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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Construction and Bui...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Construction and Building Materials
Article . 2007 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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Cold in-place recycling pavement rutting prediction model using grey modeling method

Authors: Jia-Chong Du; Stephen A. Cross;

Cold in-place recycling pavement rutting prediction model using grey modeling method

Abstract

Abstract The rut depth prediction model (RDPM), based on grey modeling method, can be an efficient and accurate model to predict rut depth. The concept of grey modeling method is introduced and the model prediction equation is derived. The RDPM was developed using rut depth data from wet and dry test conditions by asphalt pavement analyzer (APA). All cold in-place recycling (CIR) test samples consisted of reclaimed asphalt pavement (RAP) mixed with asphalt emulsion and additives were fabricated using a Superpave gyratory compactor. The parameters of RDPM are determined from two of the compacted samples and the model is verified by one of the samples of the same asphalt emulsion contents and test condition. The regression analysis shows that the RDPM is useful for making rut depth predictions, regardless of dry or wet test conditions. Thus, the RDPM can be used to estimate anticipated rut depths if the loading cycles are known.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Top 10%
Top 10%
Average
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