Hybrid Metaheuristic Optimization Algorithms with Least-Squares Support Vector Machine and Boosted Regression Tree Models for Prediction of Air-Blast Due to Mine Blasting
Natural Resources Research,
Год журнала:
2024,
Номер
33(3), С. 1349 - 1363
Опубликована: Март 11, 2024
Язык: Английский
Estimating Brazilian Tensile Strength of Granite Rocks Using Metaheuristic Algorithms-Based Self-Organizing Neural Networks
Rock Mechanics and Rock Engineering,
Год журнала:
2024,
Номер
57(7), С. 4653 - 4668
Опубликована: Март 13, 2024
Язык: Английский
Prediction of peak particle vibration velocity based on intelligent optimization algorithm combined with XGBoost
Expert Systems with Applications,
Год журнала:
2025,
Номер
unknown, С. 127654 - 127654
Опубликована: Апрель 1, 2025
Язык: Английский
Prediction of blast vibration velocity based on multi-model dynamic weighting ensemble
Mechanics of Advanced Materials and Structures,
Год журнала:
2025,
Номер
unknown, С. 1 - 18
Опубликована: Апрель 27, 2025
Язык: Английский
Advancing the Prediction and Evaluation of Blast-Induced Ground Vibration Using Deep Ensemble Learning with Uncertainty Assessment
Geosciences,
Год журнала:
2025,
Номер
15(5), С. 182 - 182
Опубликована: Май 19, 2025
Ground
vibration
is
one
of
the
most
dangerous
environmental
problems
associated
with
blasting
operations
in
mining.
Therefore,
accurate
prediction
and
controlling
blast-induced
ground
are
imperative
for
protection
sustainable
development.
The
empirical
approaches
give
inaccurate
results,
as
evident
literature.
Hence,
numerous
researchers
have
started
to
use
fast-growing
soft
computing
that
satisfying
performance.
However,
achieving
high-prediction
performance
detecting
uncertainty
crucial,
especially
operations.
This
study
aims
propose
a
deep
ensemble
model
predict
quantify
uncertainty,
which
usually
not
addressed.
used
200
published
data
from
ten
granite
quarry
sites
Ibadan
Abeokuta
areas,
Nigeria.
equation
(United
States
Bureau
Mines-based
approach)
was
applied
comparison.
comparison
models
demonstrated
proposed
achieved
superior
performance,
offering
more
predictions
reliable
quantification.
Specifically,
it
exhibited
lowest
root
mean
square
error
(22.674),
negative
log-likelihood
(4.44),
interval
width
(1.769),
alongside
highest
R2
value
(0.77)
coverage
probability
(0.95).
reached
desired
95%,
demonstrating
underestimated
or
overestimated.
Язык: Английский
A comprehensive survey on machine learning applications for drilling and blasting in surface mining
Machine Learning with Applications,
Год журнала:
2023,
Номер
15, С. 100517 - 100517
Опубликована: Дек. 11, 2023
Drilling
and
blasting
operations
are
pivotal
for
productivity
safety
in
hard
rock
surface
mining.
These
restricted
due
to
complexities
such
as
site-specific
uncertainties,
risks,
environmental
economic
constraints.
Machine
Learning
(ML)
is
a
transformative
approach
tackle
these
resulting
significant
cost
reductions.
ML
applications
can
reduce
overall
costs
by
up
23%
decrease
the
amount
of
explosives
much
89%
compared
traditional
methods.
This
survey
presents
comprehensive
review
how
be
applied
optimize
drill
blast
designs
while
accounting
its
operational
challenges.
Our
research
highlights
difficulties
collecting
quality
data,
complexity
interpreting
this
data
into
insightful
information,
selection
models
relating
mining
objectives,
need
established
methods
assess
efficiency
quantitatively.
We
provide
synthesis
model
development
practices
drilling
demonstrate
value
methodologies.
Based
on
our
survey,
we
present
actionable
recommendations
developing
methodologies
improve
safety,
costs,
enhance
processes.
includes
establishing
standardized
schematics,
multiobjective
optimization,
evaluation
metrics.
benefits
guide
mine
management
engineers
adopt
techniques
on-ground
practices.
aims
serve
resource
both
practitioners
researchers
shaping
future
direction
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
Prediction of peak particle velocity using hybrid random forest approach
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Дек. 28, 2024
Blasting
excavation
is
widely
used
in
mining,
tunneling
and
construction
industries,
but
it
leads
to
produce
ground
vibration
which
can
seriously
damage
the
urban
communities.
The
peak
particle
velocity
(PPV)
one
of
main
indicators
for
determining
extent
vibration.
Owing
complexity
blasting
process,
there
controversy
over
parameters
will
be
considered
as
inputs
empirical
equations
machine
learning
(ML)
algorithms.
According
current
researches,
burden
has
controversial
impact
on
blast-induced
To
judge
whether
affects
vibration,
data
considering
have
been
recorded
at
Wujiata
coal
mine.
Correlation
coefficient
analyze
relationship
between
variables,
correlation
distance
from
center
monitored
point
(R)
greatest
value
-
0.67.
This
study
firstly
summarizes
most
common
equations,
a
new
equation
established
by
dimension
analysis.
shows
better
performance
predicting
PPV
than
other
regression
Secondly,
confirmed
applicability
PPV.
Based
assessments,
error
characteristic
curve
Uncertainty
analysis
first
round
PPV,
random
forest
(RF)
K-Nearest
Neighbors
(KNN)
show
four
Then,
second
round,
based
artithmetic
optimization
algorithm
(AOA),
optimized
(AOA-RF)
model
accurate
compared
with
(AOA-KNN)
presented
literature.
Finally,
points
predicted
informed
danger
are
marked
Chinese
safety
regulations
blasting.
Язык: Английский
Measurement and Prediction of Blast-Induced Flyrock Distance Using Unmanned Aerial Vehicles and Metaheuristic-Optimized ANFIS Neural Networks
Natural Resources Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 21, 2024
Язык: Английский
Chaos Game Optimization-Hybridized Artificial Neural Network for Predicting Blast-Induced Ground Vibration
Applied Sciences,
Год журнала:
2024,
Номер
14(9), С. 3759 - 3759
Опубликована: Апрель 28, 2024
In
this
study,
we
introduced
the
chaos
game
optimization-artificial
neural
network
(CGO-ANN)
model
as
a
novel
approach
for
predicting
peak
particle
velocity
(PPV)
induced
by
mine
blasting.
The
CGO-ANN
is
compared
with
other
established
methods,
including
swarm
(PSO-ANN),
genetic
algorithm-artificial
(GA-ANN),
single
ANN,
and
USBM
empirical
model.
aim
to
demonstrate
superiority
of
PPV
prediction.
Utilizing
dataset
comprising
180
blasting
events
from
Tonglushan
Copper
Mine
in
China,
investigated
performance
each
results
showed
that
outperforms
models
terms
prediction
accuracy
robustness.
This
study
highlights
effectiveness
promising
tool
mining
operations,
contributing
safer
more
efficient
practices.
Язык: Английский