Nonlinear genetic-base models for prediction of fatigue life of modified asphalt mixtures by precipitated calcium carbonate
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Azarhoosh:2020:RMPD,
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author = "A. R. Azarhoosh and Z. Zojaji and
F. {Moghadas Nejad}",
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title = "Nonlinear genetic-base models for prediction of
fatigue life of modified asphalt mixtures by
precipitated calcium carbonate",
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journal = "Road Materials and Pavement Design",
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year = "2020",
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volume = "21",
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number = "3",
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pages = "850--866",
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keywords = "genetic algorithms, genetic programming, fatigue life,
indirect tensile fatigue test, ITF test, precipitated
calcium carbonate, PCC, CaCO3",
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publisher = "Taylor \& Francis",
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DOI = "doi:10.1080/14680629.2018.1513372",
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size = "17 pages",
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abstract = "Fatigue cracking is the most important structural
failure in flexible pavements. The results of a
laboratory study evaluating the fatigue properties of
mixtures containing precipitated calcium carbonate
(PCC) using indirect tensile fatigue (ITF) test were
investigated in this paper. The hot mix asphalt (HMA)
samples were made with four PCC contents (0percent,
5percent, 10percent, and 15percent), and tested at
three different testing temperatures (2degrees Celcius,
10degrees Celcius and 20degrees Celcius) and stress
levels (100, 300, and 500 kPa). Due to the complex
behaviour of asphalt pavement materials under various
loading conditions, pavement structure, and
environmental conditions, accurately predicting the
fatigue life of asphalt pavement is difficult. In this
study, genetic programming (GP) is used to predict the
fatigue life of HMA. Based on the results of the ITF
test, PCC improved the fatigue behaviour of studied
mixes at different temperatures. But, the considerable
negative effect of the increase of the temperature on
the fatigue life of HMA is evident. On the other hand,
the results indicate The GP-based formulas are simple,
straightforward, and particularly valuable for
providing an analysis tool accessible to practicing
engineers.",
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notes = "p865 'The GP models are capable of predicting the
fatigue life of asphalt mixtures with high
accuracy.'
Department of Civil Engineering, Faculty of
Engineering, University of Bojnord, Bojnord, Iran",
- }
Genetic Programming entries for
Alireza R Azarhoosh
Zahra Zojaji
Fereidoon Moghaddas Nejad
Citations