Impact and Failure Analysis of U-Shaped Concrete Containing Polyurethane Materials: Deep Learning and Digital Imaging Correlation-Based Approach
Saleh Ahmad Laqsum,
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Han Zhu,
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Sadi Ibrahim Haruna
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et al.
Polymers,
Journal Year:
2025,
Volume and Issue:
17(9), P. 1245 - 1245
Published: May 2, 2025
This
study
investigates
the
use
of
advanced
convolutional
neural
networks
(CNNs)
to
analyze
and
classify
fracture
behavior
U-shaped
concrete
modified
with
polyurethane
(PU)
under
repeated
drop-weight
impact
loads.
A
total
17
specimens
were
tested
multiple
loads
for
each
PU
binder
content
(0%,
10%,
20%,
30%)
by
weight
cement.
By
integrating
digital
image
correlation
(DIC)
dynamic
static
mechanical
testing,
this
research
evaluates
concrete’s
resistance
flexural
varying
content.
Three
CNN
architectures,
InceptionV3,
MobileNet,
DenseNet121,
trained
on
a
dataset
comprising
1655
high-resolution
crack
images
failure
stages
into
no
crack,
initial
failure.
Experimental
results
revealed
that
20%
optimally
enhances
strength,
while
properties
declined
significantly
30%
The
strain
localization
in
DIC
analysis
indicated
reduced
matrix
cohesion,
which
was
measured
extent
concentration
material,
highlighting
importance
maintaining
below
avoid
compromising
structural
integrity.
Among
models,
InceptionV3
demonstrated
superior
accuracy
(96.67%),
precision,
recall,
outperforming
MobileNet
(94.56%)
DenseNet121
(90.03%).
combination
deep
learning
offers
robust,
automated
framework
assessment,
improving
efficiency
over
traditional
methods
such
as
visual
inspections,
are
time-consuming
reliant
expert
judgment.
Language: Английский
Defects Identification and Crack Depth Determination in Porous Media on the Brick Masonry Example Using Ultrasonic Methods: Numerical Analysis and Machine Learning
Journal of Composites Science,
Journal Year:
2025,
Volume and Issue:
9(6), P. 267 - 267
Published: May 28, 2025
Automation
of
the
structural
health
monitoring
process
involves
use
successful
methods
for
detecting
defects
and
determining
their
critical
characteristics.
An
efficient
means
crack
detection
in
composite
materials
is
ultrasonic
method,
but
its
application
to
determine
parameters,
such
as
depth
construction
practice,
difficult
or
leads
large
errors.
This
article
focuses
on
machine
learning
usage
detect
cracks
like
brickwork.
Ceramic
bricks
with
various
mechanical
properties
pre-grown
from
2
60
mm
are
considered.
To
understand
processes
occurring
during
pulse
transmission,
modeling
was
performed
ANSYS
environment.
The
brick
considered
a
porous
medium
weakened
by
crack.
Numerical
allows
identification
main
features
signal
response
determination
amplitude-time
range
different
porosity
values.
Using
made
it
possible
solve
two
related
problems.
first,
binary
classification,
i.e.,
presence
absence
crack,
solved
100%
accuracy.
second
depth.
A
neural
network
built
using
an
ensemble
decision
trees.
accuracy
prediction
R2
=
0.983,
error
predicted
values
within
8%.
Language: Английский