Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance (2021) / Chapter Skim
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38 Analysis Dataset Count Type New Pavements Rehab. Pavements Total Perf.
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39 Annual average temperature Temp T Freezing index FI FI Pavement performance, time AGE t Pavement performance, rutting RUT Pavement performance, fatigue cracking FTC Pavement performance, thermal cracking TRC Pavement performance, roughness or ride IRI Analysis Method 1: Common Data Subgroups Analysis Method 1 subdivided the study LTPP dataset into smaller subgroups of LTPP sections with common characteristics such that each group could be examined for the influence of as-constructed AV under the premise that pavements in that group would perform in a similar manner and as-constructed AV would be a distinguishing factor between sections. This analysis involved a number of steps to process the LTPP dataset, assemble the common subgroups of data, and examine the influence of as-constructed AV within each subgroup.
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40 Establish a Single Value Performance Criteria for Each Performance Type Once the performance curves were generated, the second step was to determine one or more values related to the performance curve that would distinguish between the levels of performance between LTPP sections. For rutting performance, the primary interest was rutting related to the stiffness of the surface asphalt layer.
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41 percent, but the team elected to narrow the center distribution to 53% to have more sections in the upper and lower subgroups. Figure 2-13.
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42 Figure 2-15. Distribution of LTPP section by average annual precipitation.
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43 Figure 2-17. Distribution of LTPP sections by pavement structure for new and rehab pavements.
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44 Assemble Subgroup Datasets Using the criteria developed in the previous steps, a master dataset was created in a spreadsheet that was sorted into separate sets for new construction and rehabilitation. Each row of the spreadsheet represented one LTPP section and included: (1)
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45 Performance meeting expectation No influence of air voids Performance contradicting expectation Figure 2-18. Examples of influence of as-constructed air voids on rutting performance.
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46 Performance meeting expectation No influence of air voids Performance contradicting expectation Figure 2-19. Examples of the influence of as-constructed air voids on fatigue performance.
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47 Figure 2-20. Years of performance monitoring for LTPP sections with no transverse cracking.
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48 were deemed as having insufficient data. The analysis of transverse (thermal)
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49 Performance meeting expectation No influence of air voids Performance contradicting expectation Figure 2-23. Examples of the influence of as-constructed air voids on ride performance.
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50 Table 2-6. Comparing ride and wheel path cracking trends.
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51 Table 2-8. Summary of as-constructed air voids impact on fatigue cracking performance.
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52 Table 2-10. Summary of as-constructed air voids impact on ride performance.
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53 BCm mean asphalt binder content %mix wt Ageyr age of the pavement for year yr years Precyr total annual precipitation at year yr inches Tempyr annual mean temperature at year yr ยฐF FIyr annual freezing index at year yr ยฐF-days TRFyr annual total traffic at year yr kESALs CumESALyr cumulative traffic at year yr (since opening to traffic date for new pavements, or since assignment date for rehabilitated pavements) computed as ๐ถ๐ข๐๐ธ๐๐ด๐ฟ = ๐๐๐๐๐
๐น + โ (๐๐
๐น )
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54 in the data on the estimated model parameters without the loss of information resulting from removing data with outliers. As an example of the outliers present in the data, Figure 2-24 shows the boxplot of scaled variables for some of the variables considered for the ride models.
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55 consisted of removing any variables that were not statistically significantโi.e., with a p-value lower than 0.10.
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56 The rutting performance data plot in Figure 2-25 provides an overview of general characteristics and trends in the input data subsets. However, the observed effects of AV_s grouping in the plot may be misleading due to confounding factors.
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57 Table 2-12. Summary statistics of data used for estimating rutting model for new pavements.
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58 Table 2-13. Estimated parameters of rutting model for new pavements.
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59 Summary Statistics of Rutting Dataset for Rehabilitated Pavements. Table 2-14 shows the summary statistics of the variables considered for developing the rutting performance models for rehabilitated pavements.
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60 had a statistically significant effect on the value of rutting, which may be a reflection of the influence of the stronger AC pavement and climate independent variables. The dominance of negative signs on these six interactions implies that the influence of as-constructed AV on performance contradicts expectation.
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61 Figure 2-27. Comparison of rehabilitated pavement rutting performance between LTPP measurements and regression model predictions.
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62 Lcrklwp = total length of left wheel path with detected cracking; and Lcrkrwp = total length of right wheel path with detected cracking. The plot in Figure 2-28 shows the median scaled cracking percent (CP*
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63 %Gmmm = as-constructed air voids, weighted average; Xj,yr = model variable j for year ๐ฆ๐; and ฮฒj = model parameter on variable j. Summary Statistics of Fatigue Cracking Dataset for New Pavements.
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64 contradicted performance expectations. Further, AV was significant as an interaction with the thickness of the base, stiffness of the subgrade, and AC layer P200 gradation, which may be a reflection of the influence of these pavement material properties as independent variables.
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65 Figure 2-29. Comparison of new pavement fatigue cracking performance between LTPP measurements and regression model predictions.
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66 Variable Units Mean Std Dev Min 10%ile 50%ile 90%ile Max ESAL kESAL 403.65 372.82 12.83 103.42 281.96 840.00 3,480.00 CumESAL kESAL 2,869.20 2,773.16 15.23 429.52 1,963.13 6,340.44 22,366.37 Estimation of Fatigue Cracking Model for Rehabilitated Pavements. Table 2-19 shows the estimated parameters of the final fatigue cracking performance model for rehabilitated pavements.
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67 Variable Value Std. Error t value Pr(>|t|)
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68 package (Therneau and Grambsch, 2000)
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69 Figure 2-32. Median thermal cracking after crack initiation versus age grouped by surface air voids quintiles.
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70 Table 2-20. Summary statistics of data used for estimating thermal cracking after crack initiation model for new pavements.
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71 Table 2-21. Estimated parameters of thermal cracking model for new pavements.
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72 Table 2-22. Summary statistics of data used for estimating thermal cracking model for rehabilitated pavements.
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73 Table 2-23. Estimated parameters of thermal cracking model for rehabilitated pavements.
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74 later years of four of the five curves. Taking this into consideration, only observations with Ageโค20 years were used for developing the ride performance models.
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75 Table 2-24. Summary statistics of data used for estimating ride model for new pavements.
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76 Table 2-25. Estimated parameters of ride model for new pavements.
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77 Summary Statistics of Ride Dataset for Rehabilitated Pavements. Table 2-26 shows the summary statistics of the variables considered for developing the ride performance model for rehabilitated pavements.
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78 slowerโkeeping everything else fixed. However, further analysis of the sensitivity of this variable and interactions based on the small parameter values and t values show that although they are statistically significant, they are most likely not significant in practical engineering terms.
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79 Figure 2-37. Comparison of rehabilitated pavement ride performance between LTPP measurements and regression model predictions.
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80 analysis because they required some different model inputs. Note that each pavement section contained multiple data points (observations)
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81 Figure 2-38. Illustration of four-layered neural network architecture.
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82 Table 2-30. Input variables required for each ANN performance model.
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83 Figures 2-39 through 2-42 show the comparisons between the measured pavement performance in the LTPP dataset and the predicted results from the ANN models for training and validation. In general, the ANN model predictions are in good agreement with the measured pavement performance, indicating that the ANN models reasonably predict the various pavement performance after the training and validation process.
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84 a. New Construction Fatigue Cracking Model b.
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85 b. Rehabilitation Transverse Cracking Model Figure 2-41.
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86 In this study, 10% of the pavement sections were randomly selected for the test set and the data of these pavement sections were not used for the development of ANN models. Figures 2-43 through 2-46 demonstrate the pavement performance prediction accuracy of the developed ANN models using the test data over the first ten years of measured performance.
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87 a. New Construction Fatigue Cracking Model b.
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88 a. New Construction Transverse Cracking Model b.
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89 b. Rehabilitation Ride Model Figure 2-46.
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90 a. New Construction Rutting Model b.
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91 constructed AV the years of performance were 10 years, so the higher AV reduced reflected fatigue cracking life by 60%. The 10-year performance window for fatigue cracking could be considered too short, but the predicted performance trends are developed sufficiently to satisfy the study objective.
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92 at 3% as-constructed AV would experience 300 ft/mi at 6.5 years. Using these values, decreasing asconstructed AV from 9% to 3% improved transverse cracking performance life by 45%.
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93 Ride performance can also be stated as the duration of time to achieve the same level of roughness. However, the average quality of ride predicted for both new construction and rehabilitated pavements at all as-constructed AV are below an IRI value of 90 and are considered good performing pavements.
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94 a. New Construction Ride Model b.
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