Shear Wave Velocity Prediction with Hyperparameter Optimization
Information,
Год журнала:
2025,
Номер
16(1), С. 60 - 60
Опубликована: Янв. 16, 2025
Shear
wave
velocity
(Vs)
is
an
important
soil
parameter
to
be
known
for
earthquake-resistant
structural
design
and
determining
the
dynamic
properties
of
soils
such
as
modulus
elasticity
shear
modulus.
Different
Vs
measurement
methods
are
available.
However,
these
methods,
which
costly
labor
intensive,
have
led
search
new
Vs.
This
study
aims
predict
(Vs
(m/s))
using
depth
(m),
cone
resistance
(qc)
(MPa),
sleeve
friction
(fs)
(kPa),
pore
water
pressure
(u2)
N,
unit
weight
(kN/m3).
Since
varies
with
depth,
regression
studies
were
performed
at
depths
up
30
m
in
this
study.
The
dataset
used
open-source
dataset,
data
from
Taipei
Basin.
was
extracted,
a
494-line
created.
In
study,
HyperNetExplorer
2024V1,
prediction
based
on
shell
(fs),
(kN/m3)
values
could
satisfactory
results
(R2
=
0.78,
MSE
596.43).
Satisfactory
obtained
Explainable
Artificial
Intelligence
(XAI)
models
also
used.
Язык: Английский
Predicting mechanical properties of CFRP composites using data-driven models with comparative analysis
PLoS ONE,
Год журнала:
2025,
Номер
20(4), С. e0319787 - e0319787
Опубликована: Апрель 7, 2025
Carbon
fiber
reinforced
polymer
(CFRP)
composites
are
increasingly
utilized
for
their
lightweight
and
superior
mechanical
properties.
This
study
uses
machine
learning
models
to
predict
the
properties
of
CFRP
based
on
volume
fraction
carbon
nanotubes
(CNTs),
interlayer
fraction,
glass
transition
temperature,
manufacturing
pressure.
Sixty-two
samples
covering
nine
different
types
CFRPs
were
designed,
manufactured,
experimentally
tested.
Three
models,
namely
ridge
regression,
random
forest,
support
vector
trained
data
compared.
The
results
demonstrated
a
high
prediction
accuracy
flexural
strength
(R
2
=
0.966),
modulus
0.871),
mode-II
energy
release
rate
0.903).
highlights
effectiveness
data-driven
in
predicting
key
composites,
potentially
reducing
need
extensive
experimental
testing
facilitating
more
efficient
material
design.
Язык: Английский
Fatigue Predictive Modeling of Composite Materials for Wind Turbine Blades Using Explainable Gradient Boosting Models
Coatings,
Год журнала:
2025,
Номер
15(3), С. 325 - 325
Опубликована: Март 11, 2025
Wind
turbine
blades
are
subjected
to
cyclic
loading
conditions
throughout
their
operational
lifetime,
making
fatigue
a
critical
factor
in
design.
Accurate
prediction
of
the
performance
wind
is
important
for
optimizing
design
and
extending
lifespan
energy
systems.
This
study
aims
develop
predictive
models
laminated
composite
life
based
on
experimental
results
published
by
Montana
State
University,
Bozeman,
Composite
Material
Technologies
Research
Group.
The
have
been
trained
dataset
consisting
855
data
points.
Each
point
consists
stacking
sequence,
fiber
volume
fraction,
stress
amplitude,
frequency,
laminate
thickness,
number
cycles
test
carried
out
specimen.
output
feature
cycles,
which
indicates
Random
forest
(RF),
extreme
gradient
boosting
(XGBoost),
categorical
(CatBoost),
light
machine
(LightGBM),
extra
trees
regressor
predict
specimens.
For
optimum
performance,
hyperparameters
these
were
optimized
using
GridSearchCV
optimization.
total
failure
could
be
predicted
with
coefficient
determination
greater
than
0.9.
A
importance
analysis
was
SHapley
Additive
exPlanations
(SHAP)
approach.
LightGBM
showed
highest
among
(R2
=
0.9054,
RMSE
1.3668,
MSE
1.8682).
Язык: Английский
PREDICTION OF THE COMPRESSIVE AND TENSILE STRENGTH OF HIGH-PERFORMANCE CONCRETE BASED ON A HYBRID MODEL OF MULTILAYER PERCEPTRON (MLP) AND LIGHTGBM
Ceramics - Silikaty,
Год журнала:
2025,
Номер
unknown, С. 0 - 0
Опубликована: Май 13, 2025
Язык: Английский
Predicting compressive and tensile strength of concrete with different sand types using machine learning
Ain Shams Engineering Journal,
Год журнала:
2025,
Номер
16(8), С. 103474 - 103474
Опубликована: Май 14, 2025
Язык: Английский
Predicting the area moment of inertia of beam and column using machine learning and HyperNetExplorer
Neural Computing and Applications,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 30, 2025
Язык: Английский
Prediction of autogenous shrinkage in ultra-high-performance concrete (UHPC) using hybridized machine learning
Asian Journal of Civil Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Окт. 30, 2024
Язык: Английский
Adaptive Neural Architecture Search Using Meta-Heuristics: Discovering Fine-Tuned Predictive Models for Photocatalytic CO2 Reduction
Sustainability,
Год журнала:
2024,
Номер
16(23), С. 10756 - 10756
Опубликована: Дек. 8, 2024
This
study
aims
to
contribute
the
reduction
of
carbon
dioxide
and
production
hydrogen
through
an
investigation
photocatalytic
reaction
process.
Machine
learning
algorithms
can
be
used
predict
yield
in
Although
regression-based
approaches
provide
good
results,
accuracy
achieved
with
classification
is
not
very
high.
In
this
context,
presents
a
new
method,
Adaptive
Neural
Architecture
Search
(NAS)
using
metaheuristics,
improve
capacity
ANNs
estimating
process
classification.
The
NAS
was
carried
out
tool
named
HyperNetExplorer,
which
developed
aim
finding
ANN
architecture
providing
best
prediction
changing
hyperparameters,
such
as
number
layers,
neurons
each
layer,
activation
functions
layer.
nature
adaptive,
since
accomplished
optimization
algorithms.
discovered
HyperNetExplorer
demonstrated
significantly
higher
performance
than
classical
ML
results
indicated
that
helped
achieve
better
estimation
Язык: Английский