Influence of nonlinear thermal radiation and exponential space dependent heat source on hybrid nanofluid stagnation point flow over a shrinking riga surface
Multiscale and Multidisciplinary Modeling Experiments and Design,
Год журнала:
2025,
Номер
8(3)
Опубликована: Фев. 12, 2025
Язык: Английский
Evaluating the strength of industrial wastesbased concrete reinforced with steel fiber using advanced machine learning
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Март 8, 2025
The
traditional
evaluation
of
compressive
strength
through
repeated
experimental
works
can
be
resource-intensive,
time-consuming,
and
environmentally
taxing.
Leveraging
advanced
machine
learning
(ML)
offers
a
faster,
cheaper,
more
sustainable
alternative
for
evaluating
optimizing
concrete
properties,
particularly
materials
incorporating
industrial
wastes
steel
fibers.
In
this
research
work,
total
166
records
were
collected
partitioned
into
training
set
(130
=
80%)
validation
(36
20%)
in
line
with
the
requirements
data
partitioning
sorting
optimal
model
performance.
These
entries
represented
ten
(10)
components
fiber
reinforced
such
as
C,
W,
FAg,
CAg,
PL,
SF,
FA,
Vf,
FbL,
FbD,
which
applied
input
variables
Cs,
was
target.
Advanced
techniques
to
(Cs)
"Semi-supervised
classifier
(Kstar)",
"M5
(M5Rules),
"Elastic
net
(ElasticNet),
"Correlated
Nystrom
Views
(XNV)",
"Decision
Table
(DT)".
All
models
created
using
2024
"Weka
Data
Mining"
software
version
3.8.6.
Also,
accuracies
developed
evaluated
by
comparing
sum
squared
error
(SSE),
mean
absolute
(MAE),
(MSE),
root
(RMSE),
Error
(%),
Accuracy
(%)
coefficient
determination
(R2),
correlation
(R),
willmott
index
(WI),
Nash–Sutcliffe
efficiency
(NSE),
Kling–Gupta
(KGE)
symmetric
percentage
(SMAPE)
between
predicted
calculated
values
output.
At
end,
has
been
found
transformative
approach
that
enhances
efficiency,
cost-effectiveness,
sustainability
wastes-based
fiber.
Among
reviewed,
Kstar
DT
emerge
most
practical
achieving
precise
results.
Their
adoption
significantly
reduce
environmental
impacts
promote
use
by-products
construction.
sensitivity
on
produced
36%
from
71%
70%
60%
34%
5%
33%
67%
61%
61%.
Fiber
Volume
Fraction
(Vf)
(67%)
high
suggests
content
greatly
crack
resistance
tensile
strength.
Steel
Orientation
(61%)
indicates
importance
alignment
distributing
stresses
enhancing
structural
integrity.
Язык: Английский
Towards sustainable construction: estimating compressive strength of waste foundry sand-blended green concrete using a hybrid machine learning approach
Deleted Journal,
Год журнала:
2025,
Номер
2(1)
Опубликована: Март 3, 2025
Язык: Английский
Compressive Strength Prediction of Geopolymers Using Stacking Ensemble and Fuzzy Splitting
Iranian Journal of Science and Technology Transactions of Civil Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 23, 2025
Язык: Английский
Numerical computation of heat and mass transport for the higher Reynolds stress tensor modelling of generalised Newtonian fluid in a rotating surface: Milne’s predictor corrector method
Multiscale and Multidisciplinary Modeling Experiments and Design,
Год журнала:
2025,
Номер
8(4)
Опубликована: Фев. 26, 2025
Язык: Английский
Thermophoretic particle deposition in a mixed convective bioconvection nanofluid with thermal radiation and chemical reaction over an exponential stretching sheet
Multiscale and Multidisciplinary Modeling Experiments and Design,
Год журнала:
2025,
Номер
8(4)
Опубликована: Март 9, 2025
Язык: Английский
Advancing Hybrid Fiber-Reinforced Concrete: Performance, Crack Resistance Mechanism, and Future Innovations
Buildings,
Год журнала:
2025,
Номер
15(8), С. 1247 - 1247
Опубликована: Апрель 10, 2025
This
research
investigates
the
effects
of
steel
(ST)
and
synthetic
(SYN)
fibers
on
workability
mechanical
properties
HPFRC.
It
also
analyzes
their
influence
material’s
microstructural
characteristics.
ST
improve
tensile
strength,
fracture
toughness,
post-cracking
performance
owing
to
rigidity,
interlocking,
robust
adhesion
with
matrix.
SYN
fibers,
conversely,
mitigate
shrinkage-induced
micro-cracking,
augment
ductility,
enhance
concrete
under
dynamic
stress
while
exerting
negative
workability.
Hybrid
fiber
systems,
which
include
offer
synergistic
advantages
by
enhancing
management
at
various
scales
augmenting
ductility
energy
absorption
capability.
Scanning
electron
microscopy
(SEM)
has
been
crucial
in
investigating
fiber–matrix
interactions,
elucidating
hydration,
crack-bridging
mechanisms,
interfacial
bonding.
establish
thick
zones
that
facilitate
effective
transfer,
whereas
reduce
micro-crack
formation
long-term
durability.
Nonetheless,
deficiencies
persist,
encompassing
optimal
hybrid
configurations,
enduring
fiber-reinforced
(FRC),
sustainable
substitutes.
Future
investigations
should
examine
multi-scale
reinforcing
techniques,
intelligent
for
structural
health
assessment,
alternatives.
The
standardization
testing
methodologies
cost–benefit
analyses
is
essential
promote
industrial
deployment.
review
offers
a
thorough
synthesis
existing
knowledge,
emphasizing
advancements
potential
HPFRC
high-performance
construction
applications.
findings
development
new,
durable,
resilient
systems
solving
current
difficulties.
Язык: Английский
Impact of lightweight clay aggregate with slag and biomedical waste ash on self-compacting concrete using machine learning approach
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Апрель 22, 2025
The
self-compacting
concrete
(SCC)
mixes
were
developed
using
lightweight
expandable
clay
aggregate
(LECA)
as
a
partial
substitute
for
coarse
aggregate,
ground
granulated
blast-furnace
slag
(GGBS)
replacement
cement,
and
combusted
bio-medical
waste
ash
(BMWA)
fine
aggregate.
substitution
levels
LECA,
GGBS,
BMWA
set
at
10%,
20%,
30%
of
respectively.
M30-grade
SCC
designed
with
two
different
water-to-binder
ratios-0.40
0.45-and
their
compressive
strength
(CS)
was
experimentally
evaluated.
data
entries
from
the
above
mix
designs
experiments
collected
in
this
research
which
deals
evaluating
impact
metallurgical
slag,
on
concrete.
An
extensive
literature
search
used
project
produced
global
representative
database
literature.
384
records
divided
into
training
(300
=
80%)
validation
(84
20%)
line
requirements
more
reliable
partitioning.
Six
advanced
machine
learning
methods
such
Artificial
Neural
Network
(ANN),
Support
Vector
Regression
(SVR),
K-Nearest
Neighbors
(KNN),
eXtreme
Gradient
Boosting
(XGB),
Random
Forest
(RF),
Adaptive
(AdaBoost)
to
model
behavior.
All
models
created
"Orange
Data
Mining"
software
version
3.36.
A
combination
error
metrics,
efficiency
metrics
determination/correlation
test
performance
accuracy.
Also,
Hoffman
Gardener's
method
evaluate
sensitivity
analysis
variables.
At
end
work,
AdaBoost
KNN
excel
predictive
accuracy
97.5%,
reducing
margin
ensuring
precise
SCC.
SVR,
XGB,
RF
also
exhibit
strong
(96.5-97%),
supporting
material
selection
proportions.
demonstrate
lowest
errors
(MAE:
0.65
MPa,
RMSE:
0.75
MPa),
indicating
performance,
minimizing
overdesign
or
underperformance
risks,
optimizing
usage.
Hoffman/Gardener's
GGBS
31%
Dens
26%
highest
is
followed
by
LECA
21%
20%.
This
enables
optimization
learning,
experimental
trials,
enhancing
efficiency,
lowering
environmental
impact,
promoting
sustainable
construction
through
effective
reuse
industrial
by-products.
Язык: Английский