Cover Image

Not for Sale



View/Hide Left Panel
Click for next page ( 4


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 3
3 data required for these models include the properties of the day. The temperature is computed using U.S. Weather Bureau binder, some gradation of the aggregates, and the volumetric data that can be accessed readily through websites (5, 6) and composition of the mix. The binder properties may be input Appendix B of this report. The computational model of tem- in any of the three MEPDG levels: perature differs from that used in the MEPDG; it calculates the Level 1: The input for this level will be the six measured temperature more accurately than by using in the Enhanced values of the master curve of the binder (the glassy shear Integrated Climatic Model as part of the MEPDG. It was modulus, the crossover frequency, rheological index, the considered necessary to increase the accuracy of computing defining temperature, and the two time-temperature shift the pavement temperature with depth because of the rele- function coefficients). The properties of extracted binders vance of the thermal stress contribution to the growth of a that were measured in the Strategic Highway Research reflection crack. Program (SHRP) asphalt studies (4) are summarized in Appendix G. Computational Efficiency Level 2: The input will be the PG of the binder and the geo- Computational efficiency of the software was accomplished graphical location of the project. All six binder proper- by calculating the growth of reflection cracks by the three dif- ties that were measured, tabulated, and reported in the ferent mechanisms separately and then combining the num- SHRP asphalt studies for a variety of asphalt binders in ber of days that each required to grow vertically all the way each of the four principal climatic zones (4) will be used. through the overlay. Another contributing factor to effi- The program will use the means of the WLF coefficients, ciency in running time was realized by re-programming all defining temperature and crossover frequency for the of the subprograms from their original language into the principal climatic zone where the project is located and C# language which is used in the MEPDG. Additional effi- then calculate the glassy shear modulus and rheological ciency was achieved by using ANN algorithms to speed up index from the Performance Grade of the binder. the frequent calculations of mixture modulus and stress Level 3: The input will be the geographical location of the intensity factors that are done each day in a simulated pave- project. The mean values of all six master curve proper- ment life. The stress intensity factor computations were ties in the climatic zone where the project is located will particularly useful in cutting down on the program running be used. time instead of using finite element calculations for that The fracture properties of the overlay mixture are derived purpose. The objective of computational efficiency was to from the input properties and the complex moduli neural reduce the overall program run time down to a minimum network algorithms. The design method does not require lab- as a convenience to the user or this program. The actual run oratory tests of the crack growth in a mixture; it allows the time for a 20 to 30 year pavement life simulation is a mat- user to try numerous variations of a mixture to determine ter of seconds and at most a few minutes, depending upon which has the best resistance to reflection cracking. The mate- the computer that is used. rial properties of the layers of the existing pavement may be input either from a Falling Weight Deflectometer measure- Calibration to Field Data ment or can be assumed. The growth of cracks due to both thermal and traffic stresses is predicted using the fracture Calibration to field data recognizes that there are three properties for predicting fatigue cracking (4). degrees of severity in reflection cracking: low, medium, and severe. Some or all levels of severity may be present on a Traffic given pavement section at any time. An S-shaped curve was fit through the field data representing the total length of The daily traffic is input as individual axle loads and is iden- surface cracks at high (H), high plus medium (MH), and tical to what is required by the MEPDG software for the three high plus medium plus low levels of cracking severity (LMH). levels of input. The user can accept standard traffic distributions The difference in the total length between any two curves that are incorporated into the MEPDG or use traffic data taken gives the total length of the different levels of distress. Fig- from W4 Tables. The load imposed by the tire on the pavement ure 2 illustrates the three cumulative severity curves. Three is assumed to result in a rectangular, rather than a circular, uni- sets of S-shaped curves were generated with each pavement form pressure distribution. test section; each curve has a characteristic scale and shape parameter. The scale parameter, , is the number of days Crack Growth and Pavement Temperature required for the crack length to reach 0.368 (1/e) of its max- The crack growth is calculated each day taking into account imum length. The shape parameter, , shows how sharply the temperature calculated to be at the tip of the crack on that the curve is rising when it reaches the number of days given