Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results
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- @Article{RANASINGHE:2019:JRMGE,
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author = "R. A. T. M. Ranasinghe and M. B. Jaksa and
F. {Pooya Nejad} and Y. L. Kuo",
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title = "Genetic programming for predictions of effectiveness
of rolling dynamic compaction with dynamic cone
penetrometer test results",
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journal = "Journal of Rock Mechanics and Geotechnical
Engineering",
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volume = "11",
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number = "4",
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pages = "815--823",
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year = "2019",
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ISSN = "1674-7755",
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DOI = "doi:10.1016/j.jrmge.2018.10.007",
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URL = "http://www.sciencedirect.com/science/article/pii/S1674775518302671",
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keywords = "genetic algorithms, genetic programming, Ground
improvement, Rolling dynamic compaction (RDC), Linear
genetic programming (LGP), Dynamic cone penetrometer
(DCP) test",
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abstract = "Rolling dynamic compaction (RDC), which employs
non-circular module towed behind a tractor, is an
innovative soil compaction method that has proven to be
successful in many ground improvement applications. RDC
involves repeatedly delivering high-energy impact blows
onto the ground surface, which improves soil density
and thus soil strength and stiffness. However, there
exists a lack of methods to predict the effectiveness
of RDC in different ground conditions, which has become
a major obstacle to its adoption. For this, in this
context, a prediction model is developed based on
linear genetic programming (LGP), which is one of the
common approaches in application of artificial
intelligence for nonlinear forecasting. The model is
based on in situ density-related data in terms of
dynamic cone penetrometer (DCP) results obtained from
several projects that have employed the 4-sided, 8-t
impact roller (BH-1300). It is shown that the model is
accurate and reliable over a range of soil types.
Furthermore, a series of parametric studies confirms
its robustness in generalizing data. In addition, the
results of the comparative study indicate that the
optimal LGP model has a better predictive performance
than the existing artificial neural network (ANN) model
developed earlier by the authors",
- }
Genetic Programming entries for
Ranasinghe Arachchilage Tharanga Madhushani Ranasinghe
Mark B Jaksa
Fereydoon Pooya Nejad
Y L Kuo
Citations