Predictive models of volumetric stability (durability) and erodibility of lateritic soil treated with different nanotextured bio-ashes with application of loss of strength on immersion; GP, ANN and EPR performance study
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- @Article{ONYELOWE:2021:CM,
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author = "Kennedy C. Onyelowe and Ahmed M. Ebid and
Light I. Nwobia",
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title = "Predictive models of volumetric stability (durability)
and erodibility of lateritic soil treated with
different nanotextured bio-ashes with application of
loss of strength on immersion; {GP}, {ANN} and {EPR}
performance study",
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journal = "Cleaner Materials",
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volume = "1",
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pages = "100006",
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year = "2021",
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ISSN = "2772-3976",
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DOI = "doi:10.1016/j.clema.2021.100006",
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URL = "https://www.sciencedirect.com/science/article/pii/S277239762100006X",
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keywords = "genetic algorithms, genetic programming, Cleaner &
green materials, Genetic programming (GP), Artificial
neural network (ANN), Evolutionary polynomial
regression (EPR), Nanotextured agro-waste ashes,
Erodibility, Volumetric stability, Treated soil,
Predictive models performance (PMP)",
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abstract = "Volumetric stability and erodibility are important
soil properties influenced by moisture through
raindrops and eventual runoff and the rise in water
tables during wet seasons. Compacted subgrade materials
made of clay respond to water ingress through swelling
and shrinking in turn during drying and this poses a
problem for foundation structures. Supplementary
cementitious materials have been used to treat soils,
in a cleaner procedure to improve the mechanical
properties and to overcome undesirable behavior during
changes in seasons. However, design and construction of
foundation structures exposed to these problems become
necessary and common, which requires constant visits to
the laboratory and equipment needs. In order to
overcome this, machine learning-based predictive models
have been proposed in this work for the estimation of
durability (Sv) via loss of strength on immersion
technique and erodibility (Er) of agro-based ashes.
Genetic programming (GP) (six levels of complexity),
artificial neural network (ANN) (sigmoid activation
function), evolutionary polynomial regression (EPR) (GA
optimized PLR method) techniques have been used to
conduct this intelligent prediction exercise. The
performance of the models was conducted using the sum
of squared errors (SSE) and coefficient of
determination (R2) indices. The results show that EPR's
Er and Sv prediction with SSE of 5.1percent and
2.7percent respectively and R2 of 97.2percent and
92.9percent respectively outclassed GP and ANN.
However, both GP and ANN showed minimal error and
acceptable R2 above 0.85, which showed their ability to
predict with good performance accuracy",
- }
Genetic Programming entries for
Kennedy C Onyelowe
Ahmed M Ebid
Light Ihenna Nwobia
Citations