May February November July January It calculates pavement responses stresses, strains, and deflections based on traffic, climate, and materials parameters to predict the progression of key pavement distresses and smoothness loss over time for asphalt concrete AC and portland cement concrete PCC pavements. These outputs are the basis for checking the adequacy of a trial design.
This presents challenges for state agencies, as local calibration is a cumbersome and intensive process and the software and embedded distress models are evolving faster than local calibrations can be completed. Researchers found that calibration was typically attempted by looking at the predicted measured distress for a set of roadway segments and reducing the error between measured and predicted values by optimizing the local calibration coefficients.
However, other approaches were taken. Although a minimum number of roadway segments necessary to conduct the local calibration for each distress model is provided in the AASHTO calibration guide, the step for estimating sample size for assessing the distress models was not always reported. For those efforts that did report a sample size, some were smaller than the minimum amount recommended.
The AASHTO calibration guide recommends conducting statistical analyses to determine goodness of fit, spread of the data, and the presence of bias in the model predictions.
Three hypothesis tests are recommended: 1 to assess the slope, 2 to assess the intercept of the measured versus predicted plot, and 3 a paired t-test to determine if the measured and predictions populations are statistically different. Although some local calibration efforts included all three hypothesis tests, many only evaluated some of the statistical tests and others relied only on qualitative comparisons of measured versus predicted distresses.
As such, it is difficult to assess the quality of the predictions or effectiveness of local calibration for each model. For asphalt pavements, the rutting model was the most commonly calibrated model. Transverse and longitudinal cracking models were calibrated the least, with the longitudinal cracking model having the poorest precision. Given the significant spread reported for the default longitudinal cracking model, it should not be used for design.
The tool is capable of analyzing raw deflection data files obtained from Falling Weight Deflectometer FWD testing devices, backcalculating in-place elastic layer moduli for flexible and rigid pavements and generating inputs for performing rehabilitation design using Pavement ME.
It can also be used to perform loss of support analysis and load transfer efficiency LTE calculations. Pavement ME Design includes several tools to assist the pavement engineer:. Skip to content. Pavement ME Design includes several tools to assist the pavement engineer: DRIP performs hydraulic design computations for the subsurface drainage analysis of pavements; XML Validator checks for most of the errors present in the xml files. It can also check for recommended ranges for data values; MapME creates ME Design project files DGPX seeded with geospatially-referenced information relevant to the analysis and design of your pavement; File and analysis APIs Tools include JULEA and ICM; Calibration Assistance helps with local calibration efforts so the user can determine whether there is any bias in the predictions; establish the cause of any bias if found through the calibration process; and optimize the calibration coefficient of the transfer function s for each distress to eliminate bias and minimize the standard error of the estimate; rePave Scoping Tool provides guidance for deciding where and under what conditions to use existing pavement as part of roadway renewal projects; and Backcalculation Tool generates backcalculation inputs from Falling Weight Deflectometer FWD files to the AASHTO Pavement ME Design software for rehabilitation design.
The tool provides three major functions: pre-processing deflection data including project segmentation , backcalculation, and post-processing of results to generate inputs for Pavement ME rehabilitation design.
0コメント