Impact of Aggregate Characteristics on Frictional Performance of Asphalt-Based High Friction Surface Treatments
CivilEng,
Год журнала:
2025,
Номер
6(1), С. 4 - 4
Опубликована: Янв. 14, 2025
High
Friction
Surface
Treatments
(HFST)
are
recognized
for
their
effectiveness
in
enhancing
skid
resistance
and
reducing
road
accidents.
While
Epoxy-based
HFSTs
widely
applied,
they
present
limitations
such
as
compatibility
issues
with
existing
pavements,
high
installation
removal
costs,
durability
concerns
tied
to
substrate
quality.
As
an
alternative
traditional
HFSTs,
this
study
investigated
the
effects
of
aggregate
gradation
designated
by
agencies
on
performance
asphalt-based
HFST.
Various
types
were
assessed
evaluate
friction
impact
polishing
cycles
non-Epoxy
It
was
found
that
adjustments
size
may
be
necessary
when
transitioning
given
different
nature
asphalt
more
temperature
susceptible
compared
Epoxy.
binder
grades
considered
study.
A
series
tests,
including
British
Pendulum
Test
(BPT),
Dynamic
Tester
(DFT),
Circular
Track
Meter
(CTM),
Micro-Deval
(MD),
Aggregate
Imaging
Measurement
System
(AIMS),
conducted
measure
Coefficient
(COF),
Mean
Profile
Depth
(MPD),
texture,
angularity
before
after
cycles.
The
results
showed
COF
slabs
decreased
significantly
than
increased
HFST
medium
gradations.
However,
coarse
gradation,
using
matched
or
even
surpassed
Epoxy
polishing.
Notably,
PG88-16
Calcined
Bauxite
(CB)
had
smallest
reduction
140K
cycles,
only
a
19%
decrease
23%
Язык: Английский
Improving Aggregate Abrasion Resistance Prediction via Micro-Deval Test Using Ensemble Machine Learning Techniques
Engineering Journal,
Год журнала:
2024,
Номер
28(3), С. 15 - 24
Опубликована: Март 1, 2024
Aggregate
is
the
most
extracted
material
from
world's
mines
and
widely
used
in
civil
construction
projects.The
Micro-Deval
abrasion
test
(MD)
one
of
important
tests
that
provides
characteristics
crushed
aggregates
show
their
resistance
against
mechanical
abrasive
factors
such
as
repeated
impact
loading.The
various
on
properties
has
led
researchers
to
seek
correlations,
often
focusing
limited
data
samples,
leading
reduced
accuracy.This
study
employs
machine
learning
(ML)
methods
predict
MD
values,
considering
diverse
aggregate
properties.Various
ensemble
ML
were
applied,
revealing
exceptional
performance
stacking
model,
which
achieved
an
R
2
score
0.95
predicting
resistance.The
feature
importance
analysis
highlights
influence
Magnesium
Sulfate
Soundness
(MSS),
Water
Absorption
(ABS),
Los
Angeles
Abrasion
(LAA)
suggesting
use
multiple
could
yield
a
more
dependable
assessment
durability.
Язык: Английский
Evaluating Friction Characteristics of High Friction Surface Treatment Application Under Varied Polishing and Slippery Conditions
Transportation Research Record Journal of the Transportation Research Board,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 24, 2024
The
frictional
attributes
of
high
friction
surface
treatment
(HFST)
play
a
crucial
role
in
ensuring
optimal
traffic
safety,
particularly
wet
weather
conditions.
Friction
consists
two
important
components:
adhesion
and
hysteresis.
This
research
focuses
on
evaluating
these
essential
factors
HFST
using
different
aggregates
distinct
sizes
by
considering
various
abrasion
polishing
methods.
To
isolate
components
for
assessment,
testing
was
carried
out
under
slippery
conditions,
including
dry,
with
water,
water
+
soap.
inclusion
liquid
hand
soap
the
test
procedure
effectively
minimized
or
even
eliminated
component’s
influence,
making
it
possible
to
primarily
focus
hysteresis
component.
Consequently,
British
Pendulum
Number
(BPN)
measured
this
predominantly
reflected
hysteresis-related
friction.
analysis
variance
results
emphasized
substantial
impact
methods
BPN
values
obtained
Notably,
Micro-Deval
Abrasion
(MDA)
105,
180,
240
min
exhibited
most
pronounced
influence
variation
higher
F-value
MDA
105
indicated
that
specific
time
exerted
more
significant
than
other
factors.
Furthermore,
utilization
Aggregate
Image
Measurement
System
yielded
valuable
insights
into
micro-texture
aggregates.
It
revealed
calcined
bauxite
size
is
anticipated
provide
rougher
morphology
(texture)
pavement
compared
sources
study,
thereby
contributing
findings
from
study
contribute
deeper
understanding
characteristics
scenarios,
providing
optimizing
applications
enhance
road
safety
skid
resistance.
Язык: Английский
Influence of Aggregate Properties on Skid Resistance of Pavement Surface Treatments
Coatings,
Год журнала:
2024,
Номер
14(8), С. 1037 - 1037
Опубликована: Авг. 15, 2024
Skid
resistance
is
a
critical
aspect
for
traffic
safety
since
it
significantly
influences
vehicle
control
and
minimizes
the
distance
required
emergency
braking.
The
surface
characteristics
of
pavements
play
pivotal
role
in
determining
skid
resistance.
To
achieve
optimal
performance,
pavement
must
sustain
specific
level
friction.
Thus,
advantageous
to
apply
treatments
areas
that
require
enhanced
This
study
investigate
impact
factors
such
as
aggregate
source,
size,
morphological
properties,
abrasion
levels
on
frictional
high-friction
treatment
(HFST).
A
complete
investigation
was
conducted
HFST
samples
by
analyzing
morphology
using
Aggregate
Image
Measurement
System
performing
Micro-Deval
testing.
evaluated
with
British
Pendulum
Tester
(BPT).
findings
revealed
different
aggregates
sizes
exhibited
varying
behaviors
post-polishing.
Notably,
fine-sized
demonstrated
higher
pendulum
number
(BPN)
values,
which
indicate
superior
performance.
Models
predicted
numbers
based
average
texture
angularity
indices
initially
balanced
both
properties
before
polishing.
However,
after
polishing,
emerged
primary
determinant
resistance,
overshadowed
angularity’s
impact.
Язык: Английский
Comparative Analysis of Lab-Data-Driven Models for International Friction Index Prediction in High Friction Surface Treatment (HFST)
Applied Sciences,
Год журнала:
2025,
Номер
15(11), С. 6249 - 6249
Опубликована: Июнь 2, 2025
High
Friction
Surface
Treatments
(HFSTs)
are
often
utilized
as
a
spot
treatment
to
enhance
selected
areas
with
high
friction
demand
rather
than
extended
pavement
sections
and
helpful
in
increasing
skid
resistance
minimizing
road
accidents.
A
laboratory
design
approach
was
created
assess
the
fundamental
ideas
behind
international
index
(IFI)
concept
update
present
IFI
model
parameters
for
HFST
applications
based
on
test
findings
gain
better
understanding
of
performance.
Two
aggregate
types
three
sizes
were
tested
under
controlled
polishing
cycles.
texture
measured
using
Dynamic
Tester
(DFT)
Circular
Track
Meter
(CTM).
Three
physics-informed
empirical
models,
including
logarithmic,
power
law,
polynomial
represent
effects,
nonlinear
scaling,
complex
interactions
between
COF
MPD.
Results
show
that
performance
varies
type,
gradation,
polishing,
traditional
may
not
fully
capture
behavior.
Model
refinements
suggested
surface
characteristics
lowest
testing
Root
Mean
Squared
Error
(RMSE)
(0.049)
highest
predictive
accuracy
R2
(0.821);
logarithmic
found
be
best.
Sensitivity
analysis
revealed
predictions
more
sensitive
(ΔIFI:
14.3–17.7%)
MPD
1.5–6.0%)
across
all
models.
These
results
demonstrate
how
these
models
can
improve
assessment
while
providing
useful
information
enhancing
safety.
This
process
is
tool
evaluating
lab
setting
since
it
calculates
lab.
Язык: Английский
Cardiovascular Classification Using Efficient Net on Electrocardiogram Images
Engineering Journal,
Год журнала:
2024,
Номер
28(12), С. 67 - 78
Опубликована: Дек. 1, 2024
Cardiovascular
disease
ranks
among
the
top
causes
of
mortality,
frequently
caused
by
sudden
obstructions
within
blood
vessels.Timely
identification
and
intervention
are
essential
for
minimizing
impact
disease.This
research
employs
image
augmentation
techniques
to
correct
class
imbalance
in
an
ECG
dataset
divided
into
five
categories:
Normal,
Abnormal
Heartbeat,
Myocardial
Infarction,
Previous
History
COVID-19.The
balanced
includes
6,322
images.To
improve
classification
accuracy
cardiovascular
diseases,
three
pre-trained
models
visual
Geometric
Group,
Residual,
Dense,
Efficient
Network
with
Version
2,
were
trained
on
dataset.Critical
hyper
parameters
fine-tuned,
yielding
optimal
performance
a
learning
rate
set
at
0.00001,
dropout
0.3,
utilizing
Adam
optimizer.EfficientNet-V2
outperformed
other
models,
reaching
level
accuracies
96.22%,
precision
96.34%,
recall
96.31%,
95.89%,
94.75%,
F1-Score
96.33%,
thus
exceeding
Densenet
161,
201,
ResNet50
VGG16.
Язык: Английский