Consideration of Preservation in Pavement Design and Analysis Procedures (2015) / Chapter Skim
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Pages 21-35
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21 C H A P T E R 5 This approach considers pavement preservation by calibrating the MEPDG local models. Calibration is a systematic process for eliminating any bias and minimizing the residual errors between observed or measured results from the real world and predicted results from the model (AASHTO 2010)
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22 conditions described in the AASHTO Local Calibration Guide (AASHTO 2010)
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23 practices. The experimental matrix would ideally include key factors, such as design type (i.e., new/reconstructed, rehabilitation)
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24 error. The suggested minimum numbers of sections for analysis of each distress type over the entire experimental/ sampling matrix are as follows (AASHTO 2010)
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25 that determines if there is a significant difference between sets of measured and predicted distress/smoothness and from an analysis of the intercept and slope estimates in the measured versus predicted linear regression model. In the example shown in Figure 6, the trend lines of the three data sets are statistically analyzed to determine if they are significantly biased in relation to the line of equality, which represents perfect prediction accuracy; calibration of the prediction model is required only if the trend line is found to be statistically different.
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26 an intercept of zero. Statistics from the linear regression analysis are examined to test the following null and alternative hypotheses: – H0: bo = 0.
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27 Null Hypothesis Parameter Untreated Sections Preservation ATreated Sections Preservation BTreated Sections Number 81 55 61 Avg. predicted rutting, in.
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28 then using Microsoft Excel Solver to determine the optimal values for all coefficients that give the smallest sum of squared error (SSE) between the predicted and measured distress/smoothness.
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29 Figure 9. Distress model calibration settings -- new rigid pavements.
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30 Manual of Practice (AASHTO 2008) ; these values are listed in the following.
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31 Example of Implementation Process The Michigan Department of Transportation (MDOT) maintains a database covering many years of preservation data for hundreds of pavement sections located throughout the state on roads with different functional classes.
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32 HMA overlay, and reconstruction with conventional HMA pavement. A preservation treatment was later placed on the improved pavement sometime between 1999 and 2007.
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33 Step 8: Eliminate Local Bias of Distress and IRI Prediction Models Because the data presented in Figure 12 and Table 19 indicate high bias and low precision for each set of pavement sections, the data were reviewed to determine if certain factors (e.g., traffic, climate, pavement cross-section, or improvement year) caused these levels of bias and error.
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34 (i.e., predicted total rutting was computed as four times the predicted HMA rutting)
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35 than the reasonable value reported for MEPDG rutting model (0.076 in. and 0.057 in., versus 0.10 in.)
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