Details:

Creators:
Kim, Yong-Rak ; Hong, Minki
Kim, Yong-Rak ; Hong, Minki Less -
Corporate Creators:
University of Nebraska--Lincoln. Dept. of Civil Engineering
Subject/TRT Terms:
[+]
Hot Mix Paving Mixtures Linear Regression Analysis Mix Design Neural Networks Pavement Design Quality Assurance Quality Control Rutting
Publication/ Report Number:
MPM-16
Resource Type:
Tech Report
Geographical Coverage:
United States
Corporate Publisher:
Nebraska Transportation Center
NTL Classification:
NTL-HIGHWAY/ROAD TRANSPORTATION-Pavement Management and Performance;NTL-HIGHWAY/ROAD TRANSPORTATION-Materials;
Abstract:
The asphalt pavement analyzer (APA) has been widely used to evaluate hot-mix asphalt (HMA) rutting potential in mix

design and quality control-quality assurance (QC-QA) applications, because the APA testing and its data analyses are

relatively simple, rapid, and easy. However, as demonstrated in many studies and also experienced by the state of

Nebraska, APA testing is in question due to its high testing variability and a lack of sufficient correlation with actual filed

performance. The primary objective of this research was to find critical materials and/or mixture design factors affecting

APA test results so as to eventually improve the current APA testing program in Nebraska. In addition to that,

development of models to predict APA rut performance with given properties of HMA mixture ingredients and mixture

design characteristics were also attempted. To find variables affecting APA rut results and the extent of these variables,

SP-4 mixture data from Nebraska and HMA mixture data from Kentucky were statistically analyzed using the multiple

linear regression method considering six factors (binder PG, aggregate gradation, nominal maximum aggregate size,

aggregate angularity, air voids in mixture, and asphalt content in mixture) as probable candidates for significantly

affecting APA rut results. For a detailed characterization of gradation effects, three indicators (gradation density, fineness

modulus, and restricted zone) were considered, and each of them was used for each statistical analysis. Results from

analyses demonstrated that the binder PG was the only variable that always shows significant impact on APA rut results,

which is in good agreement with other studies. Predicting models developed through the results of multiple linear

regression analysis and the artificial neural network technique presented a relatively low level of model adequacy which

can be observed by the coefficients of determination and cross-plots between predicted APA rut values and the measured

APA rut data. More data would be helpful to confirm the findings from this research and also to develop a better

prediction model.


Format:
PDF
Collection(s):
US Transportation Collection
Main Document Checksum:
[+]
urn:sha256:35d724c0cbcaf3156488b27f943a752ffe96870ae001c152114e4008395e9ef1
Download URL:
https://rosap.ntl.bts.gov/view/dot/25455/dot_25455_DS1.pdf
File Type:

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