Sensors,
Journal Year:
2025,
Volume and Issue:
25(8), P. 2528 - 2528
Published: April 17, 2025
Vibration-based
bridge
modal
identification
is
a
crucial
tool
in
monitoring
and
managing
transportation
infrastructure.
Traditionally,
this
entails
deploying
fixed
array
of
sensors
to
measure
responses
such
as
accelerations,
determine
dynamic
characteristics,
subsequently
infer
conditions
that
will
facilitate
prognosis
decision-making.
However,
paradigm
not
scalable,
possesses
limited
spatial
resolution,
typically
high
effort
cost.
Recently,
mobile
sensing-based
paradigms
have
demonstrated
promise
laboratory
field
settings
an
alternative.
These
methods
can
leverage
big
data
from
crowdsourcing
vibration
acquired
smartphone
devices
belonging
pedestrians
passengers
traveling
over
bridge,
constituting
significantly
large
stream
indirectly
sensed
response.
Although
the
efficacy
has
been
for
set
case
studies,
ubiquitous
implementation
requires
analyzing
impact
vehicle
dynamics
quantifying
sources
be
used
purpose
identification.
This
paper
presents
road
map
achieving
through
dynamically
diverse
datastreams
passenger
cars,
buses,
bikes,
scooters.
Existing
point
towards
crowdsourced
sensing
urban
settings,
which
would
effective
decision-making
enhanced
infrastructure
resilience.
International Journal of Structural Stability and Dynamics,
Journal Year:
2024,
Volume and Issue:
24(22)
Published: Jan. 8, 2024
The
indirect
method
for
bridge
modal
identification
based
on
the
response
of
moving
vehicles
has
attracted
widespread
attention
in
recent
years.
However,
most
existing
studies
only
focus
simply
supported
bridge,
while
continuous
girder
bridges
are
widely
used
practical
engineering.
Therefore,
this
study
proposes
an
frequency
bridges.
First,
mode
shape
formula
equal-span
beam
is
deduced
using
three-moment
equations,
and
analytical
solution
vehicle
vibration
via
vehicle–bridge
coupled
theory.
Second,
derived
verified
through
numerical
analysis
results
field
test
results.
Finally,
effects
parameters
accuracy
analyzed.
show
that
a
reasonable
trailer
should
be
selected
to
avoid
resonance
between
better
performance.
research
findings
can
provide
reference
vehicles.
Sensors,
Journal Year:
2024,
Volume and Issue:
24(13), P. 4373 - 4373
Published: July 5, 2024
The
search
for
structural
and
microstructural
defects
using
simple
human
vision
is
associated
with
significant
errors
in
determining
voids,
large
pores,
violations
of
the
integrity
compactness
particle
packing
micro-
macrostructure
concrete.
Computer
methods,
particular
convolutional
neural
networks,
have
proven
to
be
reliable
tools
automatic
detection
during
visual
inspection
building
structures.
study’s
objective
create
compare
computer
algorithms
that
use
networks
identify
analyze
damaged
sections
concrete
samples
from
different
Networks
following
architectures
were
selected
operation:
U-Net,
LinkNet,
PSPNet.
analyzed
images
are
photos
obtained
by
laboratory
tests
assess
quality
terms
defection
structure.
During
implementation
process,
changes
metrics
such
as
macro-averaged
precision,
recall,
F1-score,
well
IoU
(Jaccard
coefficient)
accuracy,
monitored.
best
demonstrated
U-Net
model,
supplemented
cellular
automaton
algorithm:
precision
=
0.91,
recall
0.90,
F1
0.84,
accuracy
0.90.
developed
segmentation
universal
show
a
high
highlighting
areas
interest
under
any
shooting
conditions
volumes
defective
zones,
regardless
their
localization.
automatization
process
calculating
damage
area
recommendation
“critical/uncritical”
format
can
used
condition
various
types
structures,
adjust
formulation,
change
technological
parameters
production.
