Investigating the Relationship of As-Constructed Asphalt Pavement Air Voids to Pavement Performance (2021)

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13 S U M M A R Y The objective of this study was to determine the effect of in-place air voids (AV) on the performance of asphalt concrete (AC) pavements. The research focused on four primary distress types related to asphalt pavement performance: rutting, fatigue cracking, transverse cracking, and ride. The research team applied three analysis methods, described as Analysis Method 1, 2, and 3, to Long-Term Pavement Performance program (LTPP) data and then validated the results with data from other sources. Although there are other pavement performance characteristics, such as raveling, they are not the focus of this study. Further, this study did not include composite pavements. The terms “in-place air voids” and “as-constructed air voids” are often used interchangeably. In-place air voids can be defined as the asphalt mixture AV as they change over time, which agencies do not typically measure and cannot be controlled. As-constructed air voids is a measured value that an agency can control in their construction specifications. The team applied the as-constructed AV value that best corresponded to the performance characteristic being analyzed. This approach required the input data for each pavement section in the study to have construction records for each AC lift. The research team recognized that as- constructed AV is not the only material parameter that influences pavement performance. In fact, other material properties play a more significant role but are not the objective of this study. They are all consolidated into the pavement structure input values. The primary source of input and output dataset for the study was the LTPP database. The LTPP database contains an extensive amount of detail for each section. The research team selected climate, traffic, and pavement structure input variables and rutting, cracking, and ride performance data as output variables. Time-based input values, such as climate and traffic, are consolidated into annual values. Material input values, specifically the pavement structure layers, are separated into layer thickness and stiffness values. Some assumptions were made regarding the nature of the measured performance distress. The measured rutting was not examined to establish the cause or links to associated pavement material properties or the pavement structure. Fatigue performance is measured as wheel path cracking and was not examined to distinguish between the causes of the cracking, such as bottom-up, top-down, or delamination (commonly caused by poor construction). Similarly, thermal cracking was measured as any transverse crack. For the analysis of rehabilitation sections, both wheel path and transverse cracking are assumed to be reflective cracking. Ride was examined because it is a common performance measure used by highway agencies, but the measured roughness was not examined to establish the cause. The ride performance most likely mirrors other distress in the pavement. Three approaches were used to analyze the LTPP dataset: common subset scatter plots, regression, and artificial neural networks. All three analysis methods examined the new construction and pavement rehabilitation LTPP sections as separate groups. Analysis Method-1 created common subsets of input data based on climate, traffic, and pavement structure, then plotted a performance indicator against as-constructed AV to establish a scatter plot representing the influence of as-constructed AV on pavement performance. Climate input was based on a modified version of the LTPP four climate zones. Traffic input was sorted into two categories based on five-year cumulative equivalent single axle loads (ESALs) from the time the surface was open to traffic. Pavement structure was quantified by the composite structural number (SN) value of the base and asphalt pavement layers as reported in the LTPP database. (Huang, 2004) The rutting performance indicator measured rutting, in inches, after four years of traffic on the asphalt surface. The fatigue cracking performance indicator was percent cracking in the 39-inch wheel path after 10 years of traffic. The thermal

14 cracking performance indicators were the year that transverse cracking initiated and the measured transverse cracking expressed as lineal feet per mile after 3 years from the time cracking initiated. The ride performance indicators were the mean international roughness index, in inches per mile, after 10 years of traffic on the surface and the number of years for the initial ride value to increase 25 in/mi. Analysis Method-2 correlated (regressed) a broad array of climate, traffic, and pavement input values against the recorded LTPP performance over time. The final regression equation for each performance category expresses the most significant variables that influence that specific pavement performance category. As-constructed AV are always included in the regression regardless of its degree of significance. With the input values for a specific pavement, Analysis-2 generates a family of curves showing the incremental influence of as-constructed AV on the selected performance category (y-axis) over time (x- axis). Analysis Method-3 applied an artificial neural network (ANN) analysis process to a broad array of climate, traffic, and pavement input values against the recorded LTPP performance over time. The input and output dataset used to achieve Analysis-2 and Analysis-3 results were the same. The final ANN solution is software driven using the most significant variables that influence pavement performance. As-constructed AV were always included as an input variable regardless of its degree of significance. With input values for a specific pavement, Analysis-3 generates a family of curves showing the incremental influence of as- constructed AV on the selected performance category (y-axis) over time (x-axis). Each analysis method involved some interpretation and generalized assumptions of the data from LTPP to maintain a reasonable size dataset to accomplish the analysis. Too much refinement of the required data diminishes the size of each group of data. Below is a list of interpretations and assumptions made regarding the data. • Rutting is predominantly influenced by the surface layer of asphalt mixture. The research team recognizes that rutting could be related to pavement structure (base/subgrade rutting) but did not examine the rutting data to the extent needed to separate rutting type. • All wheel path cracking is related to fatigue cracking on new construction sections. The research team acknowledges there are other causes of cracking in the wheel path, such as asphalt layer delamination and construction segregation, but did not examine the cracking data to the extent needed to identify the cause of wheel path cracking. • All transverse cracking is related to low-temperature thermal cracking on new construction sections. The research team recognizes there are other causes of transverse cracking but did not examine the cracking data to the extent needed to determine if the cracking was not related to low-temperature thermal cracking. • All cracking on pavement rehabilitation sections is reflective cracking. The research team recognizes that fatigue and thermal cracking could occur on pavement rehabilitation sections but did not examine the pre- and post- pavement condition to determine the cause of the cracking. Due to the limited amount of LTPP data on pre-overlay traffic, only Analysis Method 2 includes this input variable for rehabilitation models. Study Conclusions The conclusions are solely based on the analysis of LTPP sections, which were not constructed for the purpose of examining the influence of as-constructed AV. The research team anticipated that the effect of as-constructed AV would improve performance as the value reduced from 9% to 4% and may have a negative effect at values below 4%. A number of laboratory performance tests require the asphalt mixture specimens to be compacted to 7% AV to standardize the response to the test with the knowledge that the

15 test results would show improvement if the specimens were compacted to 4%. Using this laboratory perspective, the research team applied the following general terms to the results of the analysis. • Meets Expectation: The performance improved as as-constructed AV decreased and performance may decline at air void levels below 4%. • No Influence: The performance was similar across the entire range of AV. • Contradicts Expectation: The performance declined as AV decreased. On a broad basis, lower as-constructed AV do have a positive effect (met expectation) on the performance of an asphalt pavement, but the effect is not consistent between pavement demographics (climate, traffic, and pavement structure), types of pavements (new construction and rehabilitation), and measures of performance (rutting, fatigue cracking, thermal cracking, and ride). Further, the models were predominantly developed with low to moderate traffic, so caution is needed when applying the models to parameters outside the range of the study’s LTPP dataset. The results of Analysis Method 1 were based on a subjective examination of 27 subgroups of LTPP sections. The trends of the scatter plot data were classified as meeting expectation, no influence, or contradicting expectation. Analysis Method 1 concluded that the influence of as-constructed AV was mixed. • For rutting, 62% of the subgroups met expectation and 12% contradicted expectation. • For fatigue cracking, 62% met expectation and 19% contradicted expectation. • For thermal cracking, 46% met expectation and 50% contradicted expectation. • For ride, 40% met expectation and 40% contradicted expectation. The results of Analysis Method 2 were based on separate regression models developed for eight combinations of pavement type (new construction and rehabilitation) and performance measure (rutting, fatigue cracking, thermal cracking, and ride). A set of average input variables and increments of as- constructed AV were entered into the models to create a series of predicted performance curves to assess the influence of AV. Analysis Method 2 concluded that the influence of as-constructed AV was mixed. • For rutting of new construction, the regression model prediction nominally contradicted the expectation. • For rutting of rehabilitation, the regression model prediction minimally contradicted the expectation. • For fatigue of new construction, the regression model prediction significantly met the expectation. • For fatigue of rehabilitation, the regression model prediction nominally met the expectation. • For thermal cracking of new construction, the regression model prediction nominally met the expectation. • For thermal cracking of rehabilitation, the regression model prediction nominally met the expectation. • For ride of new construction, the regression model prediction nominally met the expectation. • For ride of rehabilitation, the regression model prediction nominally met the expectation. The results of Analysis Method 3 were based on separate ANN models developed for eight combinations of pavement type and performance measure. Increments of as-constructed AV were applied with the climate, traffic, and other material inputs for each LTPP section to create a series of predicted performance curves to assess the global influence of as-constructed AV. Analysis Method 3 concluded that the influence of as-constructed AV was mixed. • For rutting of new construction, the ANN model prediction curves nominally met the expectation. • For rutting of rehabilitation, the ANN model prediction showed no practical influence.

16 • For fatigue of new construction, the ANN model prediction only met the expectation at high as- constructed AV. • For fatigue of rehabilitation, the ANN model prediction significantly met the expectation. • For thermal cracking of new construction, the ANN model prediction showed no practical influence. • For thermal cracking of rehabilitation, the ANN model prediction nominally met the expectation. • For ride of new construction, the ANN model prediction nominally met the expectation. • For ride of rehabilitation, the ANN model prediction nominally met the expectation. A validation of the three analysis methods using external datasets also had mixed results. One validation approach found that rutting and fatigue trends agreed (met expectation) with Analysis Method 1 for the climate 2 subgroup but differed for the climate 4 subgroup. The other validation approach concluded that Analysis Method 2 regression models were a fair fit for the LTPP dataset and were applicable to the external validation dataset for several cases. On the other hand, Analysis Method 3 artificial neural network models were a significantly better fit for the LTPP dataset but were not applicable to the external validation dataset. The validation used datasets that were predominantly on the upper end of the LTPP dataset range and could be outside the reasonable application of the models.