Prediction of the Effect of Fly Ash on the Unconfined Compressive Strength of Basalt Fiber Reinforced Clay Using Artificial Neural Networks
Processes,
Journal Year:
2025,
Volume and Issue:
13(1), P. 157 - 157
Published: Jan. 8, 2025
In
this
study,
the
effects
of
fly
ash
(FA)
and
basalt
fiber
(BF)
additives
on
unconfined
compressive
strength
(qu)
kaolin
clay
were
experimentally
investigated,
a
dataset
was
created
based
results.
This
used
in
an
artificial
neural
network
(ANN)
model
to
predict
qu
additive
ratio,
water
content,
curing
time.
For
purpose,
samples
prepared
by
adding
1%
BF
with
length
24
mm
FA
at
ratios
3%,
6%,
9%,
12%,
15%
clay,
followed
addition
25%
30%
water.
Unconfined
tests
performed
before
after
28,
42,
56
days
determine
values.
The
evaluation
obtained
experimental
results
carried
out
creating
ANN
model.
To
validate
prediction
capabilities
ANN,
comparative
analysis
using
various
intelligence
models,
model’s
overall
performance
assessed
5-fold
cross-validation
technique.
evaluations
revealed
that
model,
data
from
studies,
demonstrated
highest
accuracy
close
agreement
According
obtained,
R
value
calculated
as
0.97,
while
RMSE
values
found
0.09,
0.10,
0.06
0.04
for
pre-curing,
28th
day,
42nd
day
56th
respectively.
Language: Английский
Investigation of Screw Pile Behavior in Cohesive Soil Under Uplift and Compressive Forces by Experimental Studies and Numerical Analyses
Arabian Journal for Science and Engineering,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Sept. 15, 2024
Language: Английский
Determination of Basalt Fiber Reinforcement in Kaolin Clay: Experimental and Neural Network-Based Analysis of Liquid Limit, Plastic Limit, and Unconfined Compressive Strength
Processes,
Journal Year:
2025,
Volume and Issue:
13(2), P. 377 - 377
Published: Jan. 30, 2025
The
use
of
basalt
fibers,
which
are
employed
in
various
fields,
such
as
construction,
automotive,
chemical,
and
petrochemical
industries,
the
sports
industry,
energy
engineering,
is
also
increasingly
common
soil
reinforcement
studies,
another
application
area
geotechnical
alongside
their
concrete.
With
this
growing
application,
scientific
studies
on
with
fiber
have
gained
momentum.
This
study
establishes
effects
liquid
limit,
plastic
strength
properties
soils,
relationships
among
unconfined
compressive
soil.
For
purpose,
12
mm
was
used
a
material
kaolin
clay
at
ratios
1.0%,
1.5%,
2.0%,
2.5%,
3.0%.
prepared
samples
were
subjected
to
tests.
As
result
experimental
ratio
that
provided
best
improvement
determined,
established.
results
then
input
data
for
an
artificial
intelligence
model.
neural
network
(NN)
trained
obtain
fiber-to-kaolin
based
strength.
model
enabled
prediction
provides
maximum
without
need
experiments.
NN
great
agreement
results,
demonstrating
providing
can
be
identified
using
requiring
studies.
Moreover,
performance
reliability
evaluated
5-fold
cross-validation
compared
other
AI
methods.
ANN
demonstrated
superior
predictive
accuracy,
achieving
highest
correlation
coefficient
(R
=
0.82),
outperforming
models
terms
both
accuracy
reliability.
Language: Английский
Estimation of Compressive Strength of Basalt Fiber-Reinforced Kaolin Clay Mixture Using Extreme Learning Machine
Materials,
Journal Year:
2025,
Volume and Issue:
18(2), P. 245 - 245
Published: Jan. 8, 2025
Background:
In
this
study,
the
unconfined
compressive
strength
(qu)
of
a
mixture
consisting
clay
reinforced
with
24
mm-long
basalt
fiber
was
estimated
using
extreme
learning
machine
(ELM).
The
aim
study
is
to
estimate
results
closest
data
obtained
through
experimental
studies
without
need
for
studies.
literature
review
reveals
that
ELM
technique
has
not
been
applied
predict
fiber-reinforced
clay,
and
aims
provide
novel
contribution
in
area.
Methods:
included
derived
from
series
mixtures
where
water
contents
20%,
25%,
30%,
35%
were
combined
kaolin
at
reinforcement
rates
0%,
1%,
2%,
3%.
Based
on
these
mixtures,
an
model
developed
qu.
Results:
ELM,
recognized
its
computational
efficiency
high
predictive
accuracy,
demonstrated
exceptional
performance
application,
achieving
R
value
0.9976
RMSE
0.0001.
Furthermore,
includes
figure
representation
illustrating
ELM-based
predictions
align
closely
results,
underscoring
reliability.
Conclusions:
To
further
validate
performance,
compared
other
artificial
intelligence
models
5-fold
cross-validation
approach.
analysis
revealed
outperformed
counterparts,
remarkable
0.000174,
thereby
solidifying
capability
accurately
soil
under
varying
content
conditions.
Thus,
it
aimed
save
labor,
material,
time.
Language: Английский
Performance assessment of a foundation resting on reinforced collapsible Sabkha soil by deep soil mixing columns using machine learning analyses
Mohamed B. D. Elsawy,
No information about this author
Abderrahim Lakhouit,
No information about this author
Turki S. Alhmari
No information about this author
et al.
Alexandria Engineering Journal,
Journal Year:
2025,
Volume and Issue:
118, P. 591 - 605
Published: Jan. 28, 2025
Language: Английский
Effect of multicollinearity in assessing the compaction and strength parameters of lime-treated expansive soil using artificial intelligence techniques
Multiscale and Multidisciplinary Modeling Experiments and Design,
Journal Year:
2024,
Volume and Issue:
8(1)
Published: Nov. 18, 2024
Language: Английский
A sustainable solution for soil improvement: a decision tree model combined with metaheuristic optimizations for fiber reinforced clays
Environment Development and Sustainability,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 18, 2024
Language: Английский
Evaluation of the Changes in the Strength of Clay Reinforced with Basalt Fiber Using Artificial Neural Network Model
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(22), P. 10362 - 10362
Published: Nov. 11, 2024
In
this
research,
the
impact
of
basalt
fiber
reinforcement
on
unconfined
compressive
strength
clay
soils
was
experimentally
analyzed,
and
collected
data
were
utilized
in
an
artificial
neural
network
(ANN)
to
predict
based
ratio
length.
For
purpose,
two
different
lengths
(6
mm
12
mm)
added
unreinforced
bentonite
at
ratios
0%,
1%,
2%,
3%,
4%,
5%,
tests
performed
prepared
reinforced
samples
determine
(qu)
values.
The
evaluation
obtained
experimental
results
carried
out
by
creating
ANN
models.
To
validate
prediction
capabilities
ANN,
a
comparative
analysis
using
linear
regression,
support
vector
machines,
Gaussian
process
regression
Ultimately,
five-fold
cross-validation
technique
employed
objectively
evaluate
overall
performance
model.
evaluations
revealed
that
model
predictions
from
studies
showed
highest
accuracy
close
agreement
with
results.
Language: Английский
The Evaluation of Effect of Jet Grout Columns to the Settlements in Soils with Numerical Methods
Sakarya University Journal of Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 21, 2024
Increasing
population
bring
together
the
need
for
construction
on
weak
soils.
At
this
point,
soil
improvement
methods
gain
importance
in
which
problematic
properties
of
soils
aimed
to
enhance.
Among
these,
jet
grout
columns
are
widely
applied
method
and
have
advantages
such
as
increasing
bearing
capacity,
reducing
settlements
risk
liquefaction.
In
paper,
utilized
reduce
layers
effect
several
parameters
settlement
examined.
The
analyzes,
performed
with
Plaxis
2D
3D.
grouts
modeled
both
single
composite
region
by
changing
length,
diameter
spacing.
To
assess
soft
clay
effect,
were
socketed
into
different
layers.
results
proved
that
reduces
settlements,
but
change
diameter,
length
spacing
affects
at
rates.
Through
3D
analysis,
up
22%
reductions
obtained
case
where
longest
assigned
lowest
spacings.
most
effective
factor
was
found
rather
than
diameter.
increase
after
a
certain
value
led
lower
performance
due
group
effect.
region,
analyzes
converge
very
much,
so
it
would
be
practical
perform
analysis
composites.
However,
ones
modelled
predicted
more
remained
safe
side.
Additionally,
better
Language: Английский