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16 100 well-formed, but before cracks become raveled, have devel- oped into multiple cracks, or before the crack width exceeds 80 about three-eighths of an inch. Most effectively, condition Average PCI 60 surveys of pavement surface distresses for the selection and timing of preventive maintenance treatments are annually 40 Runways carried out on candidate pavement sections. The first pave- ment preservation treatments are typically carried out when 20 All other facilities the pavement surface layer is between 3 and 5 years old. The 0 results of the synthesis survey show that the average frequency of PCI surveys on runways was 3.4 years (see Table 2) with 1999 2002 2005 2008 the range of from 1 to 10 years. Survey year FIGURE 8 Illustration of trends in average PCI for PAVEMENT PERFORMANCE PREDICTION runway pavements and all other pavements. For planning purposes, airport pavement maintenance man- Trends in pavement condition. Historical trends in the agers estimate future pavement preservation needs. A typical health of the network provide linkage between pavement planning period is 5 years; however, some large airports may preservation investments and the outcomes. For example, prepare pavement preservation plans for major runways for Figure 8 shows an improvement in the condition of the up to 15 years. Predicting pavement performance and storing runway pavements, but no improvement in the condi- the results in the APMS database assists managers in identi- tion of pavements on other facilities. The PCI results in fying future pavement performance. Figure 8 are based on a 3-year evaluation period. Documentation of system benefits. Systematic analyses of pavement conditions play a vital role in the documen- Use of Pavement Performance Prediction tation of APMS benefits necessary to secure continued Future pavement performance, or pavement deterioration, is financial support for the program. Documentation of funding needs. Condition analysis estimated using pavement performance models. The survey provides basic data for the determination of funding revealed that 66% of responding airport agencies use an APMS needs, as described in chapter five. to predict future pavement deterioration (see Figure 6). Pave- Technical analysis of pavement performance. System- ment performance prediction serves the following: atic pavement condition evaluation can identify: Major causes of pavement deterioration such as poor Estimation of when the pavement will require M&R drainage and inappropriate pavement materials. treatment. The need for performance prediction is illus- Well or poorly performing initial pavement struc- trated in Figure 9, which shows pavement performance tures, or the subsequent M&R treatments. curves for two pavements. Both pavements have the Rates of pavement deterioration for different pavement same present PCI, but pavement B deteriorates, and is types, facilities, and M&R treatments. The deteriora- expected to deteriorate, faster than pavement A. Conse- tion rates are used to develop pavement performance quently, pavement B will require an earlier pavement models discussed in chapter five. preservation treatment. Pavement sections with inadequate structural capacity. Estimation of treatment type. As shown in Figure 9, when pavement condition reaches a minimum accept- able service level it should be rehabilitated. To identify PAVEMENT EVALUATION FOR PREVENTIVE MAINTENANCE 100 Predicted performance Pavement Condition Index (PCI) A B Pavement condition surveys that evaluate the type, severity, and Remaining Service extent of pavement surface distresses are also used in preven- Life tive maintenance programs. However, for selection and appli- cation of preventive maintenance treatments it is also desirable to identify specific pavement conditions and early indicators Minimum acceptable service level that trigger the need for preventive maintenance treatments. Past performance Preventive maintenance treatments are best applied when they are most cost-effective, typically before distresses progress 0 Now Now + 2 Now +5 and more expensive corrective treatments are needed. For Pavement age, years example, treatments to route and seal cracks in asphalt con- crete pavements are carried out when the cracks are already FIGURE 9 Pavement performance prediction.

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17 future funding needs, the type of the M&R treatment, a few sections with known performances and applied to and its cost and timing, need to be estimated. all other sections. Family modeling is a default model- Estimation of the life span of M&R treatments. To select ing approach in MicroPAVER (Shahin 2001). cost-effective treatments, it is necessary to estimate the Extrapolation of existing trends. This approach is a cost of the treatment and its life span. The subsequent variation on family modeling. If the condition of the monitoring of the treatment performance provides feed- pavement was evaluated on only one previous occasion, back on the choices made. the family pattern is extrapolated taking into account the Deterioration rate. The predicted rate of pavement dete- condition observed in the past. If the condition of rioration can also be used as one of the factors to select the pavement was evaluated in the past on more than candidate sections for M&R. In Figure 9, pavement B is one occasion, the extrapolation using a family curve can expected continue to deteriorate at a higher rate than take into account the past observation points using pavement A, and the timing of the M&R treatment for regression analysis. pavement B is now. The extrapolation using one observation point is illus- Estimation of the remaining service life. Figure 9 also trated in Figure 10. The observed PCI value in year 10 is defines the remaining service life of the pavement. When above the family prediction curve. Following the trend known for all sections of the network, the remaining ser- established by the family prediction curve it is expected vice life can be used to characterize the overall condition that the section will reach the minimum recommended of the network. It is also useful in planning and program- PCI level in year 20, compared with year 18 expected for ming pavement M&R activities (Wade at al. 2007a). the pavement family prediction. Timing of preventive maintenance treatments. Pavement Markov probability models. Markov models have been conditions that exist at the time of the pavement evalu- used for pavement performance prediction of highway ation survey may need to be extrapolated to the time pavements. However, it appears that they have not been when an M&R treatment will be applied. In some cases, used for airfield pavements (Tighe and Covalt 2008). the lead time may be 3 or more years. Preventive main- tenance treatments are typically planned only from 2 to Artificial neural networks. Artificial neural networks (ANN), 18 months in advance. or neural networks, are computing procedures or systems that can link a large set of data (e.g., a data set describing the Pavement Performance Modeling Techniques pavement and its exposure to the traffic and environment) to an outcome (e.g., expected life span of the pavement) with- Pavement performance depends on many local factors such out using traditional statistical analysis. However, pavement as the type and frequency of traffic loads, environmental expo- performance models, whether they are developed using ANN sure, subgrade characteristics including drainage, and pave- or conventional modeling techniques, have to be calibrated ment structure. Consequently, pavement performance models to local conditions. Although the calibration process can be are not easily transferable from airport to airport. The selection facilitated using ANN, the calibration of ANN requires spe- of performance models depends on available data, agency cialized computational techniques that are still experimental. requirements for estimating future pavement preservation The applicable technology of ANN is reviewed in Trans- needs, and on the APMS software used. portation Research Circular E-C012: Use of Artificial Neural Networks in Geomechanical and Pavement Systems (1999). Typical methods used for pavement performance model- ing include: 100 Observed PCI Pavement Condition Index (PCI) Expert modeling. Expert modeling can be used when his- 80 Section-specific prediction torical pavement performance data are not available. Per- formance models, such as a relationship between pave- 60 ment condition and pavement age for different pavement Minimum recommended PCI level types (e.g., AC or PCC) and airport pavement facilities (e.g., runways and taxiways) are based on the expert 40 Pavement "family" opinion of pavement professionals (Zimmerman 2000). prediction Modeling using families of performance curves. The 20 concept of "family" modeling is based on the expecta- tion that similar airport pavements exposed to similar 0 traffic will perform in a similar way. For example, all 0 5 10 15 18 20 25 30 pavement sections on runways that have AC overlays Pavement age, years are expected to have the same pattern of pavement dete- FIGURE 10 Pavement performance prediction using a rioration. The deterioration pattern is established using pavement family prediction.