Performance-Related Specifications for Pavement Preservation Treatments (2017)

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104 Joint Resealing The steps presented in Chapter 5 were followed to develop an example for joint seal/reseal treatment in rigid pavements. 1. Select a Preservation Treatment Joint resealing and crack sealing of PCC pavements are performed to prevent (1) the infiltration of moisture into the pavement structure, and (2) the accumulation of incompressible materials in discontinuities. The intent is to minimize the development of moisture-related distresses such as spalling or faulting (Peshkin et al. 2011). Joint resealing requires removal of the existing deterio- rated sealant and installation of new sealant material. 2. Select Candidate Material and Construction Characteristics and Performance Measures The material and construction variables that can be used as AQCs for joint resealing include • Sealant type: adhesiveness and cohesiveness properties, durability, extensibility, resilience, and curing time • Pre-formed sealant placement configurations: reservoir configuration is dependent on the sealant material used, anticipated subsequent or simultaneous treatments, and aesthetic considerations • Effectiveness of resealing: sealant or filler material failure caused by improper installation. Common performance measures addressed by joint resealing follow: • Faulting, pumping, spalling, cracking: sealant keeps moisture and incompressible materials out of the pavement structure and discontinuities which mitigates faulting, spalling, pumping and cracking in rigid pavements • Sealant weathering: oxidation, weathering of sealant surface, cracking, surface erosion, debris intrusion • Cohesion/adhesion failure of the sealant: a cohesive failure can occur as the sealing splits par- allel to the joint and adhesion failures can also occur between the face of the slab and sealant. The material, cohesion, and adhesion properties of sealants influence the performance of joint reseal and its effectiveness. Joint seal/reseal effectiveness is a measure of how the percentage of the sealant can prevent the infiltration of water and fines into a joint. An FHWA manual of practice suggests evaluating joint reseal performance by measuring the resistance of the sealant to intru- sion of water and debris. The joint reseal effectiveness can be determined by using Equation A-1 (Evans et al. 1999). Examples A p p e n d i x A

Appendix A 105 % 100 (A-1)L L L fail f total = × where %Lfail = percent length of a joint allowing water to enter Lf = total length of a joint allowing entrance of water Ltotal = total joint length evaluated These percentages can be determined through a visual examination or using devices such as the Iowa vacuum (IA-VAC) tester, developed by the Iowa DOT (Evans et al. 1999). Equation A-2 estimates joint seal effectiveness (%Leff) as the ratio of the percent of joint adequately covered by sealant to the total length of the joint. % 100 % (A-2)L Leff fail= − where %Leff = percent joint seal effectiveness %Lfail = percent length allowing water to enter joint An individual joint is considered serviceable or “effective” when %Leff is at least 75% (FHWA 1999, Smith and Romine 1999). For an entire pavement section, FHWA suggests a minimal overall allowable effectiveness level of 50% (Evans et al. 1999, FHWA 1999) which indicates the need for joint reseal treatment. In other words, a pavement section is a candidate for sealant repair when only 50% of sealed joints remain with a %Leff of 75%. This minimum allowable level can vary by agency practices. These criteria were used for developing the PRS guidelines for joint resealing in PCC pavements. Joint reseal effectiveness (%Leff) was used as an AQC, given that it is associated with moisture-related performance measures such as faulting and pumping. 3. Establish AQC-Performance Relationships and Performance Limits Typically, an agency should develop a performance-based relationship for joint resealing by measuring joint effectiveness over time for several in-service pavement sections in order to exam- ine performance trends in the field. For this example, the joint deterioration data for several pave- ment sections were simulated, based on the trends presented in the FHWA Manual of Practice of joint seal repair (Evans et al. 1999, FHWA 1999). The trends show examples of deterioration in the percentage of effective joints in several pavement sections (i.e., percentage of joints in a section measuring Leff of 75%) ranging from 100 to 20% effective joints from the start to after 7 years, respectively (Evans et al. 1999). These ranges were used to simulate joint reseal effectiveness over time for multiple sections as shown in Figure A-1. Each of the sections shown in Figure A-1 was assumed to have measured percent effective joints and faulting values each year. For this example, a relationship between the percent of effective joints and faulting was simulated as shown in Figure A-2. Faulting at various joint effectiveness levels for each section was predicted by using this relationship (see Figure A-3). The effect of change in AQC (percent effective joints, %Leff) due to the treatment (joint reseal- ing) on after-treatment condition for each section needs to be determined. In this example, Section 1 deterioration was assumed to be the required after-treatment performance. An agency can set its own target after-treatment condition based on its local experience. The performance of each pavement section was compared with the performance of Section 1 to evaluate the effect of the change in AQC (%Leff) due to joint resealing.

106 performance-Related Specifications for pavement preservation Treatments Figure A-3. Joint faulting over time for PCC pavement sections. Pe rc en t o f “ ef fe ct iv e” jo int s Age (years) Figure A-1. Deterioration of percent of effective joints on PCC pavement sections. Fa ul tin g (m m) % of effective joints in a pavement section Figure A-2. Relationship between percent effective joints and faulting.

Appendix A 107 The change in performance was quantified using the SLE, which is the difference in the time between before and after treatment to an established threshold for faulting. The SLE can be determined by using Equation A-3. (A-3)% 98% %SLE t tLeff Leff n= −= = where t%Leff=98% = time-to-threshold for after-treatment faulting using initial %Leff of 98% t%Leff=n = time-to-threshold for before treatment faulting using varying initial %Leff To determine SLE, a faulting threshold of 6 mm was used in this example. Each pavement section’s performance (i.e., Sections 2 through 5) was compared to Section 1 performance to calculate SLE. Figure A-4 shows an example calculation for Section 2. The FHWA recommendation for a minimum allowable joint effectiveness (50% of effective joints in a section) should not be confused with the actual %Leff. Rather, the previously specified 75% joint effectiveness as found in the literature (FHWA 1999) is only needed to measure the effectiveness of individual joints within a section. Both of these measures of joint reseal effective- ness are crucial in developing the PRS for the treatment. These two parameters are distinguished below and will be noted hereafter as follows: 1. %Leff = percent of a single joint that is effectively sealed (minimum value should be 75%) 2. %Leff-total = percent of joints within a section that are effectively sealed (a minimum of 50% of all joints should have greater than 75% of the joint sealed) To translate the above AQC for measuring construction quality for a preservation treatment, a change in the AQC before and after joint resealing must be measured. Therefore, measuring the magnitude of D%Leff-total before and after the treatment can quantify the effectiveness of joint resealing treatments. The D%Leff-total can be calculated as follows: ∆ = −( ) ( )− − −L L Leff total eff total after eff total before% % % (A-4) where D%Leff-total = change in %Leff-total due to treatment %Leff-total(after) = monitored %Leff after warranty period %Leff-total (before) = immediate monitored %Leff before treatment Fa ul tin g (m m) Figure A-4. Example SLE calculation for joint seal treatment.

108 performance-Related Specifications for pavement preservation Treatments Equation A-4 shows that a positive or negative value of D%Leff-total correlates with a positive or negative effect of joint resealing, i.e. a positive parameter (increase in percent of joints effectively resealed) is desirable and a negative parameter (decrease in percent of joints effectively resealed) is undesirable. A relationship between the change in AQC and the improvement in pavement performance (in terms of SLE) was established as shown in Figure A-5. The relationship shows that a higher D%Leff-total will result in a higher SLE. This relationship is provided only for illustration rather than an actual standard specification. An agency can adopt this procedure using measured D%Leff-total data to establish a similar relationship for the development of PRS guidelines for joint resealing. 4. Determine Thresholds and Limits for AQC There is no single correct method for establishing specification limits. Furthermore, there is a distinct difference between the limits of AQC and quality measures (i.e., PWL). The steps outlined below were used for establishing limits for AQC and quality measures: 1. Determine AQC-performance relationships. These relationships were discussed in the pre- vious section. Figure A-5 demonstrates a relationship that can be developed between the percent of joints effectively resealed (AQC) and SLE (based on faulting). 2. Set specification limits. As described previously, an individual joint within a section is con- sidered effectively sealed if at least 75% of the sealant is intact. However, given that the change in the percent of joints effectively resealed is the AQC associated with performance, a lower one-sided specification limit of 50% of total joints effectively sealed (%Leff-total) in a section was used for evaluating quality measures, pay adjustments, and risks. 3. Decide on the quality measure. The recommended quality measure often used in current sta- tistical quality control in highway construction is PWL (Burati et al. 2003, National Academies of Sciences, Engineering, and Medicine 2012). 4. Define AQL material. PWL is used as a quality measure in pavement construction practices. However, in this case, a modified version of PWL can be used to reflect a change in pavement quality measure due to joint resealing, i.e., DPWL (PWL after - PWL before treatment). The procedure to obtain the DPWL that could be used as AQL is described below. 5. Define RQL material. The RQL is also a subjective decision made by the agency or party set- ting the specification limits. The DPWL that can be used as the RQL can be obtained from the example. A lot at RQL will receive a reduced pay factor corresponding to the level of quality; a lot may be rejected if DPWL is at or below RQL. Figure A-5. Relationship between change in percent of joints effectively resealed and SLE.

Appendix A 109 Summary • The AQC selected for the development of the joint reseal PRS guidelines is percent of joints effectively sealed within a section (D%Leff-total). • The percent of joints effectively sealed and amount of faulting over time were used to develop a relationship between AQC and SLE. • A lower specification limit of 50%Leff-total was established. • The DPWL is selected as a quality measure. • The DPWL that can be used as the AQL can be obtained as shown in the example. • The DPWL to be used as RQL can be obtained as shown in the example. 5. Specify Test Methods to Measure AQC The previously mentioned Equations 1 and 2 recommended in the Manual of Practice were used to evaluate the effectiveness of the joint sealant (Evans et al. 1999). It is recommended that such methods be used for measuring the AQC. 6. Establish a Sampling and Measurement Plan The risks associated with incorrectly accepting or rejecting a lot are related to the sample size. The procedure outlined in Chapter 5 was followed to develop guidelines for a sampling and measurement plan: 1. Determine which party performs acceptance testing. The decision about the party testing a lot must be agreed upon by the contractor and agency. 2. Determine the type of acceptance plan to be used. The relationships established between percent joints effectively sealed and the SLE showed varying degrees of effectiveness in terms of life extension for different levels of D%Leff-total. The variable acceptance plan was used to measure the change in quality due to variations in the %Leff-total. 3. Develop verification sampling and testing procedures. Verification sampling is standard pro- cedure and used to verify the accuracy of the acceptance test results. Chapter 4 provides guide- lines for different sampling methods, but the decision on whether to use split or independent sampling is unique to the goals of the agency. In practice, it is appropriate that the agency’s verification test methods are used solely for verification and the acceptance methods proposed by the contractor must first be compared to the results of agency verification testing. 4. Select the appropriate verification sampling frequency. The verification sampling frequency of the agency should be approximately 10% of the acceptance sampling rate of the contrac- tor. In practice, verification testing frequency is decided for economic, rather than statistical, reasons. Again, this decision must be agreed upon by agency and contractor and it is assumed that the procedure is already established for the purposes of this example. 5. Determine lot size and sample size. The evaluation or visual inspection of the joints is simply an inspection of the joints along the length of a sublot. Therefore, lots and sublots can be defined as segmented lengths of a project. Based on a survey of highway practice, most agencies report pavement segment lengths in 0.1-mile (500 ft) increments for roughness specifications (Merritt et al. 2015). For joint reseal, it is possible that a similar approach may be followed by a highway agency. A lot size of 0.1 mile could provide a minimum of five average %Leff-total values that can be used for calculating PWL (e.g., a sublot of about seven joints, assuming the joint spacing is 15 ft). 7. Select and Evaluate Quality Measurement Methods The PWL of each lot was estimated before and after joint resealing. The quality measure DPWL was developed to represent the change in quality and is calculated using Equation A-5. % % (A-5)PWL PWL L PWL Leff total After eff total Before( ) ( )∆ = −− −

110 performance-Related Specifications for pavement preservation Treatments The DPWL shows how much the construction quality has statistically demonstrated a shift toward or away from acceptable quality. A positive DPWL value indicates an improvement in AQC due to resealing; a negative value indicates a decline in quality. Table A-1 lists the DPWL values within 16 simulated lots. The SLE was calculated for each D%Leff-total as shown in Table A-1. Using the relationship (between AQC and SLE) and the DPWL measurements, a relationship was developed between the SLEs and the DPWL. This provides the pavement performance prediction as a function of varying quality levels as shown in Figure A-6. The relationship shows that no change in quality would result in no SLE. 8. Develop Pay Adjustment Factors for Incentives and Disincentives The relevant expected pay (EP) and operating characteristic (OC) curves were developed to assign pay factors to appropriate levels of acceptable and rejectable quality while minimizing the expected risks to both contractor and agency. 1. Predict pavement performance as a function of levels of quality. For joint reseal PRS, a rela- tionship between PWL and pavement performance in terms of life extension was established (see Figure A-6). %Leff-total (before) PWLbefore %Leff-total (after) PWLafter D%Leff-total SLE DPWL 56 73 75 100 19 6.9 27 52 56 78 100 26 8.4 44 53 62 76 100 23 7.8 38 58 77 74 100 16 6.1 23 58 79 75 100 17 6.4 21 55 67 75 100 20 7.1 33 54 66 74 100 20 7.1 34 60 83 72 100 12 5.0 17 60 84 73 100 13 5.3 16 59 80 72 100 13 5.3 20 61 86 72 100 11 4.8 14 60 83 70 100 10 4.5 17 58 79 72 100 14 5.6 21 57 76 72 100 15 5.7 24 55 69 69 100 14 5.6 31 53 62 71 100 18 6.5 38 Table A-1. Summary of data for joint resealing. y = 1.4618x0.4423 R² = 1 0 2 4 6 8 10 12 0 20 40 60 80 100 SL E (ye ars ) PWL Figure A-6. Relationship between DPWL and SLE.

Appendix A 111 2. Convert the expected performance into pay adjustment. The pay factors, calculated by using Equation A-6, correspond to the estimated change in quality ranging from 0 to 100 DPWL because SLE is a function of DPWL (see Figure A-6). Figure A-7 shows the relationship between DPWL and pay factor. ( ) = − − PF C R R R D E O1 (A-6) where PF = pay adjustment factor for new pavement or treatment (same units as C) C = present total cost of treatment, use C = 1 for PF D = design life of pavement or initial treatment E = expected life of pavement or treatment O = expected life of successive treatments R = (1 + INF)/(1+ INT) INF = long-term annual inflation rate in decimal form INT = long-term annual interest rate in decimal form Equation A-7 can be used for risk assessment to develop EP curves, assess the associated a and b risk, and determine the appropriate AQL and RQL levels necessary to award payment factors. = × ∆PF PWL22.4 (A-7)0.418 3. Adjust the AQL, RQL, and pay relationships to minimize risk. As discussed in the deter- mination of AQC limits, the AQL and RQL need to be established. The key principle in any fair payment plan is that a contractor should be awarded 100% pay for producing an AQL quality. For establishing AQL, the EP curves must be evaluated such that the payment plan awards 100% pay at AQL, while an incentive can be given if the quality of work is above AQL. Table A-2 shows the pay factors generated from the EP curve by using Equation A-7. As seen in the table, the AQL may be selected at a DPWL = 34 to ensure a contractor is not awarded bonus pay. For establishing the RQL, the EP curves can be used to determine the level of performance (in terms of life extension) that is deemed unacceptable and should result in reduced pay. y = 22.405x0.4177 R² = 0.9988 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 Pa y ad jus tm en t (% ) PWL Figure A-7. Relationship between DPWL and pay adjustment.

112 performance-Related Specifications for pavement preservation Treatments This decision is typically made to meet the needs of the agency to ensure that the pavement performs up to established standards. For instance, in the EP curve values shown in Table A-2, an agency may decide that a life extension of less than 3 years is undesirable. Therefore, the RQL will be set at DPWL = 5, and any lot produced at a quality level below that will receive no pay. The agency may also decide that any quality level between an AQL of 34 and an RQL of 5 will be accepted, but will receive a reduced pay or disincentive. Table A-3 summarizes the final AQL and RQL for joint resealing based on faulting. The OC curves shown in Figure A-8 were developed to assess the risk of receiving a payment that cor- rectly corresponds to the level of quality. When evaluating the risks associated with receiving appropriate pay for a predicted change in PWL, OC curves can be examined. Figure A-8 shows the OC curves for a desired quality of DPWL = 34 (i.e., AQL) for sample sizes of 3, 5, 10, 20 and 30. The level of quality produced by a contractor (as indicated on the x-axis) can be matched with the OC curve with desired quality to evaluate the probability of receiving a pay factor which corresponds to the desired quality. In this case, the established AQL is DPWL = 34. If a contractor produces AQL quality in the field, then the quality level must be matched with the OC curve at AQL. Figure A-8 indicates that DPWL SLE (years) PF% 0 0.2 3.12 5 (RQL) 3.0 46.07 10 4.0 61.28 15 4.8 72.16 20 5.5 80.89 25 6.1 88.29 30 6.6 94.75 34 (AQL) 7.0 100.00 40 7.5 105.75 45 7.9 110.55 50 8.2 114.99 55 8.6 119.12 60 8.9 123.00 65 9.3 126.65 70 9.6 130.10 75 9.9 133.37 80 10.2 136.49 85 10.4 139.47 90 10.7 142.31 95 11.0 145.05 100 11.2 229.74 Table A-2. Summary of EP curve values for varying joint resealing quality levels. Quality characteristics Quality levels and pay adjustment AQL (∆PWL) 34 AQLSLE (years ) 7.0 AQLPF (%) 100 RQL (∆PWL) 5 RQLSLE (years) 3.0 RQLPF (%) 46.07 Table A-3. Pay factor summary for joint reseal.

Appendix A 113 the pay adjustment plan that will award a pay factor greater than 1 has a probability of 50% of all lots sampled. This suggests that the contractor will receive pay greater than 100% (pay for above AQL) half the time and receive pay less than 100% (pay for below AQL) half the time. Given that several lots will be sampled for quality, this will average to 100% pay throughout the project, which is characteristic of an unbiased and fair adjustment plan to both agency and contractor. This also incentivizes the contractor to consistently aim for above AQL quality to offset the probability of lower quality and receive bonus pay. Also, the greater the sample size, the higher the probability of receiving pay greater than 100% if the produced quality is above the AQL. Similarly, the probability of receiving pay greater than 100% is lower if the produced quality is less than the AQL. As previously mentioned, an agency can set the sample size based on their resources and balancing the risk. Table A-4 summarizes the example PRS specifications for joint resealing. Dowel-Bar Retrofit The steps presented in Chapter 5 were followed to develop an example for dowel-bar retrofit treatment in rigid pavements. 1. Select Preservation Treatment Dowel-bar retrofit (DBR) or load-transfer restoration is a rigid pavement preservation treat- ment used to restore the load-transfer efficiency (LTE) across rigid pavement discontinuities. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Pr ob ab ili ty o f r ec ei vi ng a p ay fa ct or > 1 n = 3 5 10 20 30 AQL = PWL of 34 34 PWL Figure A-8. Predicted OC curves. PRS Item Measurement AQC(s) Change in % of effective joints ( %Leff-total ) Lot size 0.1 miles Sample size 5 AQC lower limit 50% joints in a section are “effective” (%Leff ≥ 75) Quality measure PWL Quality thresholds AQL = 34 PWL , RQL = 5 PWL Pay Equation PF (%) = 22.405( PWL)0.4177 AQL pay factor 1 RQL pay factor 0.46 P(PF>1) at AQL 50% Table A-4. Example PRS for joint resealing of PCC pavement.

114 performance-Related Specifications for pavement preservation Treatments 2. Select Candidate Material and Construction Characteristics and Performance Measures The candidate material and construction variables that can be used when performing DBRs include • Mortar properties (flow, compressive and flexural strength, shrinkage, bond strength, absorption) • Installation (reliant on dowel-bar alignment, consolidation of mortar around dowel bar, preventing the intrusion of mortar into the adjacent crack/joint, etc.) The common performance measures addressed by DBR are joint faulting, pumping, and corner breaks. The DBR treatment can improve LTE across a crack or a joint which mitigates joint/crack faulting, pumping, corner breaks, and spalling. The final orientation of the dowel bars and con- solidation of mortar around dowel bars after construction affect the LTE. Therefore, LTE was used as an AQC for this example. Faulting in concrete pavements is a direct result of pumping that causes excessive uneven elevations between the approach and leave slabs in rigid pavements. Grinding treatment is performed to correct low levels of faulting. However, where faulting exceeds 3 mm (0.125 inch) for an un-doweled JPCP discontinuity, DBR is a common treatment. Hence, faulting was the primary performance measure considered for this example. 3. Establish AQC-Performance Relationships and Performance Limits The Pavement-ME faulting model was used to develop performance-based relationships between LTE and faulting. The mechanistic-empirical model is shown below (AASHTO 2008; ARA, Inc. 2003). (A-8) 1 Fault Faultm i i m ∑= ∆ = (A-9)34 1 1 2Fault C FAULTMAX Fault DEi i i i( )∆ = × − ×− − C 1 C 5.0 (A-10)0 7 1 5 6FAULTMAX FAULTMAX DE Logi j j m EROD C∑ ( )= + × × + × = C 1 5.0 (A-11a)0 12 curling 5 200 6 FAULTMAX Log C Log P WetDays p EROD s C ( )= × δ × + × × ×    where: Faultm = Mean joint faulting at the end of month m, in. DFaulti = Incremental change (monthly) in mean transverse joint faulting during month i, in. FAULTMAXi = Maximum mean transverse joint faulting for month i, in. FAULTMAX0 = Initial maximum mean transverse joint faulting, in. EROD = Base/sub-base erodibility factor DEi = Differential density of energy for subgrade deformation accumulated during month i dcurling = Maximum mean monthly slab corner upward deflection due to temperature curling and moisture warping. PS = Overburden on subgrade, lb/ft2. P200 = Percent subgrade material passing #200 sieve. WetDays = Average annual number of wet days (greater than 0.1 inch rainfall). C1,2,3,4,5,6,7,12,34 = Global calibration constants (C1 = 1.0184; C2 = 0.91656; C3 = 0.002848; C4 = 0.0008837; C5 = 250; C6 = 0.4; C7 = 1.8331)

Appendix A 115 C C C (A-11b)12 1 2 0.25FR= + × C C C (A-11c)34 3 4 0.25FR= + × where: FR = Base freezing index defined as percentage of time the top base temperature is below freezing (32 °F) temperature. The differential energy (DE) in the above model is a function of corner deflections. LTE is a function of loaded and unloaded slab corner deflections. Therefore, to develop an appropriate prediction model using faulting data from existing projects, DE must be determined from mea- sured deflections and must be related to the LTE across the same joints. The following procedure was used to develop a performance-based relationship between LTE and faulting: 1. Select rigid pavement preservation treated sections from LTPP database (SPS-4 experiment sections). 2. Calculate the LTE and corresponding differential energy (DE) from the deflection data for the pavement sections by using the following equations: ( ) = δδ ×LTE L U % 100 (A-12) DE k L U( )= δ − δ 2 (A-13)2 2 where dL = Loaded corner deflection (inch) dU = Unloaded corner deflection (inch) k = Calculated modulus of subgrade reaction (pci) SPS-4 sections do not necessarily have DBR applications. However, given that DBR improves LTE (reduces DE), varying the magnitude of DLTE can simulate the effectiveness of DBR treatment. The DLTE is calculated as follows: ( )∆ = −LTE LTE LTEBefore DBR After DBR% (A-14)i i 3. Establish models to predict LTE and DE with age using the data from Step 2. 4. Evaluate measured faulting with pavement age for each pavement section. 5. Use the DE calculated in Step 2 as inputs in the Pavement-ME faulting model (Equa- tions A-8 through A-11) to perform mechanistic-empirical prediction of faulting. 6. Compare the predicted faulting in Step 5 to the measured faulting in Step 4. 7. If the faulting model predictions are comparable, then use the DE and LTE prediction mod- els in Step 3 to develop a relationship between DE and LTE. If the predicted faulting model does not reasonably reflect measured faulting, perform a local calibration of the model to accurately reflect existing conditions (ARA, Inc. 2009). 8. Use the DE prediction model established in Step 7 to predict the DE as a function of time for different initial LTE levels (i.e., begin with 95% LTE in the first year of pavement life and predict the propagation of DE over time). Repeat this process, but begin with 90% LTE in the first year of pavement life. Obtain a series of relationships that demonstrate the change in DE over time for various values of initial LTE. 9. Use the calculated DE as inputs in the faulting model to predict faulting over time for vari- ous initial LTE levels. Establish a faulting threshold that requires a DBR treatment and cal- culate the SLE at different levels of initial LTE. A faulting threshold of 5 mm is used in this example to estimate SLE. SLE can be determined using Equation A-15. A maximum LTE

116 performance-Related Specifications for pavement preservation Treatments was established as 95%, given that an LTE of 100% is difficult to achieve. Typically, a LTE of 90 to 95% can be achieved through a successful DBR. ( ) = −= =SLE years t tLTE LTE n (A-15)95% % where tLTE=95% = time-to-threshold for predicted faulting using initial LTE = 95% tLTE=n% = time-to-threshold for predicted faulting using varying initial n% LTE levels 10. Use Equation A-15 to develop a relationship between DLTE and SLE. This model can be used to predict the expected performance (SLE) in terms of DLTE and develop pay adjustments that can be justified by the magnitude of treatment life extension. This analysis procedure was performed on Section 32-A410 in the LTPP database for illustra- tion purposes. The FWD data from the J4 sensor were used to determine LTE and DE at the joints for a 9,000 lb. drop load. The DE values were predicted with age (see Figure A-9) and multiplied by the average equivalent single-axle loads (ESALs) per year to obtain the total DE per year. The total DE per year was used in the faulting model to predict joint faulting over time. Figure A-10 Figure A-9. Measured LTE and DE with age for Section 32-A410. Fa ul tin g (m m) Figure A-10. Predicted and measured faulting for Section 32-A410.

Appendix A 117 shows the predicted faulting over time for the pavement section. Given that the predicted value for faulting reasonably correlates with the measured value at about 7 years, the DE and LTE models shown in Figure A-9 were used to develop a relationship between LTE and DE (see Fig- ure A-11). The model shown in Figure A-11 can be used to predict DE at varying levels of LTE over time. By using incremental initial LTE values, the change in rate of DE over time can be used to observe the change in rate of the predicted faulting (see Figure A-12). An initial LTE of 95% after treatment was desired in this example. Different levels of initial LTEs were compared to the desired value of 95% to calculate SLEs using Equation A-15. Fig- ure A-13 provides an example of SLE and DLTE calculation. Faulting threshold = 5 mm tLTE=95% = 6.9 years tLTE(n=90%) = 6.1 years SLE = 6.9 - 6.1 = 0.8 years SLE (years) = tLTE=95% - tLTE(n=90%) = 6.9 - 6.1 = 0.8 years DLTE = 95 - 90 = 5% Figure A-11. DE prediction model for Section 32-A410. Fa ul tin g (m m) Figure A-12. Predicted faulting over time for varying LTEo for Section 32-A410.

118 performance-Related Specifications for pavement preservation Treatments Figure A-14 shows the relationship established between DLTE and SLE for Section 32-A410. The relationship shows that a larger change in DLTE results in a higher SLE. This relationship suggests that DBR is more effective when treating pavements with poorer load transfer, which seems counterintuitive to the concept of preservation. However, for preservation treatment, an earlier application (optimum time) should be preferred. 4. Determine Thresholds and Limits for AQC The following steps were used for establishing limits for the AQC and quality measure (PWL) for DBR treatment. 1. Determine AQC-performance relationships. The relationship between LTE and perfor- mance was discussed in the previous section. The DLTE for treatment was used as the AQC. 2. Set specification limits. A 10-year study on dowel-bar retrofit showed that retrofitted dowel bars retained an average joint load transfer between 70 and 90% over a period of 10 years (Pierce et al. 2003). A one-sided lower specification limit of 70% LTE was used for evaluating quality measures, pay adjustments, and risks. Figure A-13. Example SLE calculation for Section 32-A410. Figure A-14. Relationship between DLTE and SLE for Section 32-A410.

Appendix A 119 3. Decide on the quality measure. The recommended quality measure often used in statisti- cal quality assurance in highway construction practices is PWL (Burati et al. 2003, National Academies of Sciences, Engineering, and Medicine 2012). 4. Define AQL material. PWL is used as a quality measure in pavement construction practices. However, in this case, a modified version of PWL can be used to reflect a change in pavement quality measure due to DBRs, i.e., DPWL (PWL after - PWL before treatment). The procedure to obtain the DPWL value that can be used as an AQL is described below. 5. Define RQL material. The RQL is a subjective decision made by the agency or party setting the specification limits. The DPWL value to be used as the RQL can be obtained from the example. A lot at RQL will receive a reduced pay factor corresponding to level of quality; a lot may be rejected if DPWL is at or below RQL. Summary • The AQC selected for the development of DBR PRS is DLTE. • Given that lower LTE values have consequences in the form of severe distresses, a lower speci- fication limit of 70% was selected for this example. • The DPWL was selected as a quality measure. • The DPWL value that can be used as the AQL can be obtained as shown in the example. • The DPWL value that can be used as the RQL can be obtained as shown in the example. 5. Specify Test Methods to Measure the Selected Characteristics Well-established standards exist for evaluating and measuring surface deflections. Such meth- ods can be used for measuring pavement deflections for LTE calculations (Hall et al. 2009, Chatti et al. 2009). 6. Establish a Sampling and Measurement Plan The risks associated with incorrectly accepting or rejecting a lot are related to the sample size. The procedure outlined in Chapter 5 was followed to develop guidelines for a sampling measurement plan: 1. Determine which party performs acceptance testing. The parties (contractor and agency) involved in the project must agree upon who performs the duties of acceptance testing. 2. Determine the type of acceptance plan to be used. The relationships established between LTE and SLE show varying degrees of effectiveness in terms of life extension for different levels of DLTE. The variable acceptance plan can be used to measure the change in quality due to variations in the LTE. 3. Develop verification sampling and testing procedures. Chapter 4 provides guidelines for different sampling methods, but the decision on whether to use split or independent sam- pling is unique to the goals of the agency. In practice, it is appropriate that the agency’s verifi- cation test methods are used solely for verification and that acceptance methods proposed by the contractor must first be compared to the results of agency verification testing, if chosen for verification use. 4. Select the appropriate verification sampling frequency. The verification sampling frequency of the agency should be approximately 10% of the acceptance sampling rate of the contrac- tor. In practice, verification testing frequency is decided for economic, rather than statistical, reasons. This decision must be agreed on by the agency and the contractor and the procedure is assumed to be established for the purposes of this example. 5. Determine lot size and sample size. The standards in the PRS guidelines should be followed for the required number of deflection tests for a given pavement section length. The LTPP

120 performance-Related Specifications for pavement preservation Treatments field manual for measuring FWD deflections specifies several sampling plans which cor- respond to experiment and pavement type. For rigid pavements, the guidelines specify a maximum of 100 deflection tests performed within a 500-ft pavement section (FHWA 2000). For a typical joint spacing of 15 ft for JPCP, there will be about 36 joints in a 0.1-mile pavement segment. A lot size of 0.1 mile could provide a minimum of 5 LTE values for calculating PWL. 7. Select and Evaluate Quality Measurement Methods As discussed in Chapter 5, the quality measure PWL of each lot was estimated before and after the DBR treatment. The quality measure DPWL was developed to represent the change in quality as shown below: (A-16)PWL PWL LTE PWL LTEAfter Before( ) ( )∆ = − The DPWL shows how much the construction quality has statistically demonstrated a shift toward or away from acceptable quality. A positive value of DPWL indicates an improvement in AQC due to DBR; a negative value indicates a decline in quality. Table A-5 summarizes DPWL values calculated for a range of LTE values. The SLE was calculated for each DLTE as shown in Table A-5. Using the relationship (between AQC and SLE) and the DPWL measurements, a relationship was developed between the SLE values and the DPWL. This allows the prediction of pavement performance in terms of SLE for varying levels as shown in Figure A-15. 8. Develop Pay Adjustment Factors for Incentives and Disincentives The relevant expected pay (EP) and operating characteristic (OC) curves were developed to assign pay factors to appropriate levels of acceptable and rejectable quality while minimizing the expected risks to both the contractor and agency. 1. Predict pavement performance as a function of levels of quality. A relationship between PWL and pavement performance in terms of life extension was established. LTEbefore PWLbefore LTEafter PWLafter LTE PWL 65 15.9 95 100 30 84.1 66 21.2 95 100 29 78.8 67 27.5 95 100 28 72.5 68 34.5 95 100 27 65.5 69 42.1 95 100 26 57.9 70 50.0 95 100 25 50.0 71 57.9 95 100 24 42.1 72 65.5 95 100 23 34.5 73 72.5 95 100 22 27.5 74 78.8 95 100 21 21.2 75 84.1 95 100 20 15.9 76 88.5 95 100 19 11.5 77 92.0 95 100 18 8.0 78 94.6 95 100 17 5.4 79 96.5 95 100 16 3.5 80 97.8 95 100 15 2.2 Table A-5. Summary of simulated DPWL based on LTE before and after DBR.

Appendix A 121 2. Convert the expected performance into pay adjustment. The pay factors are calculated using Equation A-17 which considers the estimated change in quality ranging from 0 to 100 DPWL because SLE is a function of DPWL (see Figure A-15). The relationship between the DPWL and the pay factor is shown in Figure A-16. ( ) = − − PF C R R R D E O1 (A-17) where PF = pay adjustment factor for new pavement or treatment (same units as C) C = present total cost of treatment, use C = 1 for PF D = design life of pavement or initial treatment E = expected life of pavement or treatment O = expected life of successive treatment R = (1 + INF)/(1 + INT) INF = long-term annual inflation rate in decimal form INT = long-term annual interest rate in decimal form PWL SL E (ye ars ) Figure A-15. Predicted SLE due to DPWL for Section 32-A410. Figure A-16. Relationship between DPWL and pay factor.

122 performance-Related Specifications for pavement preservation Treatments Equation A-18 can be used for risk assessment to develop EP curves, assess the associated a and b risk, and determine the appropriate AQL and RQL levels necessary to award pay- ment factors. ( )( ) = ∆PayAdj PWL% 25.34 (A-18)0.36 3. Adjust the AQL, RQL, and pay relationships to minimize risk. As discussed in the deter- mination of AQC limits, the AQL and RQL need to be established. The key principle in any fair payment plan is that a contractor should be awarded 100% pay for producing an AQL quality. For establishing the AQL, the EP curves must be evaluated such that the payment plan awards 100% pay at AQL, while an incentive can be given if the quality of work is above the AQL. Table A-6 shows the pay factors generated from the EP curve by using Equation A-18. As seen in Table A-6, the AQL may be chosen at approximately 44 to ensure a contractor is not awarded bonus pay. For establishing the RQL, the EP curves can be used to determine the level of performance (in terms of life extension) that is deemed unacceptable and should result in reduced pay. Typically, this decision is made to meet the needs of the agency to ensure the pavement per- forms to established standards. For instance, in the EP curve values shown in Table A-6, an agency may decide that a life extension of less than 5 years is undesirable. Therefore, the RQL will be set at DPWL = 5, and any lot produced at a quality level below that will receive no pay. Further, the agency may also decide that any quality level between an AQL of 44 and an RQL of 5 will be accepted, but will receive a reduced pay or disincentive. Table A-7 summarizes the finalized AQL and RQL for DBR based on faulting. The OC curves were developed to assess the risk of receiving a payment that correctly corresponds to the level of quality. Figure A-17 shows these OC curves. PWL SLE (years) PF% 0 0.4 4.53 5 (RQL) 5.1 48.33 10 6.7 61.63 15 7.8 70.73 20 8.8 77.83 25 9.6 83.71 30 10.3 88.76 35 11.0 93.19 40 11.6 97.15 44 (AQL) 12.0 100.00 45 12.1 100.76 50 12.6 104.02 55 13.1 107.05 60 13.6 109.86 65 14.0 112.48 70 14.4 114.94 75 14.8 117.26 80 15.2 119.45 85 15.6 121.53 90 15.9 123.51 95 16.3 125.39 100 16.6 127.19 Table A-6. Summary of simulated DPWL based on LTE.

Appendix A 123 When evaluating the risks associated with receiving appropriate pay for the predicted change in the PWL, the OC curves can be examined. Figure A-17 shows the OC curves of desired quality DPWL = 44 (i.e. AQL) for sample sizes of 3, 5, 10, 20 and 30. The level of quality produced by a contractor (as indicated on the x-axis) must be matched with the OC curve with the desired quality to evaluate the probability of receiving a pay factor which corresponds to the desired quality. In this case, the established AQL of DPWL = 44. If a contractor produces AQL in the field, then the quality level must be matched with the OC curve at AQL. Figure A-17 indicates that the pay adjustment plan that will award a pay factor greater than 1 has a probability of 50% of all lots sampled. This suggests that the contractor will receive pay greater than 100% (pay for at AQL) half the time and receive pay less than 100% (pay for below AQL) half the time. Given that several lots will be sampled for quality, this averages to 100% pay throughout the project, which is characteristic of an unbiased and fair adjustment plan to both agency and contractor. This also incentivizes the contractor to consistently aim for above AQL to offset the probability of lower quality and receive bonus pay. Also, the greater the sample size, the higher the probability of receiving pay greater than 100% if the produced quality is above AQL. Similarly, the probability of receiving pay greater than 100% is lower if the produced quality is less than AQL. As mentioned before, an agency can set the sample size based on their resources and balancing the risk. Table A-8 summarizes the example PRS specifications for dowel-bar retrofit. Quality characteristics Quality levels and pay adjustment AQL ( ) 44 AQLSLE (years) 12 AQLPF (%) 100 RQL ( PWL PWL) 5 RQLSLE (years) 5.1 RQLPF (%) 48.33 Table A-7. Pay factor summary for dowel-bar retrofit. Figure A-17. Predicted OC curves for faulting performance due to change in LTE.

124 performance-Related Specifications for pavement preservation Treatments Thin Overlay The PRS guidelines presented in Chapter 5 were used to develop an example for thin overlay treatment in flexible pavements. The following AQCs and performance measures were identi- fied and selected: 1. Performance measures: Cracking, raveling, weathering, friction loss, bleeding, roughness 2. AQCs: Surface smoothness, layer thickness, mix design, binder content, density, air voids 3. Selected AQC and performance measure: Profile-based indices (IRI and DLI) as AQCs and expected surface roughness (IRI), fatigue cracking and rutting as performance measures. This example also demonstrates the use of a mechanistic-empirical approach for establish- ing AQC-performance relationships. The procedure is similar to the one used for the diamond grinding example, given that functional thin overlay can be applied to reduce surface roughness. The following are the steps for developing such relationships: 1. Determine profile-based indices from the measured longitudinal profiles for before and after application of thin overlay. 2. Evaluate the resulting change in dynamic axle load response before and after application of thin overlay. 3. Use changes in axle load spectra (before and after the treatment) to predict pavement perfor- mance by using the mechanistic-empirical pavement design guide (MEPDG) analysis for the pavement sections. 4. Relate change in AQCs to variation in the expected performance. The following steps were used to develop an example for thin overlay treatment. 1. Select a Preservation Treatment Thin overlay is a preservation treatment used for asphalt pavements. The treatment involves the application of 1-in-thick or less asphalt layer on an existing pavement to address minor sur- face distresses and reduce roughness. 2. Select Candidate Material and Construction Characteristics and Performance Measures The reduction in surface roughness due to functional thin overlay application is an important quality characteristic to measure the effectiveness of the treatment. Numerous profile-based indices, such as IRI and DLI, can be used to quantify surface roughness. As with the diamond grinding treatment, a mechanistic-empirical approach was adopted to develop relationships PRS Item Measurement AQC(s) Change in LTE ( ) Lot size 0.1 mile Sample size Minimum 5 AQC threshold 70% LTE Quality measure Quality thresholds AQL = 44 , RQL = 5 Pay Equation PF (%) = 25.34( LTE PWL PWL PWL PWL)0.36 AQL pay factor 1.0 RQL pay factor 0.4833 PF(A) at AQL 50% Table A-8. Example PRS for DBR of PCC pavement.

Appendix A 125 between existing surface roughness and predicted performance measures of asphalt pavements. The IRI and DLI are expected to decrease in magnitude as roughness decreases. They are appro- priate candidates as AQCs for PRS. 3. Establish AQC-Performance Relationships and Performance Limits To validate relationships between the AQC and pavement performance, the profile data from 14 overlaid LTPP SPS-3 test sections were collected. The mechanistic-empirical procedure out- lined previously for diamond grinding was repeated for this analysis. Table A-9 shows the char- acteristics of 14 pavement sections selected for the following reasons: • Data before and after longitudinal profiles • File formats available for profile data in the LTPP database • Distributed in the four LTPP climatic zones • Significant change in the roughness just after thin overlay. A few pavement sections that showed an increase in roughness after thin overlay were included to explore the reasons for an ineffective treatment. The mechanistic-empirical analysis was applied to each of these sections to determine the effect of thin overlay on pavement profiles and profile-based indices, dynamic axle loads, predicted pavement performance (in terms of faulting, cracking, and IRI at a 20 years design life using the Pavement-ME), and the SLE due to overlay. Figure A-18 shows the change in profile indices (DIRI and DDLI) for all sections due to thin overlay. The analyses revealed the following: • Overlay treatment was generally effective for all but three pavement sections (4-A310, 32-B310, and 17-B310). • An examination of the profile elevations before and after the treatment showed that thin overlay treatments did not adequately reduce surface irregularities for the same three sec- tions (4-A310, 32-B310, and 17-B310). This is evident not only in the profile elevations of these sections, but also in the PSD of the profiles. The poor performance in terms of immedi- ate roughness after treatment could be a result of various environmental, construction, and material factors. • Because IRI seems to give similar results to DLI (see Figure A-19), it is recommended for use as the AQC, because it is the most commonly used index. Note: 1Age at the time of treatment application, 2Average annual wet days, 3Freezing index, 4Average annual daily traffic, 5AASHTO soil classification S. No Section ID State Age (years) Climate Zone AAWD 2 FI3 AADT4 Overlay thickness (in) Subgrade type5 Before Application1 After 1 16-B310 ID 0.01 0.12 0.21 DF 112 627 532 1.1 A-1-b 2 16-C310 ID 0.01 0.12 0.22 DF 98 642 356 1.2 A-1-b 3 53-A310 WA 0.01 0.02 0.34 DF 128 282 294 1.8 A-1-b 4 20-B310 KA 0.00 0.50 0.57 WF 85 195 300 1.5 A-7-6 5 4-C310 AR 0.21 0.20 1.03 DNF 60 1 475 1.6 A-2-6 6 4-A310 AR 0.22 0.34 1.05 DNF 26 1 350 1.2 A-2-6 7 32-B310 NE 0.03 0.16 0.15 DF 66 310 801 1.5 A-4 8 49-A310 UT 0.00 0.01 0.18 DF 79 315 160 1 A-2-4 9 49-B310 UT 0.01 0.01 0.18 DF 113 278 160 1.7 A-2-4 10 17-B310 IL 0.49 1.05 1.51 WF 123 630 312 1.2 A-4 11 53-C310 WA 0.02 0.00 0.49 WNF 198 23 184 1.1 A-1-a 12 18-A310 IN 0.83 0.51 1.80 WF 123 182 1384 1 A-4 13 20-A310 KA 0.00 0.42 0.58 WF 83 253 129 1.2 A-7-6 14 32-A310 NE 0.73 0.71 0.83 DNF 51 101 113 1.1 A-7-6 Table A-9. Summary of asphalt pavement sections used in analysis.

126 performance-Related Specifications for pavement preservation Treatments (a) IRI 77.7 71.5 109.1 137.2 55.2 37.4 54.3 157.2 135.0 79.5 63.4 115.2 136.2 70.267.4 56.0 53.7 53.9 45.9 38.6 80.9 80.2 92.0 82.2 58.0 102.2 71.1 66.0 0 50 100 150 200 16-B310 16-C310 53-A310 20-B310 4-C310 4-A310 32-B310 49-A310 49-B310 17-B310 53-C310 18-A310 20-A310 32-A310 O ve ra ll IR I ( in/ mi ) Pavement profile section no. Before grinding After grinding (b) DLI Pavement profile section no. 7.2 7.7 13.0 14.0 9.0 3.7 12.1 16.0 15.8 9.3 6.3 14.3 11.4 6.96.9 8.4 7.0 7.8 5.0 6.6 11.8 10.3 14.1 8.4 6.1 13.3 7.1 6.7 0 2 4 6 8 10 12 14 16 18 16-B310 16-C310 53-A310 20-B310 4-C310 4-A310 32-B310 49-A310 49-B310 17-B310 53-C310 18-A310 20-A310 32-A310 O ve ra ll D LI (1 0-2 in .) Before grinding After grinding Figure A-18. Change in IRI and DLI before and after thin overlay for all sections. IRI (in/mile) y = 0.0679x + 0.2493 R² = 0.6632 -4 -3 -2 -1 0 1 2 3 4 5 6 7 -40 -20 0 20 40 60 80 100 D LI (1 0- 2 in ) Figure A-19. IRI versus DLI before and after thin overlay.

Appendix A 127 • The results of the dynamic load analysis for the pavement sections showed that treatments which could reduce the surface roughness generally reduced the dynamic loads. This suggests that there is a positive correlation with an effective thin overlay treatment which reduces roughness and the resulting dynamic loads experienced by the pavement. • The sections which showed an increase in roughness, or else a minimal change in roughness, after thin overlay exhibited a minimal change in dynamic loads or in some cases a greater dynamic response. Figure A-20 shows the minimal shift in dynamic load for the three sections on which thin overlay was ineffective. (e) Single axle —4-A310 0 0.1 0.2 0.3 0.4 0.5 0.6 0 10000 20000 30000 40000 Lo ad d ist rib ut io n Single Axle Loads Before Grinding After Grinding (f) Tandem axle —4-A310 0 0.1 0.2 0.3 0.4 0.5 0 20000 40000 60000 80000 Lo ad d ist rib ut io n Tandem Axle Loads Before Grinding After Grinding (g) Single axle —32-B310 0 0.1 0.2 0.3 0.4 0.5 0.6 0 10000 20000 30000 40000 Lo ad d ist rib ut io n Single Axle Loads Before Grinding After Grinding (h) Tandem axle —32-B310 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0 20000 40000 60000 80000 Lo ad d ist rib ut io n Tandem Axle Loads Before Grinding After Grinding (i) Single axle —17-B310 0 0.1 0.2 0.3 0.4 0.5 0.6 0 10000 20000 30000 40000 Lo ad d ist rib ut io n Single Axle Loads Before Grinding After Grinding (j) Tandem axle —17-B310 0 0.1 0.2 0.3 0.4 0.5 0 20000 40000 60000 80000 Lo ad d ist rib ut io n Tandem Axle Loads Before Grinding After Grinding Figure A-20. Example dynamic axle load spectra before and after thin overlay.

128 performance-Related Specifications for pavement preservation Treatments • A shift in the dynamic loads after thin overlay treatment should be reflected in the resulting predicted performance (cracking, rutting, and IRI). For this example, only IRI was considered as the primary performance measure for evaluating the SLEs due to thin overlay. Although fatigue cracking, rutting, longitudinal cracking, and thermal cracking were evaluated in the analysis, the decision to omit these performance measures when evaluating SLEs was based on the following reasons: – There were no clear trends between AQC and performance when attempting to relate the change in performance in terms of fatigue cracking, longitudinal cracking, and transverse cracking to the change in IRI. The trends vary significantly between the evaluated pavement sections. – This variation may result from construction and material factors, including mix design, pavement layer structure, and climate. – The pavement sections experienced varying levels of predicted fatigue and longitudinal cracking. Thin overlays are not typically designed to reduce the amount of load-related cracking on flexible pavements. – Asphalt overlays used in preservation are often much thinner than a concrete pavement slab. An improvement in terms of load-related cracking performance prediction may be unreliable for thin overlay. – Thermal cracking depends on climate; therefore, predicted performance in terms of trans- verse cracking is difficult to directly relate to thin overlay treatment. – It is questionable to draw direct conclusions from the rutting performance, because perma- nent deformation (i.e., rutting) in asphalt pavements is attributed to gross vehicle weights rather than dynamic loads. • The results of the mechanistic-empirical performance prediction indicated that thin overlay may not always improve pavement performance and such variations are associated with an increase or decrease in dynamic loads and changes in the surface profile. This is a similar observation to trends observed in the diamond grinding example. Figure A-21 shows the rela- tionships between before and after IRI and DLI with the predicted long-term roughness. Such relationships validate the mechanistic-empirical approach for relating AQC to performance. A general trend can be seen in these figures that an increase in DIRI or DDLI demonstrates an improvement in pavement performance in terms of IRI. (a) IRI (b) DLI -60 -40 -20 0 20 40 60 80 100 -50 0 50 100 IR I ( in/ mi ) IRI (in/mi) -60 -40 -20 0 20 40 60 80 100 -4 -2 0 2 4 6 8 IR I ( in/ mi ) DLI (10-2 in) Figure A-21. Relationship between AQCs and 20-yr roughness performance.

Appendix A 129 • The relationships established between DIRI (IRI after overlay - IRI before overlay, similarly with DLI) and predicted performance at the end of 20 years (in terms of IRI) were used to determine the SLE for each overlay treatment. The SLE was estimated by subtracting the before overlay “time-to-threshold” from after thin overlay “time-to-threshold” in years in terms of IRI (threshold being the pretreatment IRI). This threshold was selected for illustration. A negative SLE reflects poor performance after overlay treatment and sug- gests that the pavement will reach a threshold sooner than the do-nothing alternative. The relationships between DIRI and DDLI with SLE are shown in Figures A-22 and A-23, respectively. These figures show that a relationship between a candidate AQC and expected performance can be established and used to estimate rational pay adjustments based on the expected SLE. 4. Determine Thresholds and Limits for AQC There is no single correct method for establishing specification limits. Furthermore, there is a distinct difference between the limits of AQC and quality measures. The following steps were adopted for establishing limits for AQC and quality measures: 1. Determine AQC-performance relationships. These relationships have been discussed in the previous section. Both IRI and DLI can be used to predict the immediate effect of thin overlay on surface roughness. Because IRI and DLI correlate well, IRI was selected as the primary AQC for this example. 2. Set specification limits. A synthesis of current practice surveyed 22 states regarding localized roughness provisions for IRI-based specifications (Merritt et al. 2015). Of these states, 16 use an IRI range of 80 to 200 inch/mile for determining pay adjustments. An NCHRP study for determining quality adjustment pay factors for pavements suggested 65 to 95 inch/mile as an acceptable range of IRI for newly constructed pavements, with 65 inch/mile considered to be superior ride quality that should provide an incentive (National Academies of Sciences, Engineering, and Medicine 2012). The same study suggested 75 to 120 inch/mile as a level of roughness that requires a corrective action. Based on these findings, the research team has determined that a post-overlay treatment IRI of 90 inch/mile was deemed to provide adequate SL E (ye ars ) IRI (in/mi) 0 10 20 30 40 50 60 -50 0 50 100 Rutting SLE IRI SLE Figure A-22. DIRI vs. SLE.

130 performance-Related Specifications for pavement preservation Treatments smoothness. Therefore, a one-sided upper specification limit of 90 inch/mile was adopted and used for evaluating quality measures, pay adjustments, and risks. 3. Decide on the quality measure. PWL is the recommended quality measure used in current quality control in highway construction (Burati et al. 2003, National Academies of Sciences, Engineering, and Medicine 2012). 4. Define AQL material. PWL is used as a quality measure in pavement construction practice. However, in this case, a modified version of PWL can be used to reflect a change in pavement quality measure due to thin overlay, i.e., DPWL (PWL after - PWL before treatment). The procedure to obtain the DPWL value that could be used as AQL is described below. 5. Define RQL material. The RQL is a subjective decision made by the agency or party setting the specification limits. The DPWL value that could be used as RQL can be obtained from the example. A lot at RQL will receive a reduced pay factor corresponding to the level of quality; a lot may be rejected if DPWL is at or below RQL. Summary • The AQC selected for development of thin overlay PRS is IRI. • Given that the IRI as a measure of roughness should not have a negative consequence for being “too smooth,” only an upper limit IRI of 90 inch/mile is selected to represent adequate pavement smoothness. • The DPWL is selected as a quality measure. • The DPWL value that can be used as an AQL can be obtained as shown in the example. • The DPWL value that can be used as an RQL can be obtained as shown in the example. 5. Specify Test Methods to Measure AQC The existing well-established standards for measuring and evaluating surface roughness are recommended. Most highway agencies use lightweight profilers for measuring profile-based specifications (Merritt et al. 2015). In comparison to high-speed mounted profilers, lightweight profilers weigh significantly less and have a more manageable, lower operating speed which is ideal for operating in constrained conditions and along shorter sections of pavement. Lightweight 0 10 20 30 40 50 60 -4 -2 0 2 4 6 8 SL E (ye ars ) DLI (10-2 in) Rutting SLE IRI SLE Figure A-23. DDLI vs. SLE.

Appendix A 131 profilers are limited in that most are set up to only measure a single wheel path and require at least two carefully coordinated runs to obtain complete profile data for one lane. The FHWA Highway Performance Monitoring System (HPMS) field manual references AASHTO PP 37-04 (now RO 43-13) and ASTM E-950 for procedures to collect IRI data (FHWA 2014). AASHTO RO 43-13 (Standard Practice for Quantifying Roughness of Pavements) details the estimation of IRI with the use of a longitudinal profile index measured in accordance with ASTM E-950 (Standard Test Method for Measuring the Longitudinal Profile of Traveled Sur- faces with an Accelerometer Established Inertial Profiling Reference). In the HPMS, roughness is reported in IRI units of either m/km or inch/mile. These standards should be consulted by agencies and contractors to ensure appropriate procedures are followed when collecting rough- ness data in the field. 6. Establish a Sampling and Measurement Plan The risks associated with incorrectly accepting or rejecting a lot are related to the sample size. The procedure outlined in Chapter 5 was followed to develop guidelines for a sampling and measurement plan: 1. Determine which party performs acceptance testing. The parties (contractor and agency) involved in the project must agree upon the duties of performing acceptance testing. 2. Determine the type of acceptance plan to be used. A variable acceptance plan is best suited for measuring the magnitude of IRI. Construction and sampling variability can affect surface smoothness. The variable acceptance plan can measure this variability and determine a qual- ity measure based on statistical parameters. 3. Develop verification sampling and testing procedures. Verification sampling is a standard procedure used to verify the accuracy of the acceptance test results. Chapter 4 provides guide- lines for different sampling methods, but the decision on whether to use split or independent sampling is unique to the goals of the agency. For this example, it is assumed that an agency or a third party will measure the surface profile for the entire project length. However, the speed and lateral and longitudinal reference points should match with the acceptance test- ing. In practice, it is appropriate that the agency’s verification test methods are used solely for verification and that acceptance methods proposed by the contractor must first be compared to the results of agency verification testing. 4. Select the appropriate verification sampling frequency. The verification sampling fre- quency of the agency should be approximately 10% of the acceptance sampling rate of the contractor. In practice, verification testing frequency is decided for economic, rather than statistical, reasons. This decision must be agreed upon by the agency and contractor and it is assumed that the procedure is already established for the purposes of this example. 5. Determine lot size and sample size. The evaluation of pavement surface roughness involves the longitudinal measurement of pavement profiles. Therefore, lots and sublots should logically be defined as segmented lengths of a project. Based on a survey of highway practice, most agencies report pavement segment lengths in 0.1-mile (500-ft) increments for IRI-based specifications (Merritt et al. 2015). For this example, the LTPP pavement sections are only 500 ft long, which can be defined as the lot size. A sample is defined as a segment with a shorter length within a lot. Although the risks associated with sampling will depend on sample size, profile signal analysis showed poor sensitivity to the DLI (which was designed to capture the dynamic load of truck traffic) when profile segment lengths were less than 100 feet. Given that DLI is correlated with IRI, a sublot length of 100 ft is selected. Thus, each of the pavement sections evaluated in Table A-9 will be considered as a single lot from a larger project. Each lot will be subdivided into sublots of approximately 100 ft, and the IRI of each sublot will be considered as a sample. This results in the sample size of five for each pavement section. The statistical evaluations will be based on these established lot and sample sizes.

132 performance-Related Specifications for pavement preservation Treatments 7. Select and Evaluate Quality Measurement Methods As discussed in Chapter 5, the quality measure will be DPWL. Using the procedure outlined above, the PWL before and after thin overlay of each lot was calculated. The quality measure DPWL was developed to represent the measure of change in quality and is calculated using Equation A-19. (A-19)After BeforePWL PWL IRI PWL IRI( ) ( )∆ = − The DPWL shows how much the construction quality has statistically demonstrated a shift toward or away from acceptable quality. A positive DPWL value indicates improvement in AQC due to thin overlay; a negative value indicates a decline in quality. An example of the PWL calcu- lation using the procedure outlined in the diamond grinding example is presented. Table A-10 summarizes DPWL values for different sections. The PWL obtained from each “lot” was used to develop pay factors unique to the IRI. Based on the AQC-performance relationships previously established, the PWL can be related to expected performance in terms of SLE that can be used to develop a pay equation that relates PWL levels to expected pay. 8. Develop Pay Adjustment Factors for Incentives and Disincentives Pay adjustment factors are necessary for variable acceptance plans in developing PRS. For reasons similar to those of the diamond grinding example, a variable acceptance plan is selected for thin overlay because pavement lots can exhibit a wide range of IRI that cannot be rejected solely on a pass/fail basis. Given a selected upper specification limit of 90 inch/mile for thin overlay PRS, a sampled lot which is smoother (i.e., below 90 inch/mile) is not only acceptable but exceeds the desired quality. Similarly, a measured roughness greater than 90 inch/mile may still be feasible if the roughness level does not substantially exceed the target quality, but it would not deserve full pay. Using the pay equation relationships, the relevant EP and the OC curves were developed to assign pay factors to different levels of acceptable and rejectable quality while minimizing the expected risks. 1. Predict pavement performance as a function of levels of quality. A relationship between PWL and pavement performance was established and then substantiated by the IRI-performance No. Section ID PWLbefore PWLafter PWL 1 16-B310 90.7 100.0 9.3 2 16-C310 100.0 100.0 0.0 3 53-A310 34.0 100.0 66.0 4 20-B310 0.0 100.0 100.0 5 4-C310 100.0 100.0 0.0 6 4-A310 100.0 100.0 0.0 7 32-B310 100.0 86.9 -13.1 8 49-A310 0.0 93.9 93.9 9 49-B310 0.0 45.0 45.0 10 17-B310 76.6 70.7 -6.0 11 53-C310 100.0 100.0 0.0 12 18-A310 25.8 30.4 4.5 13 20-A310 0.0 100.0 100.0 14 32-A310 100.0 100.0 0.0 Table A-10. Summary of DPWL based on IRI before and after thin overlay.

Appendix A 133 relationships developed. Subsequently, these results were used to develop a relationship between SLE due to thin overlay and DPWL. DPWL is the change in the PWL of a lot after treatment, i.e. it is a change in the measure of quality of the selected AQC (IRI). In developing models that relate DPWL and SLE, it was found that Sections 16-C310, 4-C310, 4-A310, 53-C310, and 32-A310 exhibited a significant improvement in life extension, but had a DPWL equal to zero. Closer examination of these sections shows that the PWL before treatment and after treatment were both 100%, indicating the pavement sections were already within AQLs and did not necessarily require the treatment based on the previously specified upper quality limit of 90 inches per mile. The inclusion of the SLE versus PWL trends of these sections can result in incorrect conclusions, namely that a change in PWL of zero can still result in some life extension. Therefore, these sections were not considered in developing a pay adjustment curve to ensure that the payment accurately reflects the quality of the lot. Figure A-24 shows the relationship between predicted SLE based on roughness and DPWL for all the pavement sections considered. As determined in the evaluation of the predicted performance of thin overlay treatments before and after treatment, the performance mea- sures of alligator, longitudinal, rutting and thermal cracking will not be used to develop pay equations. The results show a linear trend in SLE versus DPWL for IRI. This suggests that, if a thin overlay treatment can smooth a large percentage of a lot (a pavement section) into acceptable levels of roughness, the pavement should expect an improvement in expected performance. These performance relationships can be used to justify a pay equation which relates quality (DPWL) to expected pay. 2. Convert the expected performance into pay adjustment. The pay factors for varying life extensions can be calculated by using Equation A-20 which corresponds to the estimated change in quality ranging from DPWL = 0 to DPWL = 100, given that SLE is a function of DPWL (see Figure A-24). y = 0.4417x R² = 0.9273 -10 0 10 20 30 40 50 60 -20 0 20 40 60 80 100 SL E (ye ars ) PWL Figure A-24. SLE versus quality measure DPWL.

134 performance-Related Specifications for pavement preservation Treatments ( ) = − − PF C R R R D E O1 (A-20) where PF = pay adjustment factor for new pavement or overlay (same units as C) C = present total cost of resurfacing, use C = 1 for PF D = design life of pavement or initial overlay E = expected life of pavement or overlay O = expected life of successive overlays R = (1 + INF)/(1+ INT) INF = long-term annual inflation rate in decimal form INT = long-term annual interest rate in decimal form The relationships were developed between pay factors and SLE for IRI as shown in Fig- ure A-25. These expected pay (EP) curves were used to (1) refine the levels of acceptable and rejectable quality, (2) develop OC curves to assess the associated a and b risk, and (3) ensure that payment factors are awarded in accordance with the level of quality achieved. Table A-11 summarizes the calculated SLE and pay adjustment factors for predicted roughness. 3. Adjust the AQL, RQL, and pay relationships to minimize risk. As discussed in the determi- nation of AQC limits, the AQL and RQL need to be established. For establishing AQL, the EP curves must be evaluated such that the payment plan awards 100% pay at AQL while incentive can be given if quality of work is above AQL. Table A-11 shows the pay factors generated from the EP curves (see Figure A-25). As seen in the table, the AQL may be chosen at approximately DPWL of 20.5 for IRI to ensure a contractor is not awarded bonus pay for AQL work. For establishing RQL, the EP curves can be used to determine the level of performance (in terms of life extension) that is deemed unacceptable and should result in reduced pay. This decision is typically made to meet the needs of the agency to ensure the pavement performs to established standards. For instance, as seen in Table A-11, an agency may decide that a life extension of less than 2 years is undesirable. Therefore, the RQL will be set at 5 DPWL, and any lot produced at a quality -50 0 50 100 150 200 250 300 0 20 40 60 80 100 Pa y ad jus tm en t (% ) PWL PF from SLE PF-PWL model Figure A-25. Predicted IRI EP model.

Appendix A 135 level below that will receive no pay. Simultaneously, the agency is also deciding that any qual- ity between AQL of 20.5 DPWL and RQL of 5 DPWL will be accepted, but will receive reduced pay or a disincentive. Table A-12 summarizes the finalized AQL and RQL for thin overlay based on performance due to roughness. For samples of sizes 3, 5, 10, 20, and 30, the OC curves were developed to assess the risk of receiving a payment that correctly corresponds to the level of quality sampled. Figure A-26 shows these OC curves. When evaluating the risks associated with receiving appropriate pay for predicted IRI, OC curves can be examined. The level of quality produced by a contractor as indicated on the x-axis can be matched with the OC curve with desired quality to evaluate the probability of receiving a pay factor which corresponds to the desired quality. In the case of predicted IRI, recall the established AQL of 20.5 DPWL. If a contractor produces AQL in the field, then the quality level of AQL must be matched with the OC curve at AQL. Figure A-26 indicates that the pay adjustment plan with PWL SLE (years) PF (%) 100 44 274 95 42 272 90 40 268 85 38 263 80 35 257 75 33 249 70 31 241 65 29 231 60 27 220 55 24 207 50 22 193 45 20 178 40 18 162 35 15 145 30 13 126 25 11 106 20.5 (AQL) 9 100 20 9 85 15 7 62 10 4 38 5 (RQL) 2 13 0 0 -13 Table A-11. Summary of pay factor for roughness. Quality1 Predicted IRI AQL 20.5 AQLSLE (yrs) 9 AQLPF (%) 100 RQL 5 RQLSLE (yrs) 2 RQLPF (%) 13 1Quality measure in units of ∆PWL Table A-12. Summary of pay factor for IRI.

136 performance-Related Specifications for pavement preservation Treatments award pay factor greater than 1 has a probability of 50% that the contractor will receive pay greater than 100% (pay for above AQL) half the time and receive pay less than 100% (pay for below AQL) half the time. Given that several lots will be sampled for quality, this averages to 100% pay through- out the project, which is characteristic of an unbiased and fair adjustment plan to both the agency and the contractor. This also incentivizes the contractor to consistently aim for above AQL quality to offset the probability of performing below AQL and receive bonus pay. The greater the sample size, the higher the probability of receiving pay greater than 100% if the produced quality is above AQL. Similarly, the probability of receiving pay greater is lower than 100% if the produced quality is less than AQL. An agency can set the sample size based on their resources and balancing the risk. Table A-13 summarizes the example PRS specifications for thin overlay. Microsurfacing The PRS guidelines presented in Chapter 5 were followed to develop an example for micro- surfacing treatment in flexible pavements. 1. Select a Preservation Treatment Microsurfacing treatment involves laying a mixture of crushed, well-graded aggregate, min- eral filler, and latex-modified emulsified asphalt over the full width of a pavement. The treatment Figure A-26. Predicted roughness OC curves. PRS Item Measurement AQC(s) IRI (inch/mile) Lot size 0.1 mile Sample size 5 AQC threshold Upper specification limit = 90 inch/mile Quality measure = PWL(IRIBEFORE) - PWL(IRIAFTER) Quality thresholds AQL = 20.5 , RQL = 5 Pay Equation PF (%) = -0.0252 2+5.39 PWL PWL PWL PWL PWL -13.15 AQL pay factor 1.0 RQL pay factor 0.13 PF(A) at AQL 50% Table A-13. Example PRS for thin overlay treatment.

Appendix A 137 is applied on an existing pavement surface primarily to seal low-severity cracks and rutting and to mitigate raveling and asphalt oxidation (Peshkin et al. 2011). 2. Select Candidate Material and Construction Characteristics and Performance Measures The material and construction variables that can be used as AQCs for microsurfacing include • Emulsion type: application rate depends on the climatic conditions • Aggregate type: depends on the aggregate source properties (e.g., sand equivalent, soundness, abrasion resistance, and crushed particles) • Fillers/additives: Portland cement or other fine materials are used as “mixing aids” to extend mixing time, improve workability, and reduce curing time • Aggregate application rates: high application rates may cause the mix to segregate and leave a flushed or excessively smooth surface texture • Surface texture: micro- and macro-textures. The following are common performance measures addressed by microsurfacing: • Surface friction • Raveling which is caused by insufficient aggregate embedment, poor quality mix bond, excessively thin application, premature opening to traffic, i.e., insufficient curing • Delamination caused by improper preparation of the substrate before applying microsurfac- ing, or caused if emulsion breaks too quickly. Microsurfacing treatment can improve surface friction and address minor surface irregulari- ties. Typically, the treatment is used to address surface friction problems on an existing asphalt surface. Currently, laser scanners [e.g., circular track meter (CTM)] are being used in the field to measure surface texture in terms of mean profile depth (MPD). Therefore, MPD was selected as the AQC for developing PRS guidelines for microsurfacing applications. Although multiple AQCs can be considered for PRS development, MPD was selected because it is an essential component of macrotexture measurements and can be measured during construction by using conventional high-speed laser measurement devices (Henry 2000). 3. Establish AQC-Performance Relationships and Determine Performance Limits An agency can develop an AQC-performance relationship if MPD can be related to friction of pavement surface. For this example, a relationship between MPD and friction number (FN) developed for microsurfacing treatment was used. The relationship is shown by Equation A-21 (Rajaei et al. 2014): 30.362 54.912 (A-21)FN LN CTMMPD( )= × + where CTMMPD = mean profile depth measured using circular track meter The CTM consists of a laser displacement sensor on an arm that rotates to measure MPD in accordance with ASTM E2157 (Rajaei et al. 2014). The relationship was developed using the results of several measured test sections, as illustrated in Figure A-27. Given that field monitoring data were not available, a series of pavement sections with vary- ing MPD deterioration rates over time were established, using typical MPD ranges identified in Figure A-27. Table A-14 shows the simulated MPDs over time.

138 performance-Related Specifications for pavement preservation Treatments Equation 6-2 was used to estimate the friction number (FN) for given MPD values in Table A-14. In this example, the desired after-treatment condition was assumed to be the deterioration of MPD observed in Section 1 (maximum MPD of 1.5). An agency can set its own target for after- treatment conditions based on its experience. The performance of each pavement section was compared with the performance of Section 1 to evaluate the effect of the change in AQC (MPD) due to microsurfacing. The change in FN was quantified using the SLE which is the difference in time between before- and after-treatment states to an established threshold for FN. The SLE can be determined by using Equation A-22. (A-22)1.5SLE years t tMPD MPD n( ) = −= = where tMPD=1.5 = time-to-threshold for after treatment FN using initial MPD = 1.5 tMPD=n = time-to-threshold for before treatment FN using varying initial MPD A survey of 11 state highway agencies showed that most specifications have a minimum FN requirement ranging from 30 to 45 (Henry 2000). The FN threshold of 40 is used in this example to estimate SLE. Figure A-28 shows the ranges of predicted FN over time. Each pavement section (i.e., Sections 2 through 5) was compared with Section 1 to calculate SLE. Figure A-29 shows an example SLE calculation. Figure A-27. Relationship of texture and friction to CTM MPD from field (Rajaei et al. 2014). Age (yrs) Section 1 Section 2 Section 3 Section 4 Section 5 0 1.50 1.25 1.15 1.00 0.85 1 1.39 1.16 1.06 0.92 0.77 2 1.28 1.06 0.97 0.83 0.69 3 1.17 0.97 0.88 0.75 0.62 4 1.06 0.87 0.79 0.67 0.54 5 0.94 0.78 0.71 0.58 0.46 6 0.83 0.68 0.62 0.50 0.38 7 0.72 0.59 0.53 0.42 0.31 8 0.61 0.49 0.44 0.33 0.23 9 0.50 0.40 0.35 0.25 0.15 Table A-14. Simulated MPD over time.

Appendix A 139 To translate the above AQC for measuring construction quality for a preservation treatment, a change in the AQC before and after microsurfacing must be measured. Therefore, measuring the magnitude of MPD before and after treatment can quantify the effectiveness of microsurfacing. The formulation for DMPD is as follows: (A-23)MPD MPD MPDafter before∆ = −( ) ( ) where DMPD = change in MPD due to treatment MPD(after) = existing monitored MPD immediately after treatment MPD(before) = existing monitored MPD before treatment Equation A-23 shows that a positive or negative value of DMPD correlates with a positive or negative effect of microsurfacing, i.e., an increase in MPD is desirable and vice versa. By measur- ing the DMPD due to the treatment, the pavement section’s SLE corresponds to a change in the AQC. A relationship between the change in the AQC and the SLE can be established as shown in Figure A-30. A larger change in MPD results in greater improvement in pavement SLE. This Figure A-28. Predicted FN over time. Figure A-29. Example SLE calculation based on FN comparison for microsurfacing treatment.

140 performance-Related Specifications for pavement preservation Treatments relationship is established only as an example for illustration and not as a standard specification. An agency can adopt this procedure using measured DMPD data to establish such relationships to develop PRS guidelines for microsurfacing. 4. Determine Thresholds and Limits for AQC There is no single correct method for establishing specification limits. Furthermore, there is a distinct difference between the limits of AQC and quality measures (PWL). The following steps were used for establishing limits for AQC and quality measures: 1. Determine AQC-performance relationships. The relationships have been discussed previ- ously. The results in Figure A-30 demonstrate a relationship can be developed between the MPD (AQC) and SLE (based on FN). 2. Set specification limits. Different highway agencies have specifications with a minimum FN requirement ranging from 30 to 45 (Henry 2000). Therefore, a one-sided lower performance specification limit of 40 FN was adopted. All quality measures, pay adjustments, and risks will be evaluated based on this assumption. 3. Decide on the quality measure. PWL is the recommended quality measure used in current statistical quality control in highway construction (Burati et al. 2003, National Academies of Sciences, Engineering, and Medicine 2012). 4. Define AQL material. PWL is used as a quality measure in pavement construction practice. However, in this case, a modified version of PWL can be used to reflect a change in pavement quality measure due to microsurfacing, i.e., DPWL (PWL after - PWL before treatment). The procedure to obtain this DPWL value is described below. 5. Define RQL material. The RQL is a subjective decision made by the agency or party setting the specification limits. The DPWL value that can be used as RQL is described below. A lot at RQL will receive a reduced pay factor equivalent to that level of quality; a lot may be rejected if DPWL is at or below RQL. Summary • The AQC selected for development of the microsurfacing example is MPD. • A lower specification limit of 40 FN (corresponding to approximately 0.6 MPD) is used. • The DPWL is selected as a quality measure. • The DPWL value that can be used as an AQL can be obtained as shown in the example. • The DPWL value that can be used as an RQL can be obtained as shown in the example. Figure A-30. Performance-based relationship between change in MPD and SLE.

Appendix A 141 5. Specify Test Methods to Measure AQC Established standards, such as ASTM E2157, can be used for measuring and evaluating surface texture. 6. Establish a Sampling and Measurement Plan The risks associated with incorrectly accepting or rejecting a lot are related to the sample size. The procedure outlined in Chapter 5 was followed to develop guidelines for a sampling and measurement plan: 1. Determine which party performs acceptance testing. The parties (contractor and agency) involved in the project must agree on the duties of performing acceptance testing. 2. Determine the type of acceptance plan to be used. The relationships established between MPD and SLE show varying degrees of effectiveness in terms of life extension for different levels of DMPD. The variable acceptance plan can be used to measure the change in quality due to statistical variation of the MPD. 3. Develop verification sampling and testing procedures. Verification sampling is a standard procedure used to verify the accuracy of the acceptance test results. Chapter 4 provides the guidelines for different sampling methods, but the decision as to whether to use split or inde- pendent sampling is unique to the goals of the agency. In practice, it is appropriate that the agency’s verification test methods are used solely for verification and that acceptance methods proposed by the contractor must first be compared to the results of agency verification testing. 4. Select the appropriate verification sampling frequency. The verification sampling frequency of the agency should be approximately 10% of the acceptance sampling rate of the contractor. In practice, verification testing frequency is decided for economic, rather than statistical, rea- sons. Again, this decision must be agreed on by the agency and contractor, and it is assumed that the procedure is already established for the purposes of this example. 5. Determine lot size and sample size. The evaluation of pavement surface texture requires the pass of conventional surface texture measuring devices over pavement profiles. Therefore, lots and sublots can be defined as segmented lengths of a project. Based on a survey of highway practice, most agencies report pavement segment lengths in 0.1-mile (500 ft) increments for roughness specifications (Merritt et al. 2015). For microsurfacing, a similar approach may be followed by a highway agency. 7. Select and Evaluate Quality Measurement Methods The PWL of each lot was estimated before and after microsurfacing. The quality measure DPWL was developed to represent the measure of change in quality and is calculated using Equation A-24. (A-24)PWL PWL MPD PWL MPDafter before( ) ( )∆ = − The DPWL shows how much the construction quality has statistically demonstrated a shift toward or away from acceptable quality. A positive DPWL value indicates an improvement in AQC due to microsurfacing; a negative value indicates a decline in quality. Table A-15 summa- rizes DPWL values calculated for a range of values of MPD within 16 simulated lots. The SLE was calculated for each DMPD as shown in Table A-15. Using the relationship (between DMPD and SLE) and the DPWL measurements, a relationship was developed between SLEs and DPWL. This allows the prediction of pavement performance in terms of SLE as a function of quality levels as shown in Figure A-31. The relationship shows that no change in quality would result in no SLE.

142 performance-Related Specifications for pavement preservation Treatments 8. Develop Pay Adjustment Factors for Incentives and Disincentives The relevant expected pay (EP) and operating characteristic (OC) curves were developed to assign pay factors to appropriate levels of acceptable and rejectable quality while minimizing the expected risks to both the contractor and agency. 1. Predict pavement performance as a function of levels of quality. A relationship between PWL and pavement performance in terms of life extension was established. Figure A-31 shows the performance in terms of quality. 2. Convert the expected performance into pay adjustment. The pay factor is calculated by using Equation A-25, which corresponds to the estimated change in quality ranging from 0 to 100 DPWL because SLE is a function of DPWL (see Figure A-31). Figure A-32 shows the relationship between DPWL and pay factor. ( ) = − − PF C R R R D E O1 (A-25) MPDbefore PWLbefore MPDafter PWLafter SLE MPD PWL 0.3 19.1 1.5 100 1.3 12.5 80.9 0.3 22.5 1.5 100 1.2 11.8 77.5 0.3 26.1 1.5 100 1.2 11.2 73.9 0.4 30.1 1.5 100 1.1 10.5 69.9 0.4 34.3 1.5 100 1.1 9.9 65.7 0.5 38.7 1.5 100 1.0 9.2 61.3 0.5 43.2 1.5 100 1.0 8.6 56.8 0.6 47.8 1.5 100 0.9 8.0 52.2 0.6 52.5 1.5 100 0.9 7.4 47.5 0.7 57.1 1.5 100 0.8 6.9 42.9 0.7 61.6 1.5 100 0.8 6.3 38.4 0.8 66.0 1.5 100 0.7 5.8 34.0 0.8 70.2 1.5 100 0.7 5.2 29.8 0.9 74.1 1.5 100 0.6 4.7 25.9 0.9 77.8 1.5 100 0.6 4.2 22.2 1.0 81.1 1.5 100 0.5 3.8 18.9 Table A-15. Summary of simulated MPD before and after microsurfacing. y = -0.0004x2 + 0.1798x R² = 1 0 2 4 6 8 10 12 14 16 0 20 40 60 80 100 120 SL E (ye ars ) PWL Figure A-31. Performance-based relationship between PWL and SLE.

Appendix A 143 where PF = pay adjustment factor for treatment (same units as C) C = present total cost of treatment, use C = 1 for PF D = design life of pavement or initial overlay E = expected life of pavement or overlay O = expected life of successive overlays R = (1 + INF)/(1+ INT) INF = long-term annual inflation rate in decimal form INT = long-term annual interest rate in decimal form Equation A-26 can be used in the risk assessment to develop OC curves, assess the associ- ated a and b risk, and determine the appropriate AQL and RQL levels necessary to award payment factors. ( )( ) ( )= − × ∆ + × ∆PF PWL PWL% 0.014 3.72 (A-26)2 3. Adjust the AQL, RQL, and pay relationships to minimize risk. As discussed in the determi- nation of AQC limits, the AQL and RQL need to be established. The key principle in any fair payment plan is that a contractor should be awarded 100% pay for producing an AQL quality. For adjustment of AQL, the EP curves must be evaluated such that the payment plan awards 100% pay at AQL while incentive can be given if quality of work is above AQL. Table A-16 shows the pay factors generated from Equation A-26. As seen in Table A-16, the AQL may be chosen to be just above DPWL = 30 to ensure a contractor is not awarded bonus pay for AQL work. For establishing RQL, the EP curves can be used to determine the level of performance (in terms of life extension) that is deemed unacceptable and should result in reduced pay. This decision is typically made to meet the needs of the agency to ensure the pavement performs to established standards. For instance, in the EP curve shown in Table A-16, an agency may decide that a life extension of less than 2 years is undesirable. Therefore, the RQL will be set at DPWL = 10, and any lot produced at a quality level below that will receive no pay. Further, the agency may also decide that any quality between an AQL of 30.3 and an RQL of 10 will be accepted, but will receive a reduced pay or disincentive. Table A-17 summarizes the finalized AQL and RQL for microsurfacing based on performance due to friction number (FN). The OC curves were developed to assess the risk of receiving a y = -0.0139x2 + 3.72x R² = 0.9996 0 50 100 150 200 250 0 20 40 60 80 100 120 Pa y ad jus tm en t (% ) PWL Figure A-32. EP model based on predicted MPD and associated FN.

144 performance-Related Specifications for pavement preservation Treatments payment that correctly corresponds to the level of quality sampled. Figure A-33 shows these OC curves. When evaluating the risks associated with receiving appropriate pay for the predicted change in PWL, Figure A-33 can be examined. Figure A-33 shows the OC curves of desired quality DPWL = 30.3 (i.e., AQL) for a sample size of 3, 5, 10, 20, and 30, respectively. The level of qual- ity produced by a contractor as indicated on the x-axis must be matched with the OC curve with desired quality to evaluate the probability of receiving a pay factor which corresponds to a desired quality. In this case, recall the established AQL of DPWL = 30.3. If a contractor pro- duces AQL quality in the field, then quality level must be matched with the OC curve at AQL. Figure A-33 indicates that the pay adjustment plan will award a pay factor greater than 1 at a probability of 50% for all lots sampled. This suggests that the contractor will receive pay greater than 100% (pay for above AQL) half the time and receive pay less than 100% (pay for below AQL) half the time. Given that several lots will be sampled for quality, this averages to 100% pay throughout the project, which is characteristic of an unbiased and fair adjustment plan to both SLE (yrs)PWL PF% 0 0.0 0.04 5 0.9 19.30 10 (RQL) 1.8 37.51 15 2.6 54.66 20 3.4 70.85 25 4.2 86.13 30.3 (AQL) 5.0 100.00 35 5.8 114.17 40 6.5 127.03 45 7.3 139.18 50 8.0 150.65 55 8.7 161.49 60 9.3 171.72 65 10.0 181.39 70 10.6 190.51 75 11.2 199.13 80 11.8 207.26 85 12.4 214.94 90 12.9 222.17 95 13.5 229.00 100 14.0 235.42 Table A-16. Summary of EP curve for varying microsurfacing quality levels. Quality characteristics Quality levels and pay adjustment AQL ( ) 30.3 AQLSLE (years ) 5 AQLPF (%) 100 RQL ( PWL PWL) 10 RQLSLE (years) 1.8 RQLPF (%) 37.51 Table A-17. Pay factor summary for microsurfacing.

Appendix A 145 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 020406080100 Pr ob ab ili ty o f r ec ei vi ng a p ay fa ct or 1 n=3 5 10 20 30 30.3 AQL = PWL of 30.3 PWL Figure A-33. Predicted OC curves for microsurfacing. AQC(s) MPD Lot size 0.1 miles Sample size Minimum 5 AQC threshold 0.6 MPD (translates to 40 FN) Quality measure Quality thresholds AQL = 30.3 PWL, RQL = 10 Pay Equation PF (%) = -0.014( 2) + 40.2 ( PWL PWL PWL PWL) AQL pay factor 1.0 RQL pay factor 0.375 P(PF>1) at AQL 50% PRS Item Measurement Table A-18. Example PRS for microsurfacing. agency and contractor. This also incentivizes the contractor to consistently aim for above AQL quality to offset the probability of defective construction and receive bonus pay. The greater the sample size, the higher the probability of receiving pay greater than 100% if the produced quality is above AQL. Similarly, the probability of receiving pay greater than 100% is lower if the produced quality is less than AQL. Table A-18 summarizes the example PRS specifications for microsurfacing. References AASHTO (2015) Mechanistic-Empirical Pavement Design Guide: A Manual of Practice, 2nd Edition. ARA, Inc. (2009) NCHRP Project 1-40B, Local Calibration Guidance for the Recommended Guide for Mechanistic- Empirical Pavement Design of New and Rehabilitated Pavement Structures, Final NCHRP Report. Burati, J. L., et al. (2003) Optimal Procedures for Quality Assurance Specifications, FHWA, FHWA-RD-02-095. Chatti, K., et al. (2009) The Effect of Michigan Multi-Axle Trucks on Pavement Distress, Michigan State University, RC-1504. Evans, L. D., et al. (1999) Materials and Procedures for Repair of Joint Seals in Portland Cement Concrete Pavements, Manual of Practice, FHWA, FHWA-RD-99-146. FHWA (1999) TechBrief: Resealing Concrete Pavement Joints, FHWA-RD-99-137. FHWA (2000) “LTPP Manual for Falling Weight Deflectometer Measurements Operational Field Guidelines” FHWA (2014) “Highway Performance Monitoring System Field Manual,” Office of Highway Policy Information, OMB Control No. 2125-0028. Hall, J. W., et al. (2009) NCHRP Report 634: Texturing of Concrete Pavements Transportation Research Board of The National Academies, National Research Council, Washington, DC.

146 performance-Related Specifications for pavement preservation Treatments Henry, J. J. (2000) NCHRP Synthesis 291: Evaluation of Pavement Friction Characteristics, Transportation Research Board of The National Academies, National Research Council, Washington, DC. National Academies of Sciences, Engineering, and Medicine (2012). NCHRP Research Results Digest 371: Quality- Related Pay Adjustment Factors for Pavements. Washington, DC: The National Academies Press. https:// doi.org/10.17226/22656 Merritt, D. K., et al. (2015) Best Practices for Achieving and Measuring Pavement Smoothness, A Synthesis of State-of-Practice, FHWA/LA.14/550. Peshkin, D., et al. (2011) Guidelines for the Preservation of High-Traffic Roadways, SHRP-S2-R26-RR-2. Pierce, L. M., et al. (2003) Ten-Year Performance of Dowel-Bar Retrofit: Application, Performance, and Lessons Learned, Transportation Research Record: Journal of the Transportation Research Board, No. 1853, Transporta- tion Research Board, National Research Council, Washington, DC, pp. 83–91, https://doi.org/10.3141/1853-10 Rajaei, M., et al. (2014) Establishment of Relationship Between Pavement Surface Friction and Mixture Design Properties, Transportation Research Record Journal of the Transportation Research Board, No. 2457, Transporta- tion Research Board, National Research Council, Washington, DC, pp. 114–120, https://doi.org/10.3141/2457-12 Smith, K. and A. Romine (1999) LTPP Pavement Maintenance Materials: SHRP Crack Treatment Experiment, Final Report, ERES Consultants, Inc., FHWA, Turner Fairbank Hwy Research Center, FHWA-RD-99-143.

Abbreviations and acronyms used without definitions in TRB publications: A4A Airlines for America AAAE American Association of Airport Executives AASHO American Association of State Highway Officials AASHTO American Association of State Highway and Transportation Officials ACI–NA Airports Council International–North America ACRP Airport Cooperative Research Program ADA Americans with Disabilities Act APTA American Public Transportation Association ASCE American Society of Civil Engineers ASME American Society of Mechanical Engineers ASTM American Society for Testing and Materials ATA American Trucking Associations CTAA Community Transportation Association of America CTBSSP Commercial Truck and Bus Safety Synthesis Program DHS Department of Homeland Security DOE Department of Energy EPA Environmental Protection Agency FAA Federal Aviation Administration FAST Fixing America’s Surface Transportation Act (2015) FHWA Federal Highway Administration FMCSA Federal Motor Carrier Safety Administration FRA Federal Railroad Administration FTA Federal Transit Administration HMCRP Hazardous Materials Cooperative Research Program IEEE Institute of Electrical and Electronics Engineers ISTEA Intermodal Surface Transportation Efficiency Act of 1991 ITE Institute of Transportation Engineers MAP-21 Moving Ahead for Progress in the 21st Century Act (2012) NASA National Aeronautics and Space Administration NASAO National Association of State Aviation Officials NCFRP National Cooperative Freight Research Program NCHRP National Cooperative Highway Research Program NHTSA National Highway Traffic Safety Administration NTSB National Transportation Safety Board PHMSA Pipeline and Hazardous Materials Safety Administration RITA Research and Innovative Technology Administration SAE Society of Automotive Engineers SAFETEA-LU Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) TCRP Transit Cooperative Research Program TDC Transit Development Corporation TEA-21 Transportation Equity Act for the 21st Century (1998) TRB Transportation Research Board TSA Transportation Security Administration U.S.DOT United States Department of Transportation

TRA N SPO RTATIO N RESEA RCH BO A RD 500 Fifth Street, N W W ashington, D C 20001 A D D RESS SERV ICE REQ U ESTED ISBN 978-0-309-44661-7 9 7 8 0 3 0 9 4 4 6 6 1 7 9 0 0 0 0 N O N -PR O FIT O R G . U .S. PO STA G E PA ID C O LU M B IA , M D PER M IT N O . 88 Perform ance-Related Specifications for Pavem ent Preservation Treatm ents N CH RP Research Report 857 TRB