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Effect of mixture component characte...

Chun, Sanghyun.

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Effect of mixture component characteristics on property and performance of Superpave mixtures.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Effect of mixture component characteristics on property and performance of Superpave mixtures./
作者:	Chun, Sanghyun.
面頁冊數:	125 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
標題:	Engineering, Civil. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3569663
ISBN:	9781303070549

Effect of mixture component characteristics on property and performance of Superpave mixtures.
Chun, Sanghyun.

Effect of mixture component characteristics on property and performance of Superpave mixtures. - 125 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--University of Florida, 2012.

This study was conducted to evaluate the effect of mixture component characteristics (i.e. DASR and IC) on properties and performance of Superpave mixtures, specializing in the development of a set of implementable gradation and volumetric criteria, and Hot-Mix-Asphalt (HMA) mixture property predictive relationships based on mixture component characterization.

ISBN: 9781303070549Subjects--Topical Terms:

783781
Engineering, Civil.

Effect of mixture component characteristics on property and performance of Superpave mixtures.
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100 1 $a Chun, Sanghyun. $3 2094176
245 1 0 $a Effect of mixture component characteristics on property and performance of Superpave mixtures.
300 $a 125 p.
500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
500 $a Adviser: Reynaldo Roque.
502 $a Thesis (Ph.D.)--University of Florida, 2012.
520 $a This study was conducted to evaluate the effect of mixture component characteristics (i.e. DASR and IC) on properties and performance of Superpave mixtures, specializing in the development of a set of implementable gradation and volumetric criteria, and Hot-Mix-Asphalt (HMA) mixture property predictive relationships based on mixture component characterization.
520 $a Four DASR-IC model parameters including DASR porosity, DF, EFT, and CFA/FFA have formed the DASR-IC criteria to effectively address the two primary components (i.e. DASR and IC) of asphalt mixtures that play a major role on properties and performance. Field performance evaluation of different Superpave mixtures was conducted to identify the relationships between the four DASR-IC parameters and field performance. Based on results analyzed, it was found that the introduction of DASR-IC criteria as performance-related design parameters to current mix design guidelines and specifications will lead to better and more consistent field rutting and cracking performance of Superpave mixtures.
520 $a In addition, the DASR-IC criteria will also provide a more rational method to consider the effect of DASR and IC on mixture behavior which strongly affects HMA fracture properties. Therefore, it is expected that this criteria will have a potential to identify the effect of mixture gradation and volumetric characteristics on mixture fracture properties which is more reliably related to performance of asphalt mixtures. Relationships able to predict initial fracture energy and creep rate, which are the properties known to govern the change in material property over time and are also required for performance model predictions, were developed in this study.
520 $a Furthermore, conceptual relationships were identified to describe changes in these properties over time (aging) by including the effect of the non-healable permanent damage related to load and moisture. This can serve as the foundation for further development of improved models to predict mixture properties as a function of age in the field based on additional field data and laboratory studies using more advanced laboratory conditioning procedures. The verified relationships will also serve to provide reliable inputs for prediction of service life using pavement performance prediction models.
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710 2 $a University of Florida. $3 718949
773 0 $t Dissertation Abstracts International $g 74-09B(E).
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791 $a Ph.D.
792 $a 2012
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3569663