REVIEWS ON ADVANCED MATERIALS SCIENCE,
Journal Year:
2024,
Volume and Issue:
63(1)
Published: Jan. 1, 2024
Abstract
This
research
integrated
glass
powder
(GP),
marble
(MP),
and
silica
fume
(SF)
into
rubberized
mortar
to
evaluate
their
effectiveness
in
enhancing
compressive
strength
(
fc′
{f}_{\text{c}}^{^{\prime}
}
).
Rubberized
cubes
were
produced
by
replacing
fine
aggregates
with
shredded
rubber
varying
proportions.
The
decrease
mortar’s
was
controlled
substituting
cement
GP,
MP,
SF.
Although
many
literature
studies
have
evaluated
the
suitability
of
industrial
waste,
such
as
SF,
construction
material,
no
yet
included
combined
effect
these
wastes
on
mortar.
study
aims
provide
complete
insight
waste
By
cement,
SF
added
different
proportions
from
5
25%.
Furthermore,
artificial
intelligence
prediction
models
developed
using
experimental
data
assess
determined
that
optimal
substitution
levels
for
15,
10,
15%,
respectively.
Similarly,
partial
dependence
plot
analysis
suggests
GP
a
comparable
machine
learning
demonstrated
significant
resemblance
test
results.
Two
individual
techniques,
support
vector
random
forest,
generate
R
2
values
0.943
0.983,
REVIEWS ON ADVANCED MATERIALS SCIENCE,
Journal Year:
2025,
Volume and Issue:
64(1)
Published: Jan. 1, 2025
Abstract
Two-stage
concrete
(TSC)
is
a
sustainable
material
produced
by
incorporating
coarse
aggregates
into
formwork
and
filling
the
voids
with
specially
formulated
grout
mix.
The
significance
of
this
study
to
improve
predictive
accuracy
TSC’s
tensile
strength,
which
essential
for
optimizing
its
use
in
construction
applications.
To
achieve
objective,
novel
reliable
models
were
developed
using
advanced
machine
learning
algorithms,
including
random
forest
(RF)
gene
expression
programming
(GEP).
performance
these
was
evaluated
important
evaluation
metrics,
coefficient
determination
(
R
2
),
mean
absolute
error
(MAE),
squared
error,
root
square
(RMSE),
after
they
trained
on
comprehensive
dataset.
results
suggest
that
RF
model
outperforms
GEP
model,
as
evidenced
higher
value
0.94
relative
0.91
reduced
MAE
RMSE
values.
This
suggests
has
superior
capability.
Additionally,
sensitivity
analyses
SHapley
Additive
ExPlanation
analysis
revealed
water-to-binder
(W/B)
ratio
most
influential
input
parameter,
accounting
51.01%
outcomes
presented
model.
research
emphasizes
TSC
design,
enhancing
performance,
promoting
sustainable,
cost-effective
construction.
E3S Web of Conferences,
Journal Year:
2024,
Volume and Issue:
529, P. 01035 - 01035
Published: Jan. 1, 2024
In
this
study,
we
examined
the
effect
of
adding
recycled
plastics
to
concrete.
The
waste
were
collected
from
a
local
market.
disposal
is
major
issue
with
many
negative
consequences.
Plastic,
being
inorganic,
does
not
change
chemical
characteristics
concrete
and
has
no
on
its
quality
or
consistency,
making
it
an
ideal
material
for
use
in
construction
industry,
where
may
help
reduce
plastic
waste.
Plastic
dual
uses
as
filler
ingredient
additive
enhance
mechanical
properties
material.
was
prepared
using
five
different
amounts
aggregate
substitution
by
volume:
10%,
20%,
30%,
40%,
50%.
Cubes
beams
cast,
cured,
tested
universal
testing
machine.
A
mixed
proportion
made
ingredients
used
At
7,
21,
28
d,
results
showed
that
compressive
flexural
strengths
increased
percentage
increased.
Also,
strength
improved
increase
waste,
reaching
maximum
at
30%.
These
highlight
that,
fiber
decreases
quantity
industrial
fibers
needed
concrete,
also
proven
be
more
inexpensive.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: Nov. 26, 2024
Rapid
urbanization
has
led
to
a
high
demand
for
concrete,
causing
significant
depletion
of
vital
natural
resources,
notably
river
sand,
which
is
crucial
in
the
manufacturing
process
concrete.
As
result,
there
growing
need
environmentally
sustainable
alternatives
fine
aggregate
Quarry
dust
(QD)
evolved
as
viable
and
ecologically
friendly
substitute
response
this
demand.
In
past,
limited
experimental
investigations
only
conventional
modeling
techniques
were
used
promote
mineral
fillers
This
study
proposed
robust
soft
computing
technique
using
gene-expression
programming
(GEP)
enhance
usability
Initially,
an
was
carried
out
examine
feasibility
mechanical
characteristics
concrete
made
from
materials
including
quarry
superplasticizer
partial
replacement
aggregate.
Ten
mixed
proportions
with
various
(0%,
20%,
40%,
60%)
make
M15
M20
grades
A
series
tests,
such
workability,
compressive
strength
(CS),
tensile
(TS),
conducted
fresh
hardened
properties
modified
The
established
database
then
develop
machine
learning
(ML)
models
GEP.
outcomes
GEP
validated
by
comparing
them
multi-linear
regression
(MLR)
statistical
metrics
root
mean
squared
error
(RMSE),
performance
index
(PI),
correlation
coefficient
(R),
external
validation
methods.
Finally,
sensitivity
analysis
performed
investigate
influence
ingredients
fillers,
superplasticizers,
others
on
To
practical
usage
study,
graphical
user
interface
(GUI)
also
created.
revealed
that
40%
aggregates
filler
shows
optimum
properties.
outperformed
MLR,
achieving
R²
values
0.96
CS
0.92
TS,
compared
MLR's
lower
0.85
0.81
TS.
equations
user-friendly
GUI
can
be
pre-mix
design
superplasticizers.
REVIEWS ON ADVANCED MATERIALS SCIENCE,
Journal Year:
2024,
Volume and Issue:
63(1)
Published: Jan. 1, 2024
Abstract
This
research
integrated
glass
powder
(GP),
marble
(MP),
and
silica
fume
(SF)
into
rubberized
mortar
to
evaluate
their
effectiveness
in
enhancing
compressive
strength
(
fc′
{f}_{\text{c}}^{^{\prime}
}
).
Rubberized
cubes
were
produced
by
replacing
fine
aggregates
with
shredded
rubber
varying
proportions.
The
decrease
mortar’s
was
controlled
substituting
cement
GP,
MP,
SF.
Although
many
literature
studies
have
evaluated
the
suitability
of
industrial
waste,
such
as
SF,
construction
material,
no
yet
included
combined
effect
these
wastes
on
mortar.
study
aims
provide
complete
insight
waste
By
cement,
SF
added
different
proportions
from
5
25%.
Furthermore,
artificial
intelligence
prediction
models
developed
using
experimental
data
assess
determined
that
optimal
substitution
levels
for
15,
10,
15%,
respectively.
Similarly,
partial
dependence
plot
analysis
suggests
GP
a
comparable
machine
learning
demonstrated
significant
resemblance
test
results.
Two
individual
techniques,
support
vector
random
forest,
generate
R
2
values
0.943
0.983,
