Application of Gene Expression Programming to Evaluate Strength Characteristics of Hydrated-Lime-Activated Rice Husk Ash-Treated Expansive Soil
Created by W.Langdon from
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- @Article{journals/acisc/OnyeloweJOOA21,
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author = "Kennedy C. Onyelowe and Fazal E. Jalal and
Michael E. Onyia and Ifeanyichukwu C. Onuoha and
George U. Alaneme",
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title = "Application of Gene Expression Programming to Evaluate
Strength Characteristics of Hydrated-Lime-Activated
Rice Husk Ash-Treated Expansive Soil",
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journal = "Applied Computational Intelligence and Soft
Computing",
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year = "2021",
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pages = "6686347:1--6686347:17",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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bibdate = "2021-09-20",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/acisc/acisc2021.html#OnyeloweJOOA21",
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URL = "https://downloads.hindawi.com/journals/acisc/2021/6686347.pdf",
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DOI = "doi:10.1155/2021/6686347",
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size = "17 pages",
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abstract = "Gene expression programming has been applied to
predict the California bearing ratio (CBR), unconfined
compressive strength (UCS), and resistance value (R
value or Rvalue) of expansive soil treated with an
improved composites of rice husk ash. Pavement
foundations suffer failures due to poor design and
construction, poor materials handling and use, and
management lapses. The evolution of sustainable green
materials and optimisation and soft computing
techniques have been deployed to improve on the
deficiencies being suffered in the abovementioned areas
of design and construction engineering. expansive soil
classified as A-7-6 group soil was treated with
hydrated-lime activated rice husk ash (HARHA) in an
incremental proportion to produce 121 datasets, which
were used to predict the behaviour of the soil strength
parameters using the mutative and evolutionary
algorithms of GEP. The input parameters were HARHA,
liquid limit (), (plastic limit , plasticity index ,
optimum moisture content (), clay activity (AC), and
(maximum dry density (omega-max) while CBR, UCS, and R
value were the output parameters. A multiple linear
regression (MLR) was also conducted on the datasets in
addition to GEP to serve as a check mechanism. At the
end of the computing and iterations, MLR and GEP
optimisation methods proposed three equations
corresponding to the output parameters of the work. The
responses validation on the predicted models shows a
good correlation above 0.9 and a great performance
index. The predicted models performance has shown that
GEP soft computing has predicted models that can be
used in the design of CBR, UCS, and R value for soils
being used as foundation materials and being treated
with admixtures as a binding component.",
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notes = "Department of Civil and Mechanical Engineering,
Kampala International University, Kampala, Uganda",
- }
Genetic Programming entries for
Kennedy C Onyelowe
Fazal E Jalal
Michael E Onyia
Ifeanyichukwu C Onuoha
George U Alaneme
Citations