Mining of Mineral Deposits,
Год журнала:
2023,
Номер
17(2), С. 9 - 19
Опубликована: Июнь 30, 2023
Purpose.
Creating
a
generalized
algorithm
to
account
for
factors
(coal
seam
thickness,
enclosed
rock
mechanical
properties,
the
dimension
and
bearing
capacity
of
artificial
support
patterns)
causing
gateroad
state
under
effect
longwall
face
goaf.
Methods.
The
assessment
stability
is
based
on
numerical
simulation
stress-strain
(SSS).
finite
element
method
used
find
out
changes
in
SSS
surrounding
rocks
at
various
stages
mining.
elastic-plastic
constitutive
model
Hoek-Brown
failure
criterion
implemented
codes
RS2
RS3
(Rocscience)
are
applied
determine
displacements
dependently
coal
strength,
width
strength
(a
packwall
comprised
hardening
mixture
“BI-lining”).
To
specify
properties
material
series
experimental
tests
were
conducted.
A
computational
experiment
dealing
with
81
combinations
affecting
was
carried
estimate
roof
slag
floor
heaving
behind
face.
group
data
handling
(GMDH
)
employed
generalize
relationships
between
factors.
Findings.
roof-to-floor
closure
has
been
determined
intersection
goaf
packwall,
material.
It
revealed
that
gains
value
30
MPa
3rd
day
from
its
beginning
use
which
fully
corresponding
requirements
protective
capacity.
possibility
using
untreated
mine
water
liquefy
proved,
allows
simplifying
optimizing
solute
mixing
pumping
technology.
Originality.
This
study
contributes
improving
understanding
influence
underground
mining
operations
highlights
importance
utilizing
simulations
designs.
impact
each
factor
resulting
variable
(decrease
cross-section
gate
road
by
height)
combinatorial
structural
identification
estimated
as
follows:
48%,
thickness
25%,
enclosing
23%,
4%.
Practical
implications.
findings
provide
stakeholders
technique
reasonable
parameters
systems,
predictive
developed
can
be
mitigate
potential
instability
issues
excavations.
results
have
implications
similar
geological
settings
valuable
design
optimization
other
regions.
Journal of Rock Mechanics and Geotechnical Engineering,
Год журнала:
2020,
Номер
13(1), С. 188 - 201
Опубликована: Ноя. 23, 2020
Slope
failures
lead
to
catastrophic
consequences
in
numerous
countries
and
thus
the
stability
assessment
for
slopes
is
of
high
interest
geotechnical
geological
engineering
researches.
A
hybrid
stacking
ensemble
approach
proposed
this
study
enhancing
prediction
slope
stability.
In
approach,
we
used
an
artificial
bee
colony
(ABC)
algorithm
find
out
best
combination
base
classifiers
(level
0)
determined
a
suitable
meta-classifier
1)
from
pool
11
individual
optimized
machine
learning
(OML)
algorithms.
Finite
element
analysis
(FEA)
was
conducted
order
form
synthetic
database
training
stage
(150
cases)
model
while
107
real
field
cases
were
testing
stage.
The
results
by
then
compared
with
that
obtained
OML
methods
using
confusion
matrix,
F1-score,
area
under
curve,
i.e.
AUC-score.
comparisons
showed
significant
improvement
ability
has
been
achieved
(AUC
=
90.4%),
which
7%
higher
than
82.9%).
Then,
further
comparison
undertaken
between
method
basic
classifier
on
prediction.
prominent
performance
over
method.
Finally,
importance
variables
studied
linear
vector
quantization
(LVQ)
Geoscience Frontiers,
Год журнала:
2023,
Номер
14(6), С. 101645 - 101645
Опубликована: Июнь 7, 2023
The
application
of
ensemble
learning
models
has
been
continuously
improved
in
recent
landslide
susceptibility
research,
but
most
studies
have
no
unified
framework.
Moreover,
few
papers
discussed
the
applicability
model
mapping
at
township
level.
This
study
aims
defining
a
robust
framework
that
can
become
benchmark
method
for
future
research
dealing
with
comparison
different
models.
For
this
purpose,
present
work
focuses
on
three
basic
classifiers:
decision
tree
(DT),
support
vector
machine
(SVM),
and
multi-layer
perceptron
neural
network
(MLPNN)
two
homogeneous
such
as
random
forest
(RF)
extreme
gradient
boosting
(XGBoost).
hierarchical
construction
deep
relied
leading
technologies
(i.e.,
homogeneous/heterogeneous
bagging,
boosting,
stacking
strategy)
to
provide
more
accurate
effective
spatial
probability
occurrence.
selected
area
is
Dazhou
town,
located
Jurassic
red-strata
Three
Gorges
Reservoir
Area
China,
which
strategic
economic
currently
characterized
by
widespread
risk.
Based
long-term
field
investigation,
inventory
counting
thirty-three
slow-moving
polygons
was
drawn.
results
show
do
not
necessarily
perform
better;
instance,
Bagging
based
DT-SVM-MLPNN-XGBoost
performed
worse
than
single
XGBoost
model.
Amongst
eleven
tested
models,
Stacking
RF-XGBoost
model,
ensemble,
showed
highest
capability
predicting
landslide-affected
areas.
Besides,
factor
behaviors
DT,
SVM,
MLPNN,
RF
reflected
characteristics
landslides
reservoir
area,
wherein
unfavorable
lithological
conditions
intense
human
engineering
activities
water
level
fluctuation,
residential
construction,
farmland
development)
are
proven
be
key
triggers.
presented
approach
could
used
occurrence
prediction
similar
regions
other
fields.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Июль 19, 2024
Concrete
compressive
strength
(CS)
is
a
crucial
performance
parameter
in
concrete
structure
design.
Reliable
prediction
reduces
costs
and
time
design
prevents
material
waste
from
extensive
mixture
trials.
Machine
learning
techniques
solve
structural
engineering
challenges
such
as
CS
prediction.
This
study
used
Learning
(ML)
models
to
enhance
the
of
CS,
analyzing
1030
experimental
data
ranging
2.33
82.60
MPa
previous
research
databases.
The
ML
included
both
non-ensemble
ensemble
types.
were
regression-based,
evolutionary,
neural
network,
fuzzy-inference-system.
Meanwhile,
consisted
adaptive
boosting,
random
forest,
gradient
boosting.
There
eight
input
parameters:
cement,
blast-furnace-slag,
aggregates
(coarse
fine),
fly
ash,
water,
superplasticizer,
curing
days,
with
output.
Comprehensive
evaluations
include
visual
quantitative
methods
k-fold
cross-validation
assess
study's
reliability
accuracy.
A
sensitivity
analysis
using
Shapley-Additive-exPlanations
(SHAP)
was
conducted
understand
better
how
each
variable
affects
CS.
findings
showed
that
Categorical-Gradient-Boosting
(CatBoost)
model
most
accurate
during
testing
stage.
It
had
highest
determination-coefficient
(R
