Using
gels
to
replace
a
certain
amount
of
cement
in
concrete
is
conducive
the
green
industry,
while
testing
compressive
strength
(CS)
geopolymer
requires
substantial
effort
and
expense.
To
solve
above
issue,
hybrid
machine
learning
model
modified
beetle
antennae
search
(MBAS)
algorithm
random
forest
(RF)
was
developed
this
study
CS
concrete,
which
MBAS
employed
adjust
hyperparameters
RF
model.
The
performance
verified
by
relationship
between
10-fold
cross-validation
(10-fold
CV)
root
mean
square
error
(RMSE)
value,
prediction
evaluating
correlation
coefficient
(R)
RMSE
values
comparing
with
other
models.
results
show
that
can
effectively
tune
model;
had
high
R
(training
set
=
0.9162
test
0.9071)
low
7.111
7.4345)
at
same
time,
indicated
accuracy
high;
NaOH
molarity
confirmed
as
most
important
parameter
regarding
importance
score
3.7848,
grade
4/10
mm
least
parameter,
0.5667.
Ain Shams Engineering Journal,
Год журнала:
2024,
Номер
15(9), С. 102925 - 102925
Опубликована: Июль 1, 2024
Explosions
and
other
artificial
seismic
sources
remain
a
major
risk
to
human
survival.
Seismicity
catalogs
often
suffer
from
contamination,
which
hinders
the
differentiation
of
tectonic
non-tectonic
events.
To
address
this
issue,
an
automated
control
system
is
developed
employing
machine
learning
(ML)
techniques
discriminate
between
earthquakes
quarry
blasts
(QBs).
By
using
ML
approaches,
such
as
probabilistic
statistical
techniques,
QBs
can
be
differentiated
natural
earthquakes.
The
proposed
method
utilizes
latitude,
longitude,
magnitude
information
improve
performance.
Evaluation
measures,
including
R2,
F1-score,
MCC
score,
others,
are
employed
assess
algorithm's
effectiveness.
Experimental
results
demonstrate
superiority
suggested
method,
achieving
success
rate
97.21%.
algorithm
has
significant
potential
for
enhancing
hazard
assessment,
supporting
urban
development
planning,
promoting
safer
communities
by
accurately
discriminating
man-made
earthquake
Applied Sciences,
Год журнала:
2023,
Номер
13(14), С. 8286 - 8286
Опубликована: Июль 18, 2023
Seismic
response
assessment
requires
reliable
information
about
subsurface
conditions,
including
soil
shear
wave
velocity
(Vs).
To
properly
assess
seismic
response,
engineers
need
accurate
Vs,
an
essential
parameter
for
evaluating
the
propagation
of
waves.
However,
measuring
Vs
is
generally
challenging
due
to
complex
and
time-consuming
nature
field
laboratory
tests.
This
study
aims
predict
using
machine
learning
(ML)
algorithms
from
cone
penetration
test
(CPT)
data.
The
utilized
four
ML
algorithms,
namely
Random
Forests
(RFs),
Support
Vector
Machine
(SVM),
Decision
Trees
(DT),
eXtreme
Gradient
Boosting
(XGBoost),
Vs.
These
models
were
trained
on
70%
datasets,
while
their
efficiency
generalization
ability
assessed
remaining
30%.
hyperparameters
each
model
fine-tuned
through
Bayesian
optimization
with
k-fold
cross-validation
techniques.
performance
was
evaluated
eight
different
metrics,
root
mean
squared
error
(RMSE),
absolute
(MAE),
percentage
(MAPE),
coefficient
determination
(R2),
index
(PI),
scatter
(SI),
A10−I,
U95.
results
demonstrated
that
RF
consistently
performed
well
across
all
metrics.
It
achieved
high
accuracy
lowest
level
errors,
indicating
superior
precision
in
predicting
SVM
XGBoost
also
exhibited
strong
performance,
slightly
higher
metrics
compared
model.
DT
poorly,
rates
uncertainty
Based
these
results,
we
can
conclude
highly
effective
at
accurately
CPT
data
minimal
input
features.
Journal of Engineering and Applied Science,
Год журнала:
2024,
Номер
71(1)
Опубликована: Фев. 12, 2024
Abstract
The
design
process
for
pile
foundations
necessitates
meticulous
deliberation
of
the
calculation
pertaining
to
bearing
capacity
piles.
primary
objective
this
work
was
investigate
potential
use
Coot
bird
optimization
(
$${\text{CBO}}$$
CBO
)
techniques
in
predicting
load-bearing
concrete-driven
Despite
availability
several
suggested
models,
investigation
estimating
pile-carrying
has
been
somewhat
neglected
research.
This
presents
and
validates
a
unique
approach
that
combines
model
with
Multi-layered
perceptron
$${\text{MLP}}$$
MLP
neural
network
adaptive
neuro-fuzzy
inference
system
$${\text{ANFIS}}$$
ANFIS
).
findings
472
different
driven
static
load
tests
were
put
database.
proposed
framework's
building,
validation,
testing
stages
each
accomplished
utilizing
training
set
(70%),
validation
(15%),
(15%)
dataset,
respectively.
According
findings,
$${{\text{MLP}}}_{{\text{CBO}}}$$
$${{\text{ANFIS}}}_{{\text{CBO}}}$$
both
offer
remarkable
possibilities
accurately
pile-bearing
given
structure.
$${R}^{2}$$
R2
values
during
stage
0.9874,
while
validating
stage,
they
0.9785,
0.987.
After
considering
various
kinds
performance
studies
contrasting
them
existing
literature,
it
concluded
provides
more
appropriate
