Optimization of multi-pass coating for magnetic-thermal-assisted laser cladding based on data-enhanced WOA-DE-TELM and LHS-AMOPSO algorithm
Surface and Coatings Technology,
Год журнала:
2025,
Номер
497, С. 131765 - 131765
Опубликована: Янв. 10, 2025
Язык: Английский
Reply to Discussion by Adoko on “Refined Approaches for Open Stope Stability Analysis in Mining Environments: Hybrid SVM Model with Multi‑optimization Strategies and GP Technique” Rock Mech Rock Eng, 57, 9781–9804
Rock Mechanics and Rock Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 2, 2025
Язык: Английский
Reliability analysis of soil slopes stabilized with piles under rainfall
Journal of Rock Mechanics and Geotechnical Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Фев. 1, 2025
Язык: Английский
Ground Settlement Prediction in Urban Tunnelling: Leveraging Metaheuristic-Optimized Random Forest Models
Arabian Journal for Science and Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 5, 2025
Abstract
With
the
continuous
acceleration
of
urbanization,
problem
ground
settlement
induced
by
underground
tunnel
construction
has
received
more
and
widespread
attention.
This
study
addresses
challenge
predicting
surface
subsidence
in
urban
construction,
a
critical
concern
geotechnical
engineering.
Random
forest
(RF)
models
were
optimized
using
three
distinct
metaheuristic
algorithms:
ant
lion
optimizer
(ALO),
multiverse
(MVO),
grasshopper
optimization
algorithm
(GOA).
The
enhancements
significantly
improved
model
accuracy,
as
demonstrated
detailed
performance
metrics
GOA-optimized
RF
(GOA-RF
Pop
=
20)
on
Changsha
Metro
Line
3
dataset,
which
included
294
instances
12
feature
parameters.
achieved
an
MAE
1.3820,
MAPE
181.2249,
correlation
coefficient
0.9273,
RMSE
2.5209
training
set;
2.4695,
275.2054,
R
value
0.8877,
4.2540
testing
set.
A
sensitivity
analysis
within
random
framework
revealed
that
torque
(To)
condition
(Gc)
had
most
significant
impact
subsidence,
whereas
influence
modified
dynamic
penetration
test
(MDPT)
was
least
pronounced.
Additionally,
MATLAB-based
application
developed
App
Designer
module,
integrating
these
into
user-friendly
GUI
facilitates
prediction
management
risks,
thereby
enhancing
practical
effectiveness
engineering
risk
mitigation
strategies.
Язык: Английский
Predicting the minimum horizontal principal stress using genetic expression programming and borehole breakout data
Journal of Rock Mechanics and Geotechnical Engineering,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression
Fractal and Fractional,
Год журнала:
2024,
Номер
8(12), С. 717 - 717
Опубликована: Дек. 5, 2024
Analyzing
geological
profiles
is
of
great
importance
for
various
applications
such
as
natural
resource
management,
environmental
assessment,
and
mining
engineering
projects.
This
study
presents
a
novel
geostatistical
approach
subsurface
profile
interpolation
using
fractional
kriging
method
enhanced
by
random
forest
regression.
Using
bedrock
elevation
data
from
49
boreholes
in
area
southeast
China,
we
first
use
regression
to
predict
optimize
variogram
parameters.
We
then
the
interpolate
analyze
variability.
also
compare
proposed
model
with
traditional
methods,
including
linear
regression,
K-nearest
neighbors,
ordinary
kriging,
cross-validation
metrics.
The
results
indicate
that
reduces
prediction
errors
enhances
spatial
reliability
compared
other
models.
MSE
25%
lower
than
10%
kriging.
In
addition,
execution
time
slightly
higher
findings
suggest
effectively
captures
complex
relationships,
offering
reliable
precise
solution
performing
tasks.
Язык: Английский
Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
Applied Sciences,
Год журнала:
2024,
Номер
14(17), С. 7855 - 7855
Опубликована: Сен. 4, 2024
Given
the
critical
role
of
true
triaxial
strength
assessment
in
underground
rock
and
soil
engineering
design
construction,
this
study
explores
sandstone
using
data-driven
machine
learning
approaches.
Fourteen
distinct
test
datasets
were
collected
from
existing
literature
randomly
divided
into
training
(70%)
testing
(30%)
sets.
A
Multilayer
Perceptron
(MLP)
model
was
developed
with
uniaxial
compressive
(UCS,
σc),
intermediate
principal
stress
(σ2),
minimum
(σ3)
as
inputs
maximum
(σ1)
at
failure
output.
The
optimized
Harris
hawks
optimization
(HHO)
algorithm
to
fine-tune
hyperparameters.
By
adjusting
structure
activation
function
characteristics,
final
made
continuously
differentiable,
enhancing
its
potential
for
numerical
analysis
applications.
Four
HHO-MLP
models
different
functions
trained
validated
on
set.
Based
comparison
prediction
accuracy
meridian
plane
analysis,
an
high
predictive
meridional
behavior
consistent
theoretical
trends
selected.
Compared
five
traditional
criteria
(Drucker–Prager,
Hoek–Brown,
Mogi–Coulomb,
modified
Lade,
Weibols–Cook),
demonstrated
superior
performance
both
datasets.
It
successfully
captured
complete
variation
space,
showing
smooth
continuous
envelopes
deviatoric
planes.
These
results
underscore
model’s
ability
generalize
across
conditions,
highlighting
a
powerful
tool
predicting
geotechnical
Язык: Английский