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From page 155... ... 155 validation data: the first dataset using local pavements that fit within the range of the model's input dataset and the second dataset using pavements that are beyond the model's range. The first dataset was key to validating the study's models. Read the entire page →
From page 156... ... 156 created charts to match the agency input data to the chart subset input data ranges. Analysis Methods 2 and 3 will generate a family of performance curves using a range of as-constructed AV for each of the four performance characteristics. Read the entire page →
From page 157... ... 157 CLIMATE ZONE 3 Wet-Freeze > 37 in. annual precip. Read the entire page →
From page 158... ... 158 Analysis Method 2 Regression Models This section covers the performance models for rutting, fatigue cracking, thermal cracking, and ride, for both new and rehabilitated pavements, developed through regression analysis as part of Analysis Method 2. The first part describes the input variables of the performance models and presents the estimated model parameters. Read the entire page →
From page 159... ... 159 Variable Description Units 𝐹𝐼 annual freezing index at year 𝑦𝑟 °F-days 𝑇𝑅𝐹 annual total traffic at year 𝑦𝑟 kESALs 𝐶𝑢𝑚𝐸𝑆𝐴𝐿 cumulative traffic at year 𝑦𝑟 (since opening to traffic date for new pavements, or since assignment date for rehabilitated pavements) computed as 𝐶𝑢𝑚𝐸𝑆𝐴𝐿 = 𝑃𝑟𝑒𝑇𝑅𝐹 + ∑ (𝑇𝑅𝐹 ) Read the entire page →
From page 160... ... 160 Variable Rutting Fatigue Cracking Thermal Cracking Ride New Rehab. New Rehab. Read the entire page →
From page 161... ... 161 Rutting Performance Model for New Pavements Figure 3 shows the predicted rutting for new pavements between 1 and 15 years of surface age for four as-constructed surface AV values: 3.0%, 4.5%, 6.0%, and 7.5%. The initial traffic value was set to 0 and the annual traffic value was set to 250 kESALs. Read the entire page →
From page 162... ... 162 Figure 4. Example Influence of as-constructed air voids on predicted rutting for rehabilitated pavements Fatigue Cracking Performance Model The definition for fatigue cracking is given in Table 1 and the fatigue performance regression model is shown as Equation 2. Read the entire page →
From page 163... ... 163 Fatigue Cracking Performance Model for New Pavements Figure 5 shows the predicted wheelpath cracking expressed as percent of lane width for new pavements between 1 and 15 years of surface age for four as-constructed surface AV values: 3.0%, 4.5%, 6.0%, and 7.5%. The initial traffic value was set to 0 and the annual traffic value was set to 250 kESALs. Read the entire page →
From page 164... ... 164 Figure 6. Example Influence of As-Constructed Air Voids on Predicted Reflected Fatigue Cracking for Rehabilitated Pavements Thermal Cracking Performance Model The average probable time to crack initiation is 10-13 years combining all LTPP sections used in the study. Read the entire page →
From page 165... ... 165 Thermal Cracking Performance Model for New Pavements Figure 7 shows the predicted thermal cracking for new pavements between 1 and 10 years after transverse crack initiation for four as-constructed surface AV values: 3.0%, 4.5%, 6.0%, and 7.5%. The initial traffic value was set to 0 and the annual traffic value was set to 250 kESALs. Read the entire page →
From page 166... ... 166 Figure 8. Example Influence of As-Constructed Air Voids on Predicted Reflected Thermal Cracking After Crack Initiation for Rehabilitated Pavements Ride Performance Model The ride performance model is shown as Equation 4. Read the entire page →
From page 167... ... 167 ksi; Mr_BSB = 21.90 ksi; Mr_SG = 9.55 ksi; Gr_4 = 55.00%; Gr_200 = 6.20%; BC = 4.99%; Prec = 35.46 in and Temp = 58.10°F. Figure 9. Read the entire page →
From page 168... ... 168 Figure 10. Example of Influence of As-Constructed Air Voids on Predicted Ride for Rehabilitated Pavements Using Analysis Method 3 ANN Models Analysis Method 3 covers the performance models for rutting, fatigue cracking, thermal cracking, and ride for both new and rehabilitated pavements developed through the artificial neural network (ANN) Read the entire page →
From page 169... ... 169 Table 8. Input Variables for ANN Pavement Performance Models Abbreviation Description Units t pavement age year T average annual temperature °F P average annual precipitation inch FI average annual freezing index °F-days TR average annual traffic volume kESAL hac total asphalt layer thickness inch hOL asphalt overlay thickness inch Pbw asphalt content of asphalt layer, weighted average %mix wt Pbt asphalt content of top asphalt lift %mix wt AVw as-constructed air voids of asphalt layer, weighted average %Gmm AVt as-constructed air voids of top asphalt lift %Gmm PNo. Read the entire page →
From page 170... ... 170 Table 10. ANN Model Inputs for LTPP Section 01-0105 Input Variable Value Input Variable Value t 2, 4, 6, 8, 10 AVt 3, 4, 5, 6, 7, 8, 9 T 63.7 PNo.4 56 P 52.4 PNo.200 6.5 FI 59.4 E130°F, 10Hz 52 TR 330 hbase 8.1 hac 4.1 Ebase 32 Pbt 5.2 Esg 12 Figure 11. Read the entire page →
From page 171... ... 171 Figure 12. Influence of As-Constructed Air Voids on Rut Depth for LTPP Section 09-0960 Fatigue Performance Model for New Construction The LTPP Section 05-0115 was selected as an example of a new pavement that illustrates how to use the ANN model to determine the influence of as-constructed AV on fatigue performance of an asphalt pavement. Read the entire page →
From page 172... ... 172 Figure 13. Influence of As-Constructed Air Voids on Fatigue Cracking for LTPP Section 05-0115 Fatigue Performance Model for Rehabilitation The LTPP Section 09-0902 was selected as an example of a rehabilitated pavement that illustrates how to use the ANN model to determine the influence of as-constructed AV on reflected fatigue performance of an asphalt pavement. Read the entire page →
From page 173... ... 173 Figure 14. Influence of As-Constructed Air Voids on Reflected Fatigue Cracking for LTPP Section 09-0902 Thermal Cracking Performance Model for New Construction The LTPP Section 31-0122 was selected as an example of a new pavement that illustrates how to use the ANN model to determine the influence of as-constructed AV on thermal cracking performance of an asphalt pavement. Read the entire page →
From page 174... ... 174 Figure 15. Influence of As-Constructed Air Voids on Thermal Cracking for LTPP Section 31-0122 Reflected Thermal Cracking Performance Model for Rehabilitation The LTPP Section 53-6056 was selected as an example of a rehabilitated pavement that illustrates how to use the ANN model to determine the influence of as-constructed AV on reflected thermal cracking performance of an asphalt pavement. Read the entire page →
From page 175... ... 175 Figure 16. Influence of As-Constructed Air Voids on Reflected Thermal Cracking for LTPP Section 53-6056 Ride Performance Model for New Construction The LTPP Section 31-0122 was selected as an example of a new pavement that illustrates how to use the ANN model to determine the influence of as-constructed AV on ride performance of an asphalt pavement. Read the entire page →
From page 176... ... 176 Figure 17. Influence of As-Constructed Air Voids on Ride for LTPP Section 31-0122 Ride Performance Model for Rehabilitation The LTPP Section 53-6020 was selected as an example of a rehabilitated pavement that illustrates how to use the ANN model to determine the influence of as-constructed AV on ride performance of an asphalt pavement. Read the entire page →
From page 177... ... 177 Figure 18. Influence of As-Constructed Air Voids on Ride for LTPP Section 53-6020 Methods Summary Chart The research team used three methods to analyze over 400 LTPP sections to determine the influence of as-constructed AV on pavement performance. Read the entire page →

From page 155...

... 155 validation data: the first dataset using local pavements that fit within the range of the model's input dataset and the second dataset using pavements that are beyond the model's range. The first dataset was key to validating the study's models.