International Journal of Pavement Engineering,
Journal Year:
2022,
Volume and Issue:
24(2)
Published: Oct. 29, 2022
Each
type
of
soil
has
different
optimal
stabilisation
additive
content.
To
design
the
component,
reliable
and
efficient
models
are
required.
The
study
proposes
Machine
Learning
(ML)
model
Support
Vector
Regression
(SVR)
to
predict
Unconfined
Compressive
Strength
(UCS)
stabilised
soil.
be
able
deliver
performance,
five
metaheuristic
algorithms:
Simulated
Annealing
(SA),
Random
Restart
Hill
Climbing
(RRHC),
Particle
swarm
optimisation
(PSO),
Hunger
Games
Search
(HGS)
Slime
Mould
Algorithm
(SMA)
integrated
with
SVR
model.
explore
effect
number
inputs
on
model's
data
was
divided
into
two
scenarios
input
variable
number.
ML
evaluated
by
K-Fold
numerical
indicators
R2,
RMSE
MAE.
results
show
that
in
Scenario
1,
SVR-HGS
a
higher
predictive
performance
than
other
models.
While
2,
SVR-PSO
gives
better
remaining
SHapley
Additive
exPlanation
(SHAP)
Partial
Dependence
Plots
2D
(PDP)
were
used
gain
insight
effects
variables
UCS,
cement
lime
variables.
Obtaining
have
an
important
influence
variation
which
is
considered
most
significant
variable.
detection
A-line
value
relatively
predictor
UCS.
At
suitable
value,
it
possible
reduce
content
chemical
stabilising
agents
(cement,
lime)
while
maintaining
UCS
at
relative
threshold.
Smart Construction and Sustainable Cities,
Journal Year:
2023,
Volume and Issue:
1(1)
Published: Aug. 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.
Ecological Informatics,
Journal Year:
2024,
Volume and Issue:
80, P. 102514 - 102514
Published: Feb. 13, 2024
This
study
assessed
water
quality
(WQ)
in
Tongi
Canal,
an
ecologically
critical
and
economically
important
urban
canal
Bangladesh.
The
researchers
employed
the
Root
Mean
Square
Water
Quality
Index
(RMS-WQI)
model,
utilizing
seven
WQ
indicators,
including
temperature,
dissolve
oxygen,
electrical
conductivity,
lead,
cadmium,
iron
to
calculate
index
(WQI)
score.
results
showed
that
most
of
sampling
locations
poor
WQ,
with
many
indicators
violating
Bangladesh's
environmental
conservation
regulations.
eight
machine
learning
algorithms,
where
Gaussian
process
regression
(GPR)
model
demonstrated
superior
performance
(training
RMSE
=
1.77,
testing
0.0006)
predicting
WQI
scores.
To
validate
GPR
model's
performance,
several
measures,
coefficient
determination
(R2),
Nash-Sutcliffe
efficiency
(NSE),
factor
(MEF),
Z
statistics,
Taylor
diagram
analysis,
were
employed.
exhibited
higher
sensitivity
(R2
1.0)
(NSE
1.0,
MEF
0.0)
WQ.
analysis
uncertainty
(standard
7.08
±
0.9025;
expanded
1.846)
indicates
RMS-WQI
holds
potential
for
assessing
inland
waterbodies.
These
findings
indicate
could
be
effective
approach
waters
across
study's
did
not
meet
recommended
guidelines,
indicating
Canal
is
unsafe
unsuitable
various
purposes.
implications
extend
beyond
contribute
management
initiatives
