Quantifying the Effects of Preservation Treatments on Pavement Performance (2018)

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5 Information relevant to pavement performance measures and preservation treatments was reviewed in order to establish a reasonable body of knowledge for use in the evaluation, valida- tion, and testing of the measures, and for the development of guidelines for their implementation. The following three activities were performed: • Review of literature available from various sources. • A web-based survey questionnaire of highway agencies. • Follow-up telephone interviews with highway agency personnel identified from the web- based survey questionnaire. The findings of these activities are described in this chapter. These findings coupled with evaluation criteria developed as part of the project were then used to identify and to recommend the more promising performance measures for evaluation. Literature Review Information available in the literature was reviewed and synthesized to help identify and evalu- ate candidate pavement performance measures. References from various sources were reviewed. These references covered topics such as preservation treatments; contributions of preservation treatments to performance, service life, LCC, and cost effectiveness; performance measures and existing guidelines; and integration of preservation into the pavement management system (PMS). The measures identified from the literature review were broadly classified into four categories: • Individual distress/condition measures. • Composite pavement condition indexes/measures. • Cost measures. • Other measures. According to AASHTO, performance measures for pavement preservation should capture factors related to the following four questions (AASHTO 1999): • Does the treatment enhance pavement performance? • Is the treatment cost-beneficial? • What is the best treatment system to use? • What is the optimum timing for the treatment? A review of pavement performance measures used by state DOTs (Simpson et al. 2013) showed that: • Most measures used by highway agencies can be broadly classified into either individual pave- ment condition measures or composite pavement condition measures. C H A P T E R 2 Review of Existing Practices and Recommended Measures

6 Quantifying the Effects of Preservation Treatments on Pavement Performance • Fifteen states used at least one individual pavement condition measure with ride quality, crack- ing, and rutting being the most common, and 24 states used at least one composite pavement condition measure with a form of the Pavement Serviceability Rating/Index (PSR/PSI) and Pavement Condition Index/Rating (PCI/PCR) being the most common. A brief overview of the various performance measures identified in the literature and the performance measure evaluation framework used by Indiana DOT are provided in the follow- ing sections. Individual Pavement Condition Measures A survey of several states on the types of pavement performance measures used (Corley-Lay 2014) showed International Roughness Index (IRI), rutting, and cracking were most commonly used for asphalt concrete (AC; hereafter referred to as “asphalt”) pavements, while IRI, patch- ing, cracking, popouts, faulting, and damaged joints were most commonly used for portland cement concrete (PCC; hereafter referred to as “concrete”) pavements. For asphalt pavements, rutting and cracking were the two most commonly used measures to assess performance. IRI was the third most commonly used measure although it is consistently applied to both asphalt and concrete pavements. Rutting measurements vary among the states because of the different methods used to gather the data. For concrete pavements, IRI and patching were the most com- monly used measures, followed by faulting and cracking. The survey responses also suggested a need to improve the faulting measure. Composite Pavement Condition Measures The literature review provided information on commonly used composite measures such as distresses and severities, IRI, traffic, and weighting factors. In addition, the various methods used by agencies to develop composite measures including regression analysis and Markov modeling were identified (Mills et al. 2012; Chou et al. 2008; George 2000; Heckel and Ouyang 2007; Giuffre 2010; Wu et al. 2010). Examples of composite pavement condition mea- sures include PCI/PCR, Pavement Quality Index (PQI), Remaining Service Life (RSL), and PSR/PSI. Some agencies have used performance measures that consider life extension or RSL. One such measure used by the Wyoming DOT (WYDOT) is the Number of Years of Life Added (NLA) to the network, which considers the life extension benefits of pavement preservation (Wyoming DOT 2014). This model suggests that, in order to maintain the total network RSL, the average annual NLA for the network should be at least by 1 year. Cost Measures The Metropolitan Transportation Commission (MTC)–the planning organization for the San Francisco Bay Area region, which includes 101 cities and nine counties in Northern California– has implemented a performance-based funding allocation formula that rewards local agencies that employ preventive maintenance efforts (Tan and Cheng 2012). The funding allocation for- mula uses the composite measure PCI to evaluate the networks. In the priority assessment meth- odology used by MTC, feasible maintenance and rehabilitation treatments are usually identified for the first year of the analysis and then prioritized based on agency selected criteria (Tan and Cheng 2012). The methodology considers several years in the analysis period to increase the long-term effectiveness of the decisions being made. LCC, or uniform annual equivalent costs, are considered in the prioritization models, to improve the basis for long-term decisions. Bay Area local agencies reported that the performance models helped them improve the timing

Review of Existing Practices and Recommended Measures 7 of rehabilitation decisions by identifying preventative or minor rehabilitative treatments prior to the time at which only very expensive alternatives can be considered. This capability has improved overall average network pavement condition index, remaining service life, and objec- tivity of decision-making processes. The local agencies also reported that selecting cost effective project strategies and treatments has helped in making the best use of the resources available for pavement preservation (Tan and Cheng 2012). To prioritize maintenance and rehabilitation (M&R) treatments, MTC uses the cost effective- ness as a benefit and develops this into a multiyear program. Cost effectiveness is estimated as the ratio of the area under the PCI versus time and the treatment LCC. The treatment having the highest ratio is the most cost effective and will be selected. Projects that do not get selected due to limitation of funds are deferred to future years. Washington State DOT (WSDOT) has implemented rehabilitation strategies to maintain their system in good condition with the investment levels available, and to manage to the lowest LCC (LLCC) for their investments. Two performance measures are used as indicators for asphalt pavements: the discounted equivalent uniform annual cost (EUAC), expressed as dollars per lane mile per year, and the equivalent single axle load (ESAL) efficiency factor (Rydholm and Luhr 2014). These measures are expressed by the following equations (Luhr et al. 2010; Rydholm and Luhr 2014):   1 1 1 (1) ( ) ( ) ( ) = + + − EUAC Agency cost i i i n n (2)ESAL efficiency factor EUAC ESALs = where i = the discount rate (assumed at 4%); n = the service life or the analysis period in years; and ESALs = equivalent single axle load for the full time of service life or analysis period. In addition to these economic performance indicators, WSDOT also evaluates the future risk by considering the backlog of rehabilitation and reconstruction needs. By deferring needed rehabilitations due to funding constraints, pavements continue to deteriorate with time and will then require more extensive and costly treatments (Luhr et al. 2010). The back- log of rehabilitation needs measure reflects the role of preservation in extending pavement life while reducing the number of needed rehabilitations. It estimates the amount of lane miles that have triggered the need for rehabilitation, but have not been addressed due to lack of funding. Other Measures Many measures focus on meeting short-term needs, and do not predict future trends or net- work condition and needs. Measures that are calculated based on asset management principles have the potential to address these concerns. For example, other performance measures included in WSDOT’s Forecaster Tool allows various prioritization options (Rydholm and Luhr 2014): • Deferred preservation liability. The liability, in dollars, of not performing needed preservation activities can be calculated by assuming cost of the preservation, restoration, and reconstruc- tion (PRR) activity that will be assigned in the following year. The economic ramification of not performing preservation will demonstrate the importance of asset management and proper funding.

8 Quantifying the Effects of Preservation Treatments on Pavement Performance • RSL. This measure is calculated as the variation of a reference year from the expected year for a LLCC activity. • Asset sustainability ratio. This measure indicates the extent assets are being replenished rela- tive to the rate they are being consumed. Lane mile years (LMY) replenished is calculated per year as the product of lane miles of pavement with PRR activities and the number of years of expected service life added by the PRR activity. Pavement consumption is the number of lane miles of the network which are consumed in one year (LMY). The asset sustainability ratio is the ratio of LMY replenished to LMY consumed. • Risk. This measure is a risk register that conveys the risk associated with deteriorated pave- ments. A risk register documents the various risk sources, probability, impact, score, mitiga- tion strategy, and responsibility. The risk being described is a generic traffic and vehicle user event and purposely left non-specific because analyzing risk for a specific type of event is too difficult to predict. Roadways are assigned a likelihood, consequence, and impact in the risk register. • Equivalent Failure Factor (EFF). This measure is the ratio of the cost of reconstruction needs to the cost to reconstruction of the entire pavement network (multiplied by 100 for conve- nience). The EFF communicates network stability without specific dollar amounts and indi- cates when a pavement network has reached a point at which reconstruction can be addressed with available funding (referred to as critical instability). NCHRP Report 551 identified a set of performance measures that could best serve the principles of good asset management (Cambridge Systematics et al. 2006). For the preservation of pave- ment assets category, it identified PCI, RSL, and debt index as performance measures. The debt index is the ratio of lost value due to deterioration to replacement value, which requires the valuation of pavements. The key drivers for highway infrastructure asset valuation are (Roads Liaison Group 2005): • Emphasize the need to preserve the highway infrastructure by placing a monetary value on highway infrastructure assets. • Demonstrate asset stewardship by monitoring asset value over time. • Promote greater accountability, transparency, and improved stewardship of public finances. • Support highway asset management—asset valuation provides one facet of the robust finan- cial framework that asset management should operate within. Several agencies have developed performance measures based on asset management prin- ciples, such as the pavement sustainability ratio and sustainability gap that are used by the Utah and Ohio DOTs, respectively (Proctor et al. 2012). The pavement sustainability ratio is defined as the ratio of the available pavement budget to the budget needed for a pavement network. The sustainability gap (also referred to as the investment gap) is the difference between the available budget and the budget need for a pavement network. Both of these measures are not unique to pavements, and can be implemented across many asset types, which means that many asset types can be directly compared to pavements using these measures (Proctor et al. 2012). Many agencies outside of the United States have considered or implemented performance measures based on asset management principles, such as depreciated replacement cost (DRC), which considers the difference of the gross replacement cost and the accumulated consumption (Roads Liaison Group 2005).The DRC, which represents the pavement condition converted to percent of depreciated replacement cost, encourages an asset management approach (Topp 2014). The asset sustainability index (ASI) is defined as the ratio of the amount budgeted for highway infrastructure preservation divided by the amount needed to adequately sustain infra- structure at a targeted condition over the long-term. This metric was first used in Australia and the United Kingdom and is gaining recognition by U.S. transportation agencies (Proctor et al. 2012); it is similar to the asset sustainability ratio.

Review of Existing Practices and Recommended Measures 9 The State of Queensland, Australia uses an asset consumption ratio (ACR), which is the value of infrastructure assets divided by gross current replacement cost of infrastructure assets (Proctor et al. 2012) as well as the asset renewal funding ratio (ARFR) (State of Queensland 2013). The ARFR is the net present value of the planned capital expenditures on renewals over 10 years divided by the net present value of the required capital expenditures on renewals over the same period (Proctor et al. 2012). The ARFR is expressed as a percent and it represents the extent to which the required capital expenditures on renewals per the asset management plans have been incorporated into the 10-year financial model of the local government (Proctor et al. 2012). Evaluation Framework After identifying performance measures, a framework for evaluating these measures was sought. One specific example of significant benefit to the project was found in the Indiana DOT study that considered the potential of integrating pavement preservation considerations within the network-level PMS, which included the following elements (Ong et al. 2010): 1. Determine triggers for pavement preservation treatments for use in PMS. 2. Develop performance models for preservation treatments. 3. Develop a RSL approach for strategy comparison at the project level. 4. Develop pavement preservation framework that integrates the districts and the central office of a state highway agency. Long-term performance models for IRI, PCR, and rut depth were developed using pave- ment performance data from the Indiana pavement management databases for interstates and national highway system (IHS and NHS) for asphalt pavements. Also, long-term performance models for asphalt and concrete non-NHS pavements were derived. Costs were not included in these performance models. Short-term models to show the effectiveness for preservation treatments were developed based on the performance jump (PJ), which is expressed in the following form: (3)PJ y yb a= − where yb = the condition before treatment; and ya = the condition after treatment. Deterioration rate reduction (DRR) is also included for some measures. Regression analysis was used in determining coefficients for the performance models. The process for selecting the optimal pavement preservation strategy for each pavement sec- tion is depicted in Figure 2. The process incorporates the developed models and the remaining service life concept, and highlights the PJ and DRR resulting from applying preservation, which are the fundamental elements used to validate the recommended performance measures (more details provided in Chapter 3). Survey Questionnaire Findings The literature review was supplemented with a survey of highway agencies and Canadian provinces and territories to determine whether or not these agencies are actively involved with pavement preservation and, if so, how they consider the contribution of preservation treatments to pavement performance, life, and LCC. The survey also helped identify agencies to provide data and other information for use in evaluating the pavement performance measures proposed in this research.

10 Quantifying the Effects of Preservation Treatments on Pavement Performance In all, 33 of 50 (or 66%) state highway agencies and 9 of 13 (or 69%) Canadian provinces and territories completed the questionnaire (a 67% overall response rate) although not every agency responded to all questions. The major findings from the survey were: • 40 of the 42 responding agencies (95%) routinely apply pavement preservation. • 37 of 40 responding agencies (93%) who routinely apply pavement preservation treat- ments believe that pavement preservation treatments contribute to improved performance, increased life, and/or reduced LCC. • 27 of 37 responding agencies (73%) who consider preservation treatments as beneficial, use performance measures to evaluate effectiveness of preservation treatments. • 21 of 31 responding agencies (68%) reported that performance measures have led to improve- ments in selecting preservation treatments and/or optimizing resources to improve overall network condition. • 33 of 39 responding agencies (85%) have considered integrating preservation into pavement or maintenance management systems. • 26 of 27 responding agencies (96%) reported that integration of pavement, construction and/or maintenance management systems will allow agency to predict future performance in terms of distress, overall condition, or other indicators. In addition, the survey identified other relevant information, such as the most commonly used preservation treatments for both asphalt and concrete pavements. Figure 3 and Figure 4 present the reported use of preservation treatments for asphalt and concrete pavements, respectively. The research team also conducted telephone interviews with highway agencies from seven states (Colorado, Maryland, Ohio, Texas, Utah, Washington, and Wyoming) and two provinces (Alberta and New Brunswick) to obtain additional information, reference material, and guidelines relating to pavement performance measures and their use. These interviews Figure 2. Process for selecting optimal pavement preservation strategy (Ong et al. 2010).

Review of Existing Practices and Recommended Measures 11 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Surface abrasion Bonded wearing course (Nova Chip) Patching Mill and fill Other seal Slurry seals Fog seals Crack seal Microsurfacing Chip Seal Thin overlays Percent of Respondents Total Number of Respondents (n) = 42 37 (88%) 29 (69%) 33 (79%) 3 (7%) 6 (14%) 7 (17%) 9 (21%) 9 (21%) 20 (48%) 3 (7%) 1 (2%) Figure 3. Asphalt preservation treatments used. Other Crack sealing Slab replacement Spall repair Full depth patching DBR Joint sealing Partial depth patching Diamond grinding 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percent of Respondents Total Number of Respondents (n) = 33 29 (88%) 24 (73%) 3 (9%) 4 (12%) 13 (39%) 15 (45%) 16 (48%) 22 (67%) 1 (3%) Figure 4. Concrete preservation treatments used. helped obtain detailed information on the performance measures used by those agencies and how they were implemented within the agency. The resulting information was then used to identify promising pavement performance measures, to set parameters for evaluating these mea- sures, and to develop implementation guidelines. Candidate Performance Measures As a result of the information gathering effort, a list of potential pavement performance measures was developed, and these measures were grouped into four categories: • Individual measures of pavement condition (ride quality, safety, surface condition, and struc- tural capacity). • Composite pavement condition indexes derived based on individual pavement condition measures. • Cost related performance measures (e.g., LCC).

12 Quantifying the Effects of Preservation Treatments on Pavement Performance • Other measures (e.g., environmental, user satisfaction, etc.) derived from/driven by indi- vidual and/or composite performance measures and asset type measures. The evaluation of performance measures is described in several sources (Cambridge System- atics et al. 2010; Grant et al. 2013; U.S. Army Corps of Engineers 2015), and each of these sources details many criteria that were considered in evaluating performance measures in this project. To help identify those measures that merited further consideration in this project (and/or to determine the need to develop new measures), the following evaluation criteria were considered: • Feasibility – includes general considerations and measure characteristics, such as: – Applicability of the performance measure to multiple pavement types and preservation treatments. – Ability to collect data in support of the performance measure in a precise and accurate manner. • Understanding – includes considerations for implementation and communication, such as: – Ease of use and ease of understanding or interpreting the measure. – Objectivity of the measure (i.e., the ability to consistently calculate the measure by different agencies or different personnel at the same agency). • Usefulness in decision-making – includes considerations, such as: – Use of the performance measure in pavement management applications (e.g., provides a criterion upon which agencies can base decisions). – Sensitivity of the performance measure to changes in performance, service life, and LCC due to the application of preservation. Each of the identified measures was evaluated using the criteria and other information found in the literature. Several performance measures are based on single indicators that are widely collected by many agencies. These measures had fair to high feasibility, high understanding, and fair to good usefulness in decision-making. These measures include: • Ride quality (generally measured as the IRI) – a measure of the unevenness of the pavement as expected to be perceived by users. • Cracking – many types of cracking can be measured; generally classified in terms of load related (e.g., fatigue cracking) and non-load related (e.g., transverse cracking). • Rutting – a measure of the unevenness in the transverse profile of the pavement. • Faulting – a measure of the difference in elevation of the joint between two adjacent slabs in a concrete pavement (applies only to jointed concrete pavements). Other measures based on single indicators were identified, but were not as widely collected or used in decision-making. These measures had fair feasibility, fair to high understanding, and low to fair usefulness in decision-making. These measures include: • Bleeding – a measure that describes the phenomenon of a film of asphalt binder rising to the surface of the pavement over time. • Raveling – a measure of the loss of the aggregates in the surface layer of the pavement. • Oxidation – a measure of the aging of the asphalt in the surface layer due to reaction with oxygen. • Spalling – a measure of the amount of breaking or chipping in the surface of a concrete pave- ment, typically at joints or cracks. • Pumping – a measure of the movement of subsurface material through cracks to the surface layer due to water pressure in concrete pavements. • Blow-ups – a measure of the upward movement of slabs and subsequent breaking of the sur- face layer at joints in concrete pavements. • Friction (skid resistance) – a measure of the resistive force of the pavement surface to the slip- page and skidding of tires.

Review of Existing Practices and Recommended Measures 13 • Surface texture – a measure of the differences in height of the pavement surface caused by aggregates (macro texture) or by small changes in particle shape of the aggregate or binder (micro texture). • Noise – a measure of the sound generated by a tire traveling along the surface of the pavement. • Accident/crash rates – a count-based measure that is not a characteristic of the pavement, but somewhat attributable to pavement condition. Many measures were identified that describe the structural condition of pavements based on the deflection of the pavement at given locations due to the application of known loads. These measures have not been widely adopted in pavement management applications, though it has been shown that they can significantly enhance decision-making (Bryce et al. 2013). It was found that these measures had low to fair feasibility, fair understanding, and fair to high usefulness in decision-making. These measures include: • Surface deflection – a measure of the amount of recoverable deformation in the pavement surface due to the application of a load of known magnitude. • Structural condition index – a measure that combines surface deflection data with traffic and subgrade soil information to estimate the structural adequacy of the pavement. • Structural adequacy index – a measure similar to the structural condition index, it combines surface deflection data with traffic, climactic, and subgrade soil information to estimate the structural adequacy of the pavement. Many measures were identified that are combinations of the individual indicators (e.g., crack- ing, faulting, etc.). The algorithms for combining the individual indicators into the composite measures are generally very extensive; they are not discussed in detail in this section. However, the general approach to calculating the composite measures consists of four steps: 1. Assign each pavement the best possible score, representative of a pavement with no distresses (e.g., a PCI of 100). 2. Measure the type, extent, and severity of the distresses present on the pavement. 3. Translate the measured distresses into a scaled value (e.g., a deduct value) and combine the deduct values. 4. Combine the original score from Step 1 with the scaled values in Step 3 to arrive at a composite measure of pavement condition. It was found that the composite measures had low to fair feasibility, fair to high understanding, and fair to high usefulness in decision-making. Many of these measures were discussed previously; they include: • Pavement condition index/rating (PCI or PCR). • Pavement quality index. • Distress index. • Pavement serviceability index/rating (PSI or PSR). • Pavement structural condition. The literature review identified many cost-based performance measures. These measures require the calculation of other performance measures in order to define an analysis period and level of performance expected from the treatment. It was found that these measures had low to fair feasibility, high understanding, and high usefulness in decision-making. These measures include: • LCC – a measure of the expected cost associated with the application of a preservation treat- ment, as well as future costs incurred during a defined time frame, typically presented as a single cost in terms of net present value. • ESAL efficiency – a measure of the ratio of the LCC to the expected ESALs on the pavement.

14 Quantifying the Effects of Preservation Treatments on Pavement Performance • Historical cost of pavement service – a measure not based on pavement condition measures, but an evaluation of past costs to identify pavement segments that may be candidates for preservation versus those that may require rehabilitation. • Expected cost of future pavement rehabilitation – similar to LCC, a measure of the cost of future rehabilitation needs based on decisions made in the present. The literature review also identified a set of performance measures that evaluate the costs associated with a large group of pavements. In this case, the network of pavements is evaluated, and a single measure is assigned to the network based on decisions made for each segment. Many of these measures compare the costs required to manage a pavement network to a specified level of service to the available budget for managing the network. Many of these measures were described previously in this chapter. It was found that these measures had low to fair feasibility, high understanding, and low usefulness in decision-making (when compared to decisions made for a specific pavement segment); these measures include: • RSL. • Deferred maintenance costs. • Deferred preservation liability. • ASI. • ACR. • ARFR. • PSI – this measure is similar to the asset sustainability index, with a specific focus on pavements. • Sustainability gap. • EFF – this measure is the cost to perform the reconstruction needs of the pavement network divided by the cost to reconstruct the entire pavement network. The performance measures evaluation criteria was re-visited during preparation of the guide document and are summarized as follows: • Performance measures should be directly linked to the objectives of pavement management within an agency and consider the following: – The objectives of pavement management within the agency. – The extent of capturing the aspects of pavement performance that are considered important by the agency. – The contribution of preservation to the performance of pavements. – The accuracy in estimating this contribution (if any is found). • General considerations, including: – Number of performance measures. � Address full range of pavement types that exist within agency’s pavement network. � Address full range of preservation treatments used by agency. – Data availability. � Measurements of pavement condition immediately prior to and immediately after applica- tion of the treatment. � Measurements of long-term pavement condition data after application of treatment. � Reference or control measurements • Measure characteristics, including: – Data quality. � Data accuracy. � Data precision. – Risk impacts. – Quantifiable. • Measure implementation. – Ease of use. – Ease of implementation.

Review of Existing Practices and Recommended Measures 15 Finally, the literature review revealed two key uses for performance measures, as illustrated in Figure 5. Specifically, performance measures should have the ability to be used to select mainte- nance actions for specific projects, and also to be aggregated over a pavement network to com- pare against an agencies strategic goals. Given that the evaluation of an agency’s strategic goals are a key part of asset management plans (CFR 2016), performance measures should also have the ability to be forecast over time. Recommended Performance Measures As noted previously, performance measures should be directly linked to the agency’s pave- ment management objectives. Therefore the performance measure needs to capture the initial and long-term effects of preservation. The performance measures must be supported by avail- able data to be useful in assessing the effect of preservation treatments. The quality of the data, in terms of accuracy and precision, is important for successful implementation of the performance measures. Finally, the performance measures should be well founded and easy to use so that they can be readily accepted and implemented. A summary of some of the benefits and concerns of the potential performance measures is presented in this section. Individual pavement condition measures such as IRI, cracking, rutting, and faulting provide the following benefits: • They are used by most state highway agencies in the day-to-day pavement related activities, and thus the agencies are familiar with them. • State highway agencies are required to report these measures as part of the Highway Perfor- mance Monitoring System (HPMS) (FHWA 2014). • They are suggested in the proposed rulemaking for pavements under the Moving Ahead for Progress in the 21st Century Act (MAP-21) legislation for monitoring, tracking, and reporting on the condition of the state’s IHS and NHS pavements. • They include many of the surface deterioration and defects that typically trigger preservation treatments. • They can be objectively measured. Although IRI, cracking, rutting, and faulting have many benefits when used as a performance measure, they also present concerns. First, they are lagging indicators because their values reflect past deterioration, and not necessarily indicative of future performance. Secondly, none of the individual measures provide a complete picture of the pavement condition. Performance measures should be linked to strategic goals of an agency, and changes in perfor- mance measures should be reflected when comparing the condition of the network to strategic Project-Level Decisions Network-Level Management Evaluate Specific Maintenance Decisions Evaluate Strategic Goals of Agency Evaluate Changes in Performance, Service Life and LCC for Segment Evaluate Changes in Condition for Pavement Network Performance Measures Aggregation of Project-Level Informs Network-Level Figure 5. Key roles of performance measures.

16 Quantifying the Effects of Preservation Treatments on Pavement Performance goals. For example, safety is a strategic goal for many agencies, and pavement friction has been shown to be directly related to the expected number of crashes along the pavement segment (de Leon Izeppi et al. 2016; Ivan et al. 2012). Safety-related pavement performance measures have been considered by some highway agencies. For example, Maryland SHA routinely measures pavement friction statewide and reports the results in terms of the friction number (Speir et al. 2009), and Virginia DOT has conducted pilot studies for network-level friction testing (de Leon Izeppi et al. 2016). The use of friction is outlined in the FHWA Technical Advisory 5040.38. However, a few agencies maintain historical friction data, such that the effect of preservation on friction can only be modeled in limited cases. Furthermore, friction is a threshold based factor; pavements with low friction values are scheduled for maintenance, but maintenance for pave- ments with high friction values is driven by other measures. Thus, use of friction as a perfor- mance measure requires many agencies to undertake a significant effort to collect relevant data, define treatment effect models, and develop friction-related performance models. Composite measures are also used by many state and local highway agencies. However, there is no consensus between these agencies regarding the distresses or the factors that should be included in these measures. In addition, preservation affects different deficiencies in different ways, as opposed to rehabilitation or reconstruction, which generally correct all functional defi- ciencies. Given that composite measures are a combination of many deficiencies, the measured effect of preservation on performance, service life, and LCC is highly dependent on how the composite measure is defined. For example, it would be inaccurate to say that there will be a positive change to the PCI if a preservation treatment such as fog seal is applied while the reason for a low PCI was rutting. In this way, composite measures do not clarify which distresses are contributing to the score. However, because many local agencies use MicroPAVER , StreetSaver, or other similar systems to manage their pavements and often employ a composite measure to make decisions, a set of composite measures was obtained from highway agencies and evaluated using the recommended framework. Furthermore, many of the measures intended for asset management applications (e.g., the asset sustainability index) were too complex and not adequately verified in the context of pave- ment preservation. In addition, many of these measures are not implementable by many high- way agencies for assessing the effect of preservation on individual pavement segments because they are an aggregation of all pavement segments needing many types of M&R. The measures based on asset management principles are dependent on the individual pavement condition measures or composite condition measures to predict M&R needs. The following performance measures were selected for further testing and validation: • Individual pavement condition measures, including: – Ride quality (IRI), for both asphalt and concrete pavements. – Cracking, for both asphalt and concrete pavements. – Rut depth, for asphalt pavements (and possibly for concrete pavements where there is sur- face abrasion from studs or chains). – Faulting, for concrete pavements only. • Composite pavement condition measures. Given that most agencies employ a composite con- dition measure, a group of composite measures was collected from the state agencies involved in the testing and validation effort for further evaluation. The specifics of these measures are detailed in Chapter 3. • Cost measures. Given the documented success of cost-based measures used by WSDOT, the equivalent uniform annual cost was selected for further testing and validation. It is recognized that there are many types of cracking that an agency may measure (e.g., fatigue cracking, transverse cracking, etc.), and that, even for the same cracking type, agencies may

Review of Existing Practices and Recommended Measures 17 have varying practices as to what should be included in that type. For example, different results were obtained when the immediate change in longitudinal cracking following a chip seal appli- cation was evaluated by some agencies. The difference was attributed to how each agency defines longitudinal cracking (e.g., some included construction joints as cracking). This research does not recommend a specific cracking type to measure; it only defines data quality measures that should be considered when selecting the cracking type. Similarly, both rutting and faulting may be measured using different techniques. For example, rutting can be measured using point measurements (e.g., using three or five sensors) or scanning lasers and faulting may be measured by laser profiler or laser cracking measurement systems (LCMS). This research does not recommend a specific measurement methodology, but suggests (in the guide) a level of quality at which the data should be collected. The ability of the recommended performance measures to show the contributions of preser- vation treatments has been evaluated using data from the Long-Term Pavement Performance (LTPP) database and from participating highway agencies; other performance measures can be evaluated in a similar manner. The process for evaluating the performance measures is presented in Chapter 3 and the guide developed in this project illustrates how highway agencies can intro- duce alternate performance measures.