 ' (,0 1 (0 ~ 11~=,~ ~ ~ 0 .. ~ ~ ~:,~\ 0 ~ ~ e tV ~ ,= E 0 cn ~w/u! 'xapul ssau46no~ 'eD '~0 *~\ ., ., ., ., . ,. ,` ;, ~u'IUl 'xapul ssau46no~ . 1 . 51

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Table 38. Comparison of current and hypothetical pay adjustments for State 4 PCC. . Pay Factor Corresponding to: Maximum Incentive ~ | | Maximum Disincentive PI=0 inlmi PI=7 in/ml ~ PI=20 inlmi = 1.05 Pavement Family Full-Pay Level State 4 PCC Current LeKI PI=7 inimi New Level Option 1 PI=5 in/ml 1.10 New Level Option 2 PI=3 in/ml 1.00 0.90 0.95 j 0.33 0.89 0.28 l ' 1.05 1 in/ml = 0.016 m/km As discussed earlier, the SHA should be willing to pay the cost of building a pavement to a nominal smoothness plus any benefits of building it to an even smoother level. Although significantly greater pay adjustments, like those shown in figures 83 through 85, might seemingly entice many contractors to build to O in/ml (O m/km), contractors must take into consideration their cost of Paining additional smoothness. Hence, the level to which Hey build will be dictated by the difference between the anticipated incentive and the ASC. Contractors will build only to the level where He incentive still exceeds the ASC, for as one contractor pointed out In his response to the first contractor survey, "No contractor will pay more to get less." By subtracting the ASC in He numerator of He pay factor equation (equation 9), as shown below, the contractor's perspective on what smoothness level to build at can be better understood. PFaC = [BPa~ + (Lccad. ~ LCCaC)-ASC]/BPad (9) where: PFac = Pay factor for as-constructed smoothness level. BPad = Bid price corresponding to as-designed, or target, smoothness level, $. LCCad = PW life-cycle cost of maintenance and rehabilitation corresponding to as-designed smoothness level, $. LCCac - PW life-cycle cost of maintenance and rehabilitation corresponding to as-constructed smoothness level, $. ASC = Additional smoothness cost, $. The resulting pay factor curve indicates two things to the contractor: the break- even smoothness level and the most profitable smoothness level. These levels are illustrated in figure 87. At the break-even smoothness level, the cost of additional smoothness equals the incentive payment, and the resulting pay factor is 1.0. At the most profitable smoothness level, the difference between He incentive payment and the ASC is greatest, and the resulting pay factor is represented by the maximum 152

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Con~actor's view of pay factor curve (with | consideration of AS C) Pay factor curve 1.05 - _.................................... 1.00 0.95 0.90 0.8= , , ... Hi,, _ - . ~ I' _ : Contractor s cost of ............ . . . ...... . additional smoothness ............ . . . ...... . . (ASC) . - , ~._ ~ e.~. \' At' 2 ...................... ;,,.. 1 a'_ Frost profitable "Break-even" smoothness levet .. smoothness level , _ . : : 4 4.5 5 Initial RI Figure 87. Illustration of contractor's perspective on pay adjustments (based on analysis of State 3 PCC parkway pavements). point on the contractor pay factor curve. It is exactly at this point that the rate at which additional incentive money is obtained becomes less Man the rate at which additional smoothness costs are incurred. Inclusion of User Costs in [CCA The inclusion of user costs in a LCCA is a controversial discussion topic in the transportation community. There is a general consensus that user costs must be included when conducting a LCCA; however, there is much controversy on what user costs should be included, how they should be calculated, and how they should be used. Although most States feel the LCCA should include user costs, there is a need for guidelines on how to accomplish this {ask. The LCCA performed up to now have only considered initial construction and future rehabilitation (overlay only) costs. However, because many people believe that user costs should be included in a comprehensive LCCA, an attempt was made in this study to include the effects of user costs as a function of initial serviceability. Although any estimate of user costs is a crude approximation, the magnitudes of the estimated costs presented in this report are believed to be representative. A discussion of this user cost investigation is provided below. 153

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User costs were estimated based on results reported by McFarland (1972~. Four different types of user costs were considered in that investigation, consisting of lime (delay), vehicle operating, accident, and discomfort costs. All four were evaluated In terms of cents per vehicle mile as a function of serviceability (PSI) and the following six highway types: I. Rural 2 lane. 2. Rural- ~ lane, undivided. 3. Rural ~ or more lanes, divided. 4. Urban 2 lane. 5. Urban ~ lane, undivided. 6. Urban ~ or more lanes, divided. The cost tables provided In McFarIand's 1972 report were updated to 1996 costs using an annual Inflation rate of 5 percent. Because user costs are based on the number of vehicles, traffic data were required for the analysis. Average daily traffic (ADT) data was plotted versus time for a particular project. A polynomial regression equation was fit through the ADT versus time data In order to establish a reasonable relationship to serve as a means of extrapolating future traffic over the analysis period. The developed best-fit mode} (a function of Crucial smoothness and limed was used to predict the future roughness on a year-by-year basis for different Initial smoothness levels. Rehabilitation (for simplicity just the placement of an AC overlay) was applied when Me serviceability reached a level of 2.75 (as determined from the predicted-life-versus-initial-smoo~ness curse developed for each pavement type in each State). Overlays were assumed to last 10 years and deteriorate linearly from the target smoothness level to We trigger smoothness level over time. The appropriate cost per vehicle mile is obtained from Me updated cost tables by knowing the projected pavement smoothness and identified pavement type. Yearly user costs are calculated by multiplying the appropriate cost per vehicle mile tunes the projected number of vehicles traveling on the project for a given year (365 days times the projected ADT). For a given project, the cumulative user costs for different initial smoothness levels can be plotted versus time. For this Investigation, two specific Interstate pavement projects were evaluated. These projects consisted of one AC and one PCC project each located In Me same State and built In the mid 1970s. ~- , - - , Figures 88 and 89 show the cumulative PW user costs versus time for these two projects, respectively. Both graphs include all four types of user costs (i.e., time, vehicle operating, accident, and discomfort). Because of the magnitude of these costs, it is difficult to get a clear idea of the cumulative PW dollars saved over time due to 154

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80,OOO,oOo 70,000,000 - - ~60,000,000 c lo, ' 50,000,000 40,000,000 30,000,000 1 O,000,000 60,000,000 a,, 50,000,000 ~40,000,000 o U u, 30,000,000 20,000,000 - - U 10,000,000 o Included User Costs: Time, VOC, Accident, Discomfort. Trigger of 17o in/mi, and a target of 60 in/ml were used. . ~ .-~~ T ,~ l I'> it, A' A' it,' O- , 1 1 0 5 1 0 15 20 25 30 35 40 45 50 Age (years) Figure SS. Cumulative PW user costs versus time for selected PCC pavement project. Included User Costs: Time, VOC, Accident, Discomfort. Trigger of 175 ~n/mi, and a target of 50 in/ml were used. 4,7# 0 5 10 15 20 25 30 35 40 45 50 Age (years) Figure 89. Cumulative PW user costs versus hme for selected AC pavement project. 155 O in/ml 60 in/ml (target) ---90 in/ml O in/ml 50 in/ml (target) ---70 inlmi

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changes in initial smoothness. However, an estimate of the savings associated with a given initial smoothness level as compared to a target smoothness level can be determined using the following equation: SAV~GSX~ = CPWUCTARGEr~-CPWUCX(Y) (10) where: SAV~GSxc$' = Savings in cumulative PW user cost for a given initial smoothness level (X) at a particular year (Y), $. Cumulative PW user cost calculated for the target ~rutial smoothness level at a particular year (Y), $. CPWUCx = Cumulative PW user cost calculated for the given initial smoothness level (X) at a particular year (Y), $. In the case of the PCC project, table 39 contains values of actual yearly user costs, PW yearly user costs, and cumulative PW user costs for initial smoothness levels of 0, 60 (target), and 90 in/ml (0, 0.95, and 1.42 m/km). These three levels correspond roughly to California PI values of 0, 7 (target), and 15 in/ml (0, 0.11, and 0.24 m/km). Figure 90 illustrates the savings in cumulative PW user costs over time for the three different initial smoothness levels, as compared to the yearly values at the target initial smoothness level. For example, at year 15, the cumulative user costs (taken from table 39) for the three initial smoothness levels are as follows: Level 1: 0 in/ml (0 m/km) = $22,994,ooo Level 2: 60 in/ml (0.95 m/km) = $24,883J000 (target level) Level 3: 90 in/ml (1.42 m/km) = $26J143,OOO Therefore, the respective savings in cumulative PW user costs at year 15 (compared to the estimated value at the target of 60 in/ml (0.95 m/km)) are calculated as follows: Savings at O in/ml (0 m/km) => $24,883,000 - $22,994,000 = $1,889,000. Savings at 60 in/ml (0.95 m/km) => $24,883,000 -$24,883,000 = $0. Savings at 90 in/ml (1.42 m/km) => $24,883,000 - $26,143,000 = -$1,260,000. These three respective savings are plotted at year 15 in figure 90. It should be noted that the irregularities seen in figure 90 are due to the applications of overlays at different years for different initial smoothness levels. These different overlay schedules result in different rates of increasing cumulative PW user costs (see table 31 for further explanation of specific values and their interactions). By incorporating into the LCCA the cumulative PW user costs associated with different initial smoothness values and corresponding to a specified analysis period, a comprehensive graph of "total life-cycle costs plus user costs" versus initial smoothness can be developed. Figures 91 and 92 illustrate such graphs for the two subject projects. As can be seen in each of these figures, three different versions of 156

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Age (years) 1 2 -3 4 5 6 8 10 11 2 13 14 ~15 16 17 is 19 20 2 22 23 24 25 26 27-- 28 . 29 30 31 32 33 34 35 36 . 37 38 39 40 41 42 43 . 44 45 =46~_ _47 _ 48 49 50 . Table 39. Actual yearly, PW yearly, and cumulative PW user costs for different Crucial smoothness levels ($ thousands). Yearly User Cost Per Mile 1,554 1,636 1,718 1,800 1 882 1964 2,046 2,128 2,210 2,292 2,374 2456 2,538 2,620 2 702 2,784 2,866 2,948 3,030 3,112 3194 3,276 3,358 3,440 3,672 3,811 3,982 4,150 4,315 4491 4,664 4,832 4,995 5,150 4527 4679 4,868 5,053 5,234 5,427 5,617 5800 , 5,975 6,142 5,381 5,546 5,754 5,956 6,153 6,364 Yearly PW User Cost Per Mile 1,494 1,512 1,527 1,539 1 547 1 552 1,555 1,555 1,553 1,548 1,542 1,534 - 1,524 1,513 1500 1,486 1 471 1,455 1,438 1,420 1401 1,382 1,362 1342 1,377 1,375 1,381 1,384 1,384 1 385 1 383 1,377 1,369 1,357 1 147 1,140 1,141 1,138 1,134 1,130 1,125 1 117 1,106 1,093 921 913 911 907 900 895 ~ Represents year of overlay. Cum. PW User Cost Per Mile 1,494 3,007 4,534 6,072 7619 9171 10,726 12,281 13,833 15,381 16,923 18 457 19,981 21,494 22994 24,481 25 952 27,407 28,845 30f265 - 31 666 33,049 34,411 35,753 37,130 38,505 39,886 41,270 42,654 44038 45,421 46,798 48,167 49J5254 50 672 , 51,812 - 5-2,952 54,091 55,~5 56,355 -57,48 - 0 58,597 59703 , 60,797 61,718 _ -62,63 63 542 . , 64,448 65,349 66,244 ~ Yearly User | Cost Per L Mile 1,619 1,711 1,805 1,900 2,002 2108 . ~ 2~16 2,326 2,438 2,551 2,666 2,788 2,911 2,731 2,858 3 008 . ~ 1 3157 . , 3,304 3,461 3,616 3 768 . ~ 1 3 916 . , 4,060 3,586 3,725 3,893 4,060 4~3 1 4,397 1 4 569 . , 4,735 4,897 5,051 4,441 4,592 4,779 4,963 5,142 5,334 5,522 5,703 5877 , , 6,042 5296 5,459 5,665 5,866 6,061 6,270 6,474 157 . Yearly PW User Cost Per Mile 1, 5 5 7 1,582 1,604 1,624 1,645 1,666 1,684 1 700 1,713 1,723 1,732 1,741 1 748 1,577 1,587 1 606 1621 1 631 1,643 1,650 1,654 - 1,653 1,647 1,399 1,397 1,404 1,408 1,408 1,410 1 409 1,404 1,396 1,384 1,170 1,164 1,165 1,163 1,158 1,155 1,150 1,142 1132 1,119 943 935 933 928 923 918 911 _ Cum. PW User Cost Per Mile 1,557 3,138 4,743 T 6,367 8,012 9,678 T 11362 , ~ 1 13061 , ~ 1 14,774 1 16 498 , , 18,229 9,971 21719 , , 1 23,296 24,883 ~26,489 28 109 , , 1 29740 , , 1 31,383 T 33,033 1 34 687 , , 1 36 339 , , 37,986 39,386 4~0,783 42,187 43,595 1 45,003 r 46413 , , 1 47 822 , , 49,226 50,622 52,0~06 53,177 54,340 55,505 56,668 57,826 58,982 - 60,132 61,274 62,406 63,525 64,467 65,402 66 335 , , 67,263 68,186 69,103 70,014 ~ Yearly User| Yearly PW | Cum. PW | Cost Per | User Cost | User Cost | Mile ~ Per Mile | Per Mile 1~774 1 1,706 1 1,706 _1,878 1 1,737 1_ 3,443 1,985 1 1,765 1 5,207 2,094~ -1,790-r- 6,998 2,205 ~1,812 T 8,810 2,318 11,832 ~10,642 2 432 11 848 ~12 490 , . .. . . 2,547 11,861 L 14,351 2,304 11619 1 15970 , .. . . 1 2424 11638 1 17607 , , I, , , 2,565 11,666 1 19,273 2,705 11,690 1 20,963 r ~2,845 T 1,7081-- 22,671 2,992 1 1,728 1 24,399 1 3,140 1 1,743 1 26,143 L_ 3,284 T 1,754 1 27,896 r ~ 3,426 1 1,759 1 29,655 3,564 1 1,759 1 31,415 1 ~ 3,159 1 1,499 1 32,914 r 3,?91T 1,5-02~ --34,416 rim 3,441~ ~1,514 1~_~35,930 r36081 15231 37453 ,. . . . . 13,764 1 1,527 1 38,980 1 .- . 3~929 1 1,533 1 40,513 4,092 1 1,535 1 42,048 14,2521 1,534 r 435811 4,407 1 1,528 1 45,110 14,555 1 1,519 1 46,6296 I~ 4014 1 1,287 1 479161 I~ 1 1 ~ 14,158 1 1,282 1 49,198 1 . i4,336 1 1~286 1 50,483 --4,511 ~1,286 1 51,769 4,683 ~1,284 1 53,053 ,866 t 1~2821 54,335 15,045 1 1,279 1 55,614 i5,219 1 1,272 1 56,886 5,387 1 1,262 1 58,1480 5,547 1 1,250 1 59,397 4,869 1 1,055 1 60,452 5,026 1 1,047 1 61,499 1 ~ Owl ~:~ 1 5,414 r 1,043 1 63,587 1 . . , 1 5,602 1 1,037 1 64,625 1 1 5,802 r-10331 656-581 5,99~1__~ 1,027 l 66,684: 6 187 T 1 018 1 67 703 , . . . . . 6,367 1 1,008 1 68,711 6,53-8 T 9951 69,706 *5,723 T 838 1 70,543 5,893 1 829 1 71,372

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.,, 3,000,000 ._ OJ c~ v 2,000,000 U v' 1 ,000 ,000 ._ - U u, ._ U. -1 ,000,000 -2,000,000 ~. ~ _ ~.. .... ,\.. _ 4;0 ~ ; 2 ~Nf, v I T I I I Included User Costs: Time, VOC, Accident, Discomfort. , . . ~ . ., ~ r ~ ~ ~ ~ r ) 2 5 ~, Age (years) , , ~0 s e Figure 90. DIustration of sav~ngs ~n cumulative PW user costs over fime. so,ooo,ooo - . - . . . . . 1 1 1 1 1 70~000tO00 ~- ~ - ; . .. ................................ 60,OOO,OOO 6~ - U 50J000~000 - U 40,000,000 30,000,000 20,000,000 10,000,000 +O in/ml ~60 in/ml (target) ~90 in/ml 1 _ _ _ ~ 0 20 40 60 80 100 120 140 Initial Roughness (MRN), in/ml Total LCC Total LCC + VOC ~Total LCC + Time Delay + VOC Total LCC + Time Delay + VOC + Accident + Discomfort Figure 91. Graph of total LCC (including user costs) vs. ~nitial roughness for selected PCC pavement project. 158

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4D,000,000 1 40,000,000 ~ '_ -A 3~,000,000 30,000,000 25,000,000 20,000,000 15,000,000 10,000,000 5,000,000 0 20 40 60 80 100 120 140 Initial Roughness (MRN), in/ml | Total LCC | TotalLCC +VOC Total LCC + Time Delay + VOC . Total LCC + Time Delay + VOC ~ Accident + Discomfort Figure 92. Graph of total LCC (including user costs) vs. iriitial roughness for selected AC pavement project. user costs were depicted; the top curve representing all four user cost components, the curve below that representing two user cost components twine and vehicle operating), and the one below Cat representing only vehicle operating costs. The total life-cycle cost curve, cor~sist~ng of initial construction cost and future overlay costs, is located close to the x-axis. The effect of including user costs In the LCCA is obvious in both of these figures. User costs so overwhelmingly dominate the project life-cycle costs that the least overall cost occurs at the smoothest possible level, which is a MRN of O m/ml (O m/km). Although other projects were not analyzed, variations in savings from project to project would be expected, primarily due to factors such as the smoothness-life relationship and traffic. Nevertheless, it is strongly believed that the magnitude of savings associated with smoother levels for other projects would stay relatively uniform, such Cat Me smoothest level is always identified as Me most cost- effective level. Summary This section outlined a procedure for evaluating the cost-effectiveness of initial smoothness levels and presented a detailed evaluation of several current pavement smoothness specifications using that cost-effectiveness procedure. The evaluation procedure introduced is based on LCCA techniques and is contingent on the 159

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inclusion of relationships between initial smoothness and initial construction cost and between initial smoothness and pavement life. The evaluation of current smoothness specifications focused on two aspects: the optimum cost-effective smoothness level and the appropriateness of ~ncentive/dis~ncentive payment amounts. Asphalt and concrete specifications from five different States were evaluated using LCCA techniques, contractor estimates on the cost of obtaining additional smoothness, and the smoothness-life relationships developed earlier for various pavement families (i.e., combinations of State and pavement type). Some of the key findings of the smoothness specification cost-effectiveness evaluation are as follows: . Seven of rune concrete pavement families showed Me optimum cost- effectiveness (Pl) range, excluding consideration of user costs, as being between O and 5.5 in/ml (O and 0.09 m/km). Four of five asphalt pavement families showed the optimum cost-effectiveness (Pl) range as being between O and 3.5 in/ml (0 and 0.06 m/km). Eleven of 13 asphalt overlay families showed Me optimum cost-effectiveness (Pl) range as being between O and 2 in/ml (0 and 0.03 m/km). In comparison with actual current pay adjustment curves, the theoretical pay acljustment curves, on the whole, showed that greater incentive and disincentive amounts are warranted in terms of the benefits/disbenefits obtained from various initial smoothness levels. When shifted to coincide with full-pay (Pl) smoothness levels of 5 and 3 in/ml (0.08 and 0.05 m/km) for PCC and AC pavements, respectively, the recalculated theoretical pay adjustment curves still showed greater magnum Incentive amounts and more punitive disincentive amounts in comparison with the current pay adjushnent curves. The inclusion of user costs in a comprehensive LCCA has a profound effect on the determination of the most cost-effective smoothness level. For two selected pavement projects, a comparison of cumulative PW user costs associated with Tree distinct crucial smoothness levels (MRNs of 0, 60, and 90 in/ml [0, 0.95, and 1.42 m/km]) showed significant cost savings for Me pavement constructed to the smoothest level. For both projects, Me addition of user costs to Me project costs (construction cost plus future overlay costs) clearly dominated the total LCC such that the most cost-effective smoothness level was found to be 0 ~n/mi (0 m/km). Overall Chapter Summary This chapter presented the results of several analyses conducted on the effect of Initial pavement smoothness and of pavement smoothness specifications. The results of the analysis of the effect of initial pavement smoothness on the future smoothness of the pavement clearly show that nutial pavement smoothness has a significant effect 160

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on the future smoothness of the pavement. This analysis was conducted for different pavement types and for different age ranges, with the results being that initial pavement smoothness is significant to the future smoothness of the pavement. The effect was stronger for new pavement construction than for overlay pavement construction, suggesting that the performance of overlays is governed more by other factors (e.g., reflection cracking). These results were conducted on many sets of independent pavement performance data from across the US and over different pavement types. Analyses were also conducted to investigate the effect of Crucial pavement smoothness on pavement life. The results of the analysis strongly indicate that initial pavement smoothness has a significant effect on pavement life, using both roughness mode! and pavement failure analysis techniques. The analyses show that added pavement life is obtained by achieving a higher level of initial smoothness over the range of initial smoothness values that were available for analysis. The rate at which additional life is achieved is dependent upon, among other things, pavement type, facility type, and location. Sensitivity analyses, in which the percentage change in life as a function of percentage change in smoothness was determined, showed sizable increases In life for most pavement families, corresponding to nominal increases in smoothness. At least a 9 percent increase In life corresponding to a 25 percent increase In smoothness (from a target profile index of 7 in/ml [0.~l m/km] for concrete and 5 in/ml [0.08 m/km] for asphall) was observed for the vast majority of the pavement families. A 50 percent increase in smoothness from these target levels was found to Increase life by at least 15 percent ~ many cases. Me results of Ads analysis are based on the assumption that roughness is a primary factor influencing the decision to rehabilitate a pavement. An analysis of the effect of pavement smoothness specifications on the resulting levels of initial pavement smoothness was also conducted. This analysis indicated that pavement smoothness specifications do have a beneficial effect on the resulting initial smoothness of the pavement. Comparison of pavement smoothness distributions before and after the implementation of a smoothness specification indicate decreased initial roughness values and decreased variability for pavements constructed under the smoothness specification. A procedure for evaluating the cost-effectiveness of initial smoothness levels was developed and presented. The procedure illustrates how the life-cycle costs of a pavement constructed to different smoothness levels (under various incentive/disincentive scenarios) can be determined and Men compared to see which level is most cost-effective. Such an evaluation was conducted on several pavement families, and the results showed Mat Me most cost-effective smoothness (Pl) levels are considerably higher than what is typically used as the current target (PI between 5 and 10 in/ml (0.08 and 0.16 m/km. Seven of nine concrete pavement families showed the optimum cost-effectiveness range as being between O and 5.5 ~n/mi (0 161

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and 0.09 m/km), whereas four of five asphalt pavement families showed the optimum range as being between O and 3.5 Mimi (0 and 0.06 m/km). In comparison with actual current pay adjustment curves, the theoretical pay adjustment curves developed in this study showed, on the whole, much greater incentive amounts and much more punitive disincentive amounts. When shifted to coincide with full-pay (Pl) smoothness levels of 5 and 3 in/ml (0.08 and 0.05 m/km) for PCC and AC pavements, respectively, recalculated theoretical pay adjustment curves stiD showed greater maximum incentive amounts and more punitive disincentive amounts than current pay adjustment curves. The inclusion of user costs in a comprehensive LCCA has a profound effect on the determination of the most cost-effective smoothness level. For two selected State pavement projects, me addition of user costs to total life-cycle costs resulted In O n/mi (O m/km) as being the most cost-effective smoothness level. Taken together, the implications of these findings are very significant. Initial smoothness has been shown to provide value in the performance and service life of pavements. This serves to justify the importance of achieving high initial smoothness from a pavement structural standpoint, which is in addition to the importance of smoothness to the nding comfort of the user. And, this strongly supports the benefits of employing smoothness specifications, which have been shown to be an effective means of achieving higher levels of initial smoothness. 162