Quantifying the Effects of Preservation Treatments on Pavement Performance (2018) / Chapter Skim
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A-1 A T T A C H M E N T Guide for Pavement Preservation Performance Measures
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A-2 C O N T E N T S A-3 Chapter A-1 Introduction A-3 A-1.1 Background A-3 A-1.2 Objective A-4 A-1.3 Using this Guide A-4 A-1.4 Implementation Overview A-7 A-1.5 Organization of Guide A-8 Chapter A-2 Performance Measures A-8 A-2.1 Background A-10 A-2.2 Selection Criteria A-12 A-2.3 Recommended Performance Measures A-13 A-2.4 Alternate Performance Measures A-15 Chapter A-3 Data Requirements A-15 A-3.1 Overview of Data Requirements A-18 A-3.2 Minimum Data Required to Implement Performance Measures A-21 A-3.3 Desirable Data Elements for Implementing Performance Measures A-22 A-3.4 Obtaining Data for Developing Models in Support of Performance Measures A-23 A-3.5 Data Availability and Quality A-25 Chapter A-4 Implementation Process A-25 A-4.1 Calculating Initial Change in Condition A-26 A-4.2 Calculating Changes in Performance A-28 A-4.3 Calculating Performance, Service Life, and LCC A-31 Chapter A-5 Summary A-31 A-5.1 Performance Measures Implementation A-31 A-5.2 Other Considerations A-31 References A-35 Appendix A Case Studies A-35 AA-1 Initial Condition Jump and Long-Term Performance Changes A-46 AA-2 Calculating Changes in Performance, Service Life, and LCC A-53 AA-3 Implementation of Performance Measures A-59 AA-4 References
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A-3 A-1.1 Background Pavement preservation provides a means for maintaining and improving the functional condition of an existing pavement segment through application of a preventative and responsive set of treatments that slow deterioration or correct isolated pavement defects (i.e., it defers costly pavement rehabilitation or reconstruction to a later time)
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A-4 Quantifying the Effects of Preservation Treatments on Pavement Performance • Does application of preservation treatments affect the life of a given pavement segment, and how does this contribute to the overall serviceability of a pavement network? Do these treatments help maintain the level of service above a specific threshold and delay application of more costly rehabilitation treatments?
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Attachment A-5 Figure A-1.1. General approach to implementation of pavement preservation performance measures.
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A-6 Quantifying the Effects of Preservation Treatments on Pavement Performance A-1.4.1 Selection of Performance Measures The highway agency selects the pavement preservation performance measures to use from those recommended in this guide, those identified by the agency, or a combination of the two. This guide recommends criteria for identifying and selecting performance measures as well as a process for implementing these measures.
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Attachment A-7 A-1.5 Organization of Guide This guide is organized into five chapters. This chapter provides background information, guide objective, overview of guide implementation process, and organization of the guide.
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A-8 This chapter discusses the selection of performance measures, presents a set of recommended measures, and discusses the use of agency specific measures. Figure A-2.1 presents a flow chart that illustrates the process for selecting a set of performance measures that can be used to develop models to assess the effectiveness of their preservation practices.
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Attachment A-9 Thus, pavement performance measures (as defined in this guide) are metrics that quantify the degree of achieving specific goals pertaining to performance.
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A-10 Quantifying the Effects of Preservation Treatments on Pavement Performance A-2.2 Selection Criteria The selection of performance measures should be directly linked to the agency's pavement management objectives. Some of these objectives will be common among many agencies, such as those related to maintaining an acceptable level of ride quality or maintaining an acceptable level of service for the lowest LCC.
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Attachment A-11 – Long-term (preferably more than five years) pavement condition data for each combination of pavement type and preservation treatment.
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A-12 Quantifying the Effects of Preservation Treatments on Pavement Performance natively, the agency could decide to use the performance measures they are familiar and comfortable with, if they are supported by available data. A-2.3 Recommended Performance Measures In the development of this guide, a number of performance measures were evaluated in terms of their ability to capture the effects of preservation treatments on pavement performance (Rada et al.
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Attachment A-13 A-2.4 Alternate Performance Measures The recommended performance measures are considered satisfactory for quantitatively incorporating the effects of preservation treatments into the pavement management decision-making process for many cases. Nonetheless, highway agencies may choose to use other measures (e.g., friction)
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A-14 Quantifying the Effects of Preservation Treatments on Pavement Performance have shown to capture the effects of preservation treatments on pavement condition (Rada et al.
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A-15 The previous chapter detailed the selection of performance measures, presented a set of recommended measures, and discussed several agency specific performance measures. The next step is to gather data to support the implementing of the measures (Figure A-3.1)
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A-16 Quantifying the Effects of Preservation Treatments on Pavement Performance The data in Figure A-3.2 show that pavement condition data are expected to have variability that would influence the conclusions about the effectiveness of preservation if evaluated on a segment-by-segment basis. As shown the calculated condition jump using data from a single segment is less than the condition jump calculated using data from several segments.
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Attachment A-17 δ = tolerable margin of error; and c = number of categories in which the data are grouped (e.g., discretizing the pavements into good, fair, or poor condition, each with either high or low traffic results in c = 6)
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A-18 Quantifying the Effects of Preservation Treatments on Pavement Performance The tolerable margin of error is defined by the agency and should be selected based on desired precision and resolution for the models. However, in absence of a known precision or resolution, an estimate for the tolerable margin of error is the measurement error.
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Attachment A-19 complications may arise if the proper data are not available. The basic steps needed to calculate the condition jump and associated data are the following: 1.
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A-20 Quantifying the Effects of Preservation Treatments on Pavement Performance • Reviewing the pavement condition database to determine if data stored for smaller increments is available. For example, many agencies collect and input data for small increments (e.g., 0.1 mile)
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Attachment A-21 When developing performance models using multiple pavement segments, it's generally assumed that this group of pavement segments is representative of the population of pavement (i.e., pavements are of the same type, in the same functional class, and belong to the same population)
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A-22 Quantifying the Effects of Preservation Treatments on Pavement Performance A-3.4 Obtaining Data for Developing Models in Support of Performance Measures Many sources exist for gathering the data required for developing models in support of the performance measures. However, it is important to recognize that certain data should be prioritized above other data, and the data used to develop the models are assumed to be representative of the pavement network.
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Attachment A-23 A-3.5 Data Availability and Quality A primary consideration in selecting data source to support implementation of the performance measures is the availability of the data. Ideally, the agency has available complete sets of data spanning many years of condition metrics that can be used to calculate the performance measures for the desired preservation treatments.
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A-24 Quantifying the Effects of Preservation Treatments on Pavement Performance treatment is adopted to improve safety (e.g., by increasing friction or reducing splash and spray) , then the condition metric should accurately reflect changes in safety.
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A-25 This chapter details the process for implementing the performance measures and provides guidance on how to use these measures to assess the effectiveness of preservation. The approach to calculating the change in condition and changes in performance following preservation and the use of the measures to assess the effects of preservation is presented in Figure A-4.1, and is discussed in detail in the following sections.
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A-26 Quantifying the Effects of Preservation Treatments on Pavement Performance 3. Perform a regression where the independent variable is the condition before the application of preservation, and the dependent variable is the calculated difference.
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Attachment A-27 following a thin overlay is given in Appendix A This approach is preferred, but it requires much more data to complete.
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A-28 Quantifying the Effects of Preservation Treatments on Pavement Performance This approach was used for the examples presented in Appendix A For example, if changes in the performance measure versus time are assumed to be linear for the time period of condition measurements, then a linear regression model can be fit to the data, and the slope of the regression can be used for estimating differences in performance.
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Attachment A-29 This model can be developed as part of implementation of the measures (presented in Appendix A for estimating rut growth following a thin overlay) , or otherwise an existing deterioration model may be used.
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A-30 Quantifying the Effects of Preservation Treatments on Pavement Performance approach that describes the cost per year of life extension while accounting for the discounted worth of money over time is recommended. Equation 4-2 is used to calculate the EUAC.
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A-31 A-5.1 Performance Measures Implementation This guide detailed a 3-step process for the implementation of performance measures to assess the effects of preservation treatments on pavement performance, service life, and LCC. The first step detailed how to select performance measures of the suggested four measures, and described a method to identify alternative measures.
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A-32 Quantifying the Effects of Preservation Treatments on Pavement Performance Bergdahl, M., Ehling, M., Elvers, E., Földesi, E., Körner, T., Kron, A., Lohauß, P., Mag, K., Morais, V., Nimmergut, A., Viggo Sæbø, H., Timm, U., João Zilhão, M
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Attachment A-33 Speir, R., T Puzin, R
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A-35 This appendix presents 10 case studies that illustrate important concepts for consideration in implementing performance measures to assess the effect of preservation treatments on pavement performance. Four case studies illustrate the effect of data quality on the initial condition jump and long-term performance model development, and the benefits of collecting additional explanatory variables.
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A-36 Quantifying the Effects of Preservation Treatments on Pavement Performance Microsurfacing data from three Virginia districts was gathered to evaluate transverse cracking and rutting. The data provided condition for data on 0.1-mile segments from 2007 to 2015, as well as data pertaining to pavement functional class, average annual daily truck traffic, pavement thickness, and pavement structural number as obtained from deflection testing (the structural number and thickness information were provided only for interstate pavements on a 0.2-mile interval)
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Attachment A-37 was assumed that cracking deteriorated linearly when assessing whether the growth rate of transverse cracking was only a function of the treatment type or a function of additional explanatory variables. For this evaluation, a robust linear regression model was fit to each segment for which at least five years of data following treatment application was available to estimate a growth rate transverse cracking following the process described in Section AA.1.2.
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A-38 Quantifying the Effects of Preservation Treatments on Pavement Performance No control segments were provided by the Virginia DOT to enable assessment of the differences in the distress growth rates between treated and control segments. However, analysis on segments that are selected as controls would determine whether the distress growth rates following preservation are significantly different from those for unpreserved pavements.
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Attachment A-39 To assess the performance in terms of faulting for each segment following diamond grinding, a robust linear regression model was fit to the data for each segment. A linear fault growth was assumed to assess whether the growth rate of faulting was only a function of the treatment type or additional independent variables.
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A-40 Quantifying the Effects of Preservation Treatments on Pavement Performance measurement errors, it should not be expected that all segments would be shown to have eliminated transverse cracking completely. The change in roughness following a chip seal is shown in Figure AA-4.
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Attachment A-41 there is significant variability in the data. The figure shows that transverse cracking is expected to grow more quickly when a chip seal is placed on a pavement with a higher number of transverse cracks.
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A-42 Quantifying the Effects of Preservation Treatments on Pavement Performance the line fitting the data points as a function of time following treatment application was calculated using a robust regression technique that iteratively applies weights to the individual residual values. The median values for traffic, number of freeze-thaw cycles, yearly rainfall, structural number, and yearly temperature were also gathered for each test section.
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Attachment A-43 Rutting following the overlay application was evaluated for both control test sections and treatment test sections; the comparison is shown in Figure AA-9. The figure shows that the growth rate of the control test sections tend to be higher than for treatment test sections (i.e., more data are below the line of equality)
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A-44 Quantifying the Effects of Preservation Treatments on Pavement Performance Figure AA-9. Rut growth rate for treatment and control segment.
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Attachment A-45 The change in faulting in the wheelpath resulting from diamond grinding is shown in Figure AA-12. The figure shows that the majority of test data are not on the line of equality that defines the situations in which faulting is eliminated following diamond grinding indicating that some faulting remains after grinding.
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A-46 Quantifying the Effects of Preservation Treatments on Pavement Performance However, the majority of pavements did not have an adequate number of faulting measurements following diamond grinding. Because a small number of pavements (less than 10)
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Attachment A-47 section. The weighted distress for the treatment test section is 70.0 in./mile, and the weighted distress for the control test section is 69.4 in./mile.
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A-48 Quantifying the Effects of Preservation Treatments on Pavement Performance AA-2.2 Calculating Performance, Service Life, and LCC The following example illustrates the procedure for estimating changes in performance and service life for the thin overlay examples presented in the previous section. The IRI performance model is given by Equation A-1; IRI is expressed in terms of in./mile.
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Attachment A-49 The following steps were taken to estimate the effect of the thin overlay on pavement performance: Step 1: Define t1, t2 and t3 The value for t1 is defined as the effective age of the pavement when preservation is applied. This can be calculated using Equation A-1: 100 40 exp 1 0.05 ln 100 40 18.30.05 11 tt= → = = The value for t2 is defined as the effective age of the pavement when the deterioration of the control segment reaches the threshold value; also calculated from Equation A-1: 120 40 exp 1 0.05 ln 120 40 22.00.05 21 tt= → = = The value for t3 is defined as the time required for the treated segment to reach the deterioration threshold value.
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A-50 Quantifying the Effects of Preservation Treatments on Pavement Performance Step 2: Calculate the Change in Service Life The change in service life can be estimated using the values for t2 and t3 as follows: Change in service life 28.7 22.0 6.73 2t t years= − = − = Plus, the application of the preservation treatment extended the time during which pavement IRI would be less than 120 in./mile by nearly 7 years, as shown in Figure AA.16. Step 3: Calculate the Change in Performance To calculate the changes in performance (shaded area in Figure AA.16)
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Attachment A-51 Using the above information, the change in performance resulting from the application of the thin overlay shown in Figure 4.3 can be calculated using Equations A-1 and A-3 as follows: 40exp 31.27exp 120 28.7 22.0 218 . 0.05 18.3 22.0 0.047 18.3 28.7 dt dt in mile yearst t∫ ∫ ( )
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A-52 Quantifying the Effects of Preservation Treatments on Pavement Performance considered) , and a discount rate of 3 percent was assumed.
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Attachment A-53 because a similar form of the deterioration model was used resulting in similar correlation between life extension and increase in performance. AA-3 Implementation of Performance Measures Implementing performance measures into pavement management practices will help consider the contributions of preservation on performance in the agency decision-making processes.
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A-54 Quantifying the Effects of Preservation Treatments on Pavement Performance in performance was also investigated, but no reliable model could be developed. Therefore, the relative difference in performance was used in the decision-making process.
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Attachment A-55 The calculation of the life extension is described later in this section. The remaining service life is calculated as the lowest of the times it takes for each distress to reach a predefined threshold value.
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A-56 Quantifying the Effects of Preservation Treatments on Pavement Performance – The skid resistance model is given by Equation A-10. Maryland SHA data showed that the skid number following a thin overlay application could be taken as 47.
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Attachment A-57 for each pavement segment regarding the type of maintenance category that should be performed (e.g., preventive maintenance, rehabilitation, etc.) , not a specific treatment at the network level.
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A-58 Quantifying the Effects of Preservation Treatments on Pavement Performance of segments fall on the line of maximum improvement, indicating that thin overlays reset the CCI value back to 100. The remainder of the required information was extracted from the report (de Leon Izeppi et al.
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Attachment A-59 effective. In addition, the reported models (de Leon Izeppi et al.
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