International Journal of Advanced Research in Science Communication and Technology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 253 - 256
Published: May 14, 2024
When
it
comes
to
estimating,
classifying,
and
forecasting
material
strength
based
on
changing
parameters,
machine
learning
(ML)
techniques
have
shown
be
dependable
methodologies.
It
is
found
that
choosing
the
right
technique
depends
characteristics
of
problem
available
data.
Therefore,
fifteen
different
were
used
a
specific
dataset
concrete
compressive
in
order
assess
accuracy
ML
models
predict
strength.
Due
its
excellent
performance
while
dealing
with
continuous
target
variables
nonlinear
interactions
among
features
target,
Support
Vector
Regressor
(SVR)
had
greatest
prediction
(88.18%)
all
methods
employed.
To
guarantee
structural
integrity
building
projects,
essential
breaking
concrete.
The
goal
this
project
create
model
can
forecast
concrete's
depending
mix's
composition
curing
circumstances.
A
was
created
included
details
regarding
samples,
such
as
mix
ratios,
temperatures,
times,
strengths.
recise
estimation
crucial
for
advancement
construction.
bibliometric
analysis
pertinent
literature
published
conducted
comprehend
state
research
field
prediction.
previous
ten
years
seen
first
sector.
database
consisted
31,35
journal
articles
between
2012
2021
Web
Science
core
database.
knowledge
map
using
Cite
Space
6.1R2,
visualisation
tool,
analyse
at
macro
level
terms
hotspot
distribution,
spatial
temporal
evolutionary
trends,
respectively.
Next,
we
become
separate
into
two
groups
Smart Cities,
Journal Year:
2024,
Volume and Issue:
7(6), P. 3458 - 3488
Published: Nov. 12, 2024
This
paper
presents
a
comprehensive
review
of
the
transformative
impacts
3D
printing
technology
on
smart
cities.
As
cities
face
rapid
urbanization,
resource
shortages,
and
environmental
degradation,
innovative
solutions
such
as
additive
manufacturing
(AM)
offer
potential
pathways
for
sustainable
urban
development.
By
synthesizing
66
publications
from
2015
to
2024,
study
examines
how
improves
infrastructure,
enhances
sustainability,
fosters
community
engagement
in
city
planning.
Key
benefits
include
reducing
construction
time
material
waste,
lowering
costs,
enabling
creation
scalable,
affordable
housing
solutions.
The
also
addresses
emerging
areas
integration
with
digital
twins
(DTs),
machine
learning
(ML),
AI
optimize
infrastructure
predictive
maintenance.
It
highlights
use
materials
soft
robotics
structural
health
monitoring
(SHM)
repairs.
Despite
promising
advancements,
challenges
remain
terms
cost,
scalability,
need
interdisciplinary
collaboration
among
engineers,
designers,
planners,
policymakers.
findings
suggest
roadmap
future
research
practical
applications
cities,
contributing
ongoing
discourse
technologically
advanced
Buildings,
Journal Year:
2024,
Volume and Issue:
14(2), P. 377 - 377
Published: Feb. 1, 2024
The
determination
of
mechanical
properties
for
different
building
materials
is
a
highly
relevant
and
practical
field
application
machine
learning
(ML)
techniques
within
the
construction
sector.
When
working
with
vibrocentrifuged
concrete
products
structures,
it
crucial
to
consider
factors
related
impact
aggressive
environments.
Artificial
intelligence
methods
can
enhance
prediction
through
use
specialized
algorithms
materials’
strength
determination.
aim
this
article
establish
evaluate
algorithms,
specifically
Linear
Regression
(LR),
Support
Vector
(SVR),
Random
Forest
(RF),
CatBoost
(CB),
compressive
in
under
diverse
operational
conditions.
This
achieved
by
utilizing
comprehensive
database
experimental
values
obtained
laboratory
settings.
following
metrics
were
used
analyze
accuracy
constructed
regression
models:
Mean
Absolute
Error
(MAE),
Squared
(MSE),
Root-Mean-Square
(RMSE),
Percentage
(MAPE)
coefficient
(R2).
average
MAPE
range
from
2%
(RF,
CB)
7%
(LR,
SVR)
allowed
us
draw
conclusions
about
possibility
using
“smart”
development
compositions
quality
control
concrete,
which
ultimately
entails
improvement
acceleration
manufacture.
best
model,
CatBoost,
showed
MAE
=
0.89,
MSE
4.37,
RMSE
2.09,
R2
0.94.
The
procedures
used
to
create
modern
cars
require
extensive
thought
in
various
relevant
scientific
domains.
Arguably,
the
most
challenging
obstacle
facing
automobile
sector
is
managing
production
facilities
by
integrating
software
lines
and
CI/CD.
All
this
determined
market
demands,
engine
of
a
vehicle,
complexity
assembling
entire
car
installing
its
corresponding
embedded
software.
As
result,
concerns
about
types
global
change
have
grown,
as
well
lack
ability
use
fossil
fuels,
creating
substantial
impact
on
purchase
sale
automobiles.
research
foundation
reflected
covering
strategies
for
deployment
administration
software,
opportunities
business
improvement
particular
processes.
This
article
strives
provide
summary
investigation
original
equipment
manufacturers,
segmentation,
effects
changes
automotive
manufacturing
examining
correlation
between
certain
specific
brand
powertrain
vehicle.
tries
examine
numerous
datasets
from
United
States
America
Washington
State,
basis
which
we
may
estimate
possible
future
industry’s
sales.
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(7), P. 3715 - 3715
Published: March 28, 2025
This
review
article
provides
an
in-depth
exploration
of
the
recent
advancements
in
seismic
analysis
and
design
steel–concrete
composite
structures,
as
reflected
literature
from
last
ten
years.
It
investigates
key
factors,
such
material
behavior,
connection
detailing,
analytical
modeling
techniques,
methodologies.
The
highlights
synergistic
benefits
derived
combination
steel
concrete
components
to
improve
performance.
Various
systems,
including
beams,
beam-columns,
frames,
shear
walls,
foundations,
beam–column
joints,
are
analyzed
through
experimental
studies
assess
their
dynamic
response
characteristics
under
extreme
earthquake
conditions.
evaluates
advanced
numerical
methods,
finite
element
fiber-based
models,
for
capability
predict
nonlinear
behavior
buildings
bridges.
A
comparative
modern
isolation
energy
dissipation
techniques
is
also
included.
Furthermore,
optimization
structures
seismically
active
regions
discussed.
concludes
by
identifying
areas
where
additional
research
necessary
enhance
resilience
structures.
