Ingeniería y Competitividad,
Journal Year:
2023,
Volume and Issue:
25(Suplemento)
Published: Dec. 14, 2023
This
document
provides
a
contemporary
overview
of
wide
array
aspects
concerning
foam
concrete
and
its
inherent
properties.
review
covers
topics
such
as
the
use
alternative
binders,
influence
water/cement
ratio,
fine
aggregate
replacements
an
examination
mechanical
By
meticulously
scrutinizing
compressive
strength
data
from
multiple
authors,
this
exploration
not
only
highlights
current
state
knowledge
but
also
underscores
potential
for
future
investigations
in
realm
foamed
concrete.
Similarly,
realizes
limitations
that
unique
structure
imposes
on
diverse
applications
construction
engineering.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Jan. 14, 2025
To
study
the
enhancement
effect
of
carbon
nanotubes
(CNTs)
on
splitting
tensile
properties
foamed
concrete
backfill
in
which
cement
and
fly
ash
were
used
as
cementitious
materials
natural
sand
was
aggregate,
specimens
CNT-modified
prepared.
Brazilian
tests
to
investigate
strength
backfill,
digital
speckle
correlation
method
analyze
stress
field
characteristics
crack
expansion
law
during
testing.
The
stress–strain
energy
dissipation
laws
studied
at
various
static
loading
rates,
a
relationship
between
strength,
ultimate
strain,
rate
established.
results
showed
that
optimum
CNT
content
0.05%,
peak
strain
modified
increased
by
an
average
67.2%
21.7%,
respectively.
Moreover,
after
modification
with
CNTs,
less
likely
develop
concentration
areas
before
reaching
strength.
triangular
stable
loadbearing
structure
formed
caused
end
curve
exhibit
varying
degrees
"backlash".
For
correlated
logarithmically
rate,
while
power
function
rate.
At
low
dissipated
energy,
reverse
true
for
higher
rates.
Polymers,
Journal Year:
2025,
Volume and Issue:
17(7), P. 967 - 967
Published: April 2, 2025
Disposing
of
waste
tyres
in
landfills
poses
significant
environmental
hazards,
making
recycling
a
crucial
alternative.
Rubberised
concrete
has
been
found
to
exhibit
lower
density
and
better
thermal
insulation
performance
than
conventional
concrete.
In
order
maximise
the
potential
rubberised
concrete,
this
study
investigates
mechanical
properties
foamed
polypropylene
fibre
(FRPFC).
FRPFC
was
produced
using
mix
crumb
rubber
(CR)
granules,
fibres,
foam,
targeting
800
kg/m3,
with
CR
substituting
sand
at
varying
levels.
Compressive
strength,
flexural
splitting
tensile
conductivity
were
evaluated.
The
results
demonstrate
that
increasing
granule
content
enhances
compressive
strength
due
reduced
porosity
from
foam
usage.
For
instance,
improved
by
55%
(2.64
4.10
MPa)
as
increased
0%
80%.
Similarly,
(1.61
MPa
2.49
39%
(0.41
0.57
MPa),
respectively,
when
rose
100%
water-to-cement
ratio
0.50.
Furthermore,
decreased
34%
(0.3608
W/mK
0.2376
W/mK)
fully
replaced
showcasing
insulation.
Statistical
analysis
ANOVA
confirmed
significantly
influences
FRPFC,
higher
(80%
100%)
leading
superior
performance.
These
findings
highlight
FRPFC's
an
environmentally
sustainable
thermally
efficient
construction
material,
contributing
enhanced
compared
Civil Engineering Journal,
Journal Year:
2024,
Volume and Issue:
10(1), P. 249 - 264
Published: Jan. 1, 2024
This
study
explores
how
dynamic
characteristics
of
concrete,
such
as
shear
modulus,
modulus
elasticity,
and
Poisson's
ratio,
affect
stability
performance
in
civil
engineering
applications.
Traditional
testing
procedures,
which
include
the
time-consuming
costly
process
mixing
casting
specimens,
are
both
costly.
The
primary
objective
this
research
is
to
improve
efficiency
by
using
Artificial
Neural
Networks
(ANNs)
regression
analysis
predict
properties
providing
a
machine-learning-based
alternative
traditional
experimental
methodologies.
A
set
72
concrete
specimens
was
methodically
built
evaluated,
with
compressive
strengths
50
MPa,
aspect
ratios
ranging
from
1
2.5,
an
average
density
2400
kg/m3.
An
input
dataset
ANN
targets
were
these
samples.
model,
used
cutting-edge
deep
learning
techniques,
went
through
extensive
training,
validation,
testing,
well
statistical
analysis.
comparison
shows
that
predicted
elasticity
approaches
nearly
match
values,
maximum
error
5%.
Despite
good
forecasts
for
errors
up
20%
detected
on
occasion,
attributed
sample
shape
variations.
Doi:
10.28991/CEJ-2024-010-01-016
Full
Text:
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