Applied Sciences,
Год журнала:
2023,
Номер
13(4), С. 2612 - 2612
Опубликована: Фев. 17, 2023
The
brittleness
of
rock
is
known
to
be
an
important
property
that
affects
the
fragmentation
characteristics
in
mechanized
cutting.
As
interaction
between
cutting
tool
and
(i.e.,
cutter
forces,
efficiency,
s/p
ratio,
abrasivity)
during
mechanical
strongly
influenced
by
fragmentation,
tools
disc
pick
cutter)
experience
different
behaviors
depending
on
brittleness.
In
this
study,
relationships
abrasivity
rock,
efficiency
a
Tunnel
Boring
Machine
(TBM)
were
investigated
for
Korean
types.
was
calculated
mathematical
relations
uniaxial
compressive
Brazilian
tensile
strengths
rock.
evaluated
forces
specific
energy
from
linear
machine
(LCM)
test
Cerchar
index
(CAI)
test,
respectively.
results
show
significantly
correlated
with
CAI
values.
Consequently,
some
prediction
models
energy,
proposed
as
functions
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.
Smart Construction and Sustainable Cities,
Год журнала:
2023,
Номер
1(1)
Опубликована: Авг. 9, 2023
Abstract
Preventing/mitigating
natural
disasters
in
urban
areas
can
indirectly
be
part
of
the
17
sustainable
economic
and
social
development
intentions
according
to
United
Nations
2015.
Four
types
disasters—flooding,
heavy
rain-induced
slope
failures/landslides;
earthquakes
causing
structure
failure/collapse,
land
subsidence—are
briefly
considered
this
article.
With
increased
frequency
climate
change-induced
extreme
weathers,
numbers
flooding
failures/landslides
has
recent
years.
There
are
both
engineering
methods
prevent
their
occurrence,
more
effectively
early
prediction
warning
systems
mitigate
resulting
damage.
However,
still
cannot
predicted
an
extent
that
is
sufficient
avoid
damage,
developing
adopting
structures
resilient
against
earthquakes,
is,
featuring
earthquake
resistance,
vibration
damping,
seismic
isolation,
essential
tasks
for
city
development.
Land
subsidence
results
from
human
activity,
mainly
due
excessive
pumping
groundwater,
which
a
“natural”
disaster
caused
by
activity.
Countermeasures
include
effective
regional
and/or
national
freshwater
management
local
water
recycling
groundwater.
Finally,
perspectives
risk
hazard
prevention
through
enhanced
field
monitoring,
assessment
with
multi-criteria
decision-making
(MCDM),
artificial
intelligence
(AI)
technology.
Smart Construction and Sustainable Cities,
Год журнала:
2023,
Номер
1(1)
Опубликована: Ноя. 10, 2023
Abstract
Efforts
to
reduce
the
weight
of
buildings
and
structures,
counteract
seismic
threat
human
life,
cut
down
on
construction
expenses
are
widespread.
A
strategy
employed
address
these
challenges
involves
adoption
foam
concrete.
Unlike
traditional
concrete,
concrete
maintains
standard
composition
but
excludes
coarse
aggregates,
substituting
them
with
a
agent.
This
alteration
serves
dual
purpose:
diminishing
concrete’s
overall
weight,
thereby
achieving
lower
density
than
regular
creating
voids
within
material
due
agent,
resulting
in
excellent
thermal
conductivity.
article
delves
into
presentation
statistical
models
utilizing
three
different
methods—linear
(LR),
non-linear
(NLR),
artificial
neural
network
(ANN)—to
predict
compressive
strength
These
formulated
based
dataset
97
sets
experimental
data
sourced
from
prior
research
endeavors.
comparative
evaluation
outcomes
is
subsequently
conducted,
leveraging
benchmarks
like
coefficient
determination
(
R
2
),
root
mean
square
error
(RMSE),
absolute
(MAE),
aim
identifying
most
proficient
model.
The
results
underscore
remarkable
effectiveness
ANN
evident
model’s
value,
which
surpasses
that
LR
model
by
36%
22%.
Furthermore,
demonstrates
significantly
MAE
RMSE
values
compared
both
NLR
models.
