REVIEWS ON ADVANCED MATERIALS SCIENCE,
Год журнала:
2024,
Номер
63(1)
Опубликована: Янв. 1, 2024
Abstract
The
degradation
of
concrete
structures
is
significantly
influenced
by
water
penetration
since
serves
as
the
primary
vehicle
for
movement
harmful
compounds.
process
capillary
absorption
widely
recognized
a
crucial
indicator
durability
unsaturated
concrete,
it
allows
dangerous
substances
to
enter
composite
material.
capacity
intricately
linked
its
pore
structure,
inherently
porous.
main
goal
this
work
create
an
innovative
predictive
tool
that
assesses
porosity
analyzing
components
using
machine-learning
(ML)
framework.
Seven
distinct
batch
design
variables
were
included
in
generated
database:
fly
ash,
superplasticizer,
water-to-binder
ratio,
curing
time,
ground
granulated
blast
furnace
slag,
binder,
and
coarse-to-fine
aggregate
ratio.
Four
distant
ML
algorithms,
including
AdaBoost,
linear
regression
(LR),
decision
tree
(DT),
support
vector
machine
(SVM),
are
utilized
infer
generalization
capabilities
algorithms
estimate
accurately.
RReliefF
algorithm
was
implemented
calculate
significant
features
influencing
porosity.
This
study
concludes
comparison
alternative
techniques,
AdaBoost
method
demonstrated
superior
performance
with
R
2
score
0.914,
followed
SVM
(0.870),
DT
(0.838),
LR
(0.763).
results
evaluation
indicated
binder
possesses
remarkable
influence
on
concrete.
Buildings,
Год журнала:
2024,
Номер
14(1), С. 225 - 225
Опубликована: Янв. 14, 2024
The
present
study
utilized
machine
learning
(ML)
techniques
to
investigate
the
effects
of
eggshell
powder
(ESP)
and
recycled
glass
(RGP)
on
cement
composites
subjected
an
acidic
setting.
A
dataset
acquired
from
published
literature
was
employed
develop
learning-based
predictive
models
for
mortar’s
compressive
strength
(CS)
decrease.
Artificial
neural
network
(ANN),
K-nearest
neighbor
(KNN),
linear
regression
(LR)
were
chosen
modeling.
Also,
RreliefF
analysis
performed
relevance
variables.
total
234
data
points
train/test
ML
algorithms.
Cement,
sand,
water,
silica
fume,
superplasticizer,
powder,
90
days
CS
considered
as
input
outcomes
research
showed
that
could
be
applied
evaluate
reduction
percentage
in
composites,
including
ESP
RGP,
after
being
exposed
acid.
Based
R2
values
(0.87
ANN,
0.81
KNN,
0.78
LR),
well
assessment
variation
between
test
anticipated
errors
(1.32%
1.57%
1.69%
it
determined
accuracy
ANN
model
superior
KNN
LR.
sieve
diagram
exhibited
a
correlation
amongst
predicted
target
results.
suggested
RGP
significantly
influenced
loss
samples
with
scores
0.26
0.21,
respectively.
research,
approach
suitable
predicting
mortar
environments,
thereby
eliminating
lab
testing
trails.
Construction and Building Materials,
Год журнала:
2024,
Номер
412, С. 134879 - 134879
Опубликована: Янв. 1, 2024
Textile
fibre-reinforced
concrete
based
reviews
have
explored
various
engineering
properties,
such
as
strengthening
of
concrete,
enhancing
strain
capacity,
crack
control,
durability,
and
energy
absorption.
An
essential
missing
component
is
a
comprehensive
analysis
the
thermal
acoustic
insulation
performance
textile
concrete.
The
paper
provides
large-scale
analytical
database
by
analysing
prior
literature
on
It
further
microstructural
pore-structural
aspects
to
provide
an
overview
underlying
mechanisms
driving
these
properties.
This
review
explores
impact
fibre
inclusion
from
0–20
mass
percentage
(wt%)
0–40
volume
(v%).
key
findings
are
that
jute
mortar
demonstrated
superior
conductivity,
achieving
0.068
W/mK
at
20
wt%
inclusion,
followed
0.08
basalt
fibres
v%
demonstrating
possess
commendable
qualities.
Notably,
30
2–4
mm
miscanthus
in
showed
outstanding
dual
performance,
optimal
conductivity
0.09
90%
absorption
841
Hz.
Finally,
study
suggests
directions
address
identified
gaps
can
be
utilised
design
future
research
focusing
end-user
applications.
Case Studies in Construction Materials,
Год журнала:
2024,
Номер
20, С. e03083 - e03083
Опубликована: Март 28, 2024
Sustainable
development
in
the
building
industry
can
be
achieved
through
use
of
versatile
cementitious
composites.
Thus,
incorporating
nanoparticles
into
cement
composites
create
materials
with
enhanced
performance
and
numerous
applications.
The
utilization
carbon
nanotubes
(CNTs)
construction
has
great
promise
for
developing
efficient
solutions
to
establish
a
sustainable
ecosystem
diverse
characteristics.
However,
forecasting
characteristics
these
is
significant
challenge
due
their
intricate
composite
structure
nonlinear
behavior.
Designing
conducting
laboratory
experiments
on
samples
across
multiple
age
groups
challenging,
time-consuming,
costly.
Moreover,
there
presently
lack
model
that
predict
concrete's
compressive
strength
(fc')
nanoparticles.
Three
machine
learning
(ML)
techniques,
K-nearest
neighbor
(KNN),
linear
regression
(LR),
artificial
neural
network
(ANN),
were
used
fc'
nanocomposites
containing
CNTs
this
research.
A
thorough
database
consisting
282
data
entities
CNTs-based
concrete
model's
reliability
was
assessed
using
R2
test
statistical
error
analysis.
ANN
had
most
outstanding
value
0.885,
while
KNN
LR
models
values
0.838
0.744,
respectively.
RReliefF
analysis
utilized
evaluate
primary
components
predicting
outcomes.
This
research
shows
properties
CNT-based
are
greatly
affected
by
water-to-binder
ratio,
followed
proportions
coarse
aggregates.
ML
algorithms
exhibited
superior
generalization
capabilities,
suggesting
potential
accurate
predictions
properties.
REVIEWS ON ADVANCED MATERIALS SCIENCE,
Год журнала:
2025,
Номер
64(1)
Опубликована: Янв. 1, 2025
Abstract
Expanding
the
world’s
infrastructure
drives
up
demand
for
building
materials,
particularly
ordinary
Portland
cement
(OPC)
concrete,
whose
high
carbon
dioxide
(CO
2
)
emissions
have
a
detrimental
effect
on
environment.
To
address
this
issue,
researchers
looked
into
employing
alternative
supplementary
cementitious
materials
(SCMs),
including
metakaolin
(MK),
which
is
derived
from
calcined
kaolin
clay
with
pozzolanic
properties,
to
partially
or
completely
replace
OPC
in
concrete.
This
review
article
examines
MK’s
application
alkali-activated
(AAMs)
and
OPC-based
By
interacting
calcium
hydroxide,
MK
functions
as
additive
enhancing
its
mechanical
qualities
durability.
The
use
of
source
material
AAMs,
newly
developed
class
sustainable
binders,
also
covered
article.
effects
different
combinations
additional
SCMs,
fly
ash
(FA),
ground
granulated
blast
furnace
slag
(GGBFS),
silica
fume,
rice
husk
ash,
characteristics
concrete
both
fresh
hardened
states,
are
compiled.
majority
articles
considered
study
past
decade,
while
some
relevant
2014
earlier
taken
account.
results
showed
that
adding
combination
FA
GGBFS
has
excellent
synergistic
microstructural
development,
activity,
strength
increases.
In
particular,
MK–FA
mix
demonstrated
most
encouraging
performance
gains.
Because
large
surface
area,
nano-MK
helped
achieve
denser
geopolymer
structure
improve
properties.
best
curing
temperatures
MK-based
geopolymers
gain
were
found
be
between
40
80°C
total
28
days.
pointed
out
compressive
geopolymerization
process
enhanced
by
increasing
mass
ratio
Na
SiO
3
NaOH
concentration.
Nevertheless,
was
hampered
unnecessarily
alkali
concentrations.
Moreover,
increased
replacing
TiO
GGBFS.
combining
other
SCMs
highlight
potential
solutions
lowering
environmental
footprint
buildings.
