Applied Sciences,
Journal Year:
2023,
Volume and Issue:
13(16), P. 9388 - 9388
Published: Aug. 18, 2023
Infrastructure
that
was
constructed
during
the
high
economic
growth
period
of
Japan
is
starting
to
deteriorate;
thus,
there
a
need
for
maintenance
and
management
these
structures.
The
basis
inspection
process,
which
involves
finding
recording
damage.
However,
in
headrace
tunnels,
water
supply
interrupted
inspection;
it
desirable
comprehensively
photograph
record
tunnel
wall
detect
damage
using
captured
images
significantly
reduce
interruption
time.
Given
this
background,
aim
study
establish
an
investigation
assessment
system
deformation
points
inner
walls
tunnels
perform
efficient
tunnels.
First,
we
develop
mobile
photography
device
photographs
with
charge-coupled
line
camera.
Next,
method
YOLOv7
detecting
chalk
marks
at
locations
made
cleaning
were
photographed
by
imaging
system,
results
are
used
as
automatically
accumulates
plots
distributions.
For
chalking
detection
continuous
surface
images,
accuracy
99.02%
achieved.
Furthermore,
can
evaluate
total
number
distribution
deteriorated
areas,
be
identify
causes
change
over
time
occurrence
deterioration
phenomena.
developed
duration
cost
inspections
surveys,
select
priority
repair
areas
predict
through
data
accumulation,
contributing
appropriate
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2023,
Volume and Issue:
39(6), P. 911 - 928
Published: Oct. 12, 2023
Abstract
During
the
regular
service
life
of
high‐speed
railway
(HSR),
there
might
be
serious
defects
in
concrete
slabs
infrastructure
systems,
which
may
further
significantly
affect
public
transportation
safety.
To
address
these
issues
and
fulfill
functions
HSR,
traditional
methods
for
engineers
involve
carrying
out
on‐site
inspections
manually
or
by
semi‐automatic
inspection
vehicles,
conducting
timely
corresponding
repairing
approaches
maintenance,
where
are
time‐consuming
dangerous.
In
recent
years,
machine
learning
have
been
widely
applied
to
intelligent
automatic
detection
severe
HSR.
Currently,
one
most
problems
is
lack
sufficient
high‐quality
data
model
training,
resulting
low
recognition
accuracy
HSR
defects.
solve
this
problem,
paper
proposed
an
based
on
a
few‐shot
model,
that
is,
artificial
intelligence
limited
size,
recognizes
three
conditions
HSR:
cracks,
track
board
gaps,
unbroken
state.
Lightweight
models
specifically
designed
were
proposed.
Experiments
conducted
compare
performances
different
lightweight‐designed
models,
including
accuracy,
parameter
quantity,
testing
time.
Results
showed
optimum
can
fast
satisfactorily
recognize
with
very
size
10
samples
each
training
category,
satisfactory
73.9%
test
dataset
20
amounts
2.8
million,
time
2.2
s
per
image.
This
study
provides
reference
insufficient
samples.
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2023,
Volume and Issue:
39(4), P. 575 - 594
Published: Oct. 17, 2023
Abstract
High‐resolution
(HR)
crack
images
offer
more
detailed
information
for
assessing
structural
conditions
compared
to
low‐resolution
(LR)
images.
This
wealth
of
detail
proves
indispensable
in
bolstering
the
safety
unmanned
aerial
vehicle
(UAV)‐based
inspection
procedures
and
elevating
precision
small
segmentation.
Nonetheless,
achieving
a
balance
between
segmentation
accuracy
GPU
memory
consumption
poses
substantial
challenge
deep
learning
models
when
processing
HR
To
overcome
this
challenge,
novel
“HR
framework”
(HRCSF)
is
proposed,
specifically
designed
meticulously
segment
with
resolutions
exceeding
4K.
First,
multiscale
feature
extraction
network
(MsCFEN)
was
proposed
embedment
strip
pooling
operation
enhance
representation
transverse
longitudinal
pixels
from
complex
backgrounds.
Subsequently,
two
cascaded
operations
were
tailored
MsCFEN,
enabling
comprehensive
refinement
process
that
incorporates
both
global
local
aspects.
Furthermore,
fully
leverage
potential
each
component
process,
complete
architecture
trained
using
loss
function
embedded
boundary
optimization.
Conclusively,
UAV‐based
case
study
conducted
on
real
bridge
Changsha,
demonstrating
HRCSF's
practicability
segmenting
The
implementation
HRCSF
allows
UAV
perform
effectively
distance
3
m
away
girder,
resulting
significant
50%
reduction
time
LR
methods
while
maintaining
high
detection
accuracy.
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2024,
Volume and Issue:
39(17), P. 2642 - 2661
Published: July 29, 2024
Abstract
This
study
proposes
a
novel
self‐training
framework
for
unsupervised
domain
adaptation
in
the
segmentation
of
concrete
wall
cracks
using
accumulated
crack
data.
The
proposed
method
incorporates
Bayesian
neural
networks
uncertainty
estimation
pseudo‐labels,
and
spatial
priors
screening
noisy
labels.
Experiments
demonstrate
that
approach
achieves
significant
improvements
F1
score.
Comparing
scores,
DeepLabv3+
U‐Net
showed
performance
0.0588
0.1501,
respectively,
after
adaptation.
Furthermore,
integration
Stable
Diffusion
few‐shot
image
generation
enhances
by
0.0332.
enables
high‐precision
with
as
few
100
target
images,
which
can
be
easily
obtained
at
site,
reducing
cost
model
deployment
infrastructure
maintenance.
also
investigates
optimal
number
iterations
based
on
score,
providing
insights
practical
implementation.
contributes
to
development
efficient
automated
structural
health
monitoring
AI.
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 7, 2025
Abstract
Three‐dimensional
(3D)
buried
object
detection
using
ground
penetrating
radar
(GPR)
benefits
from
the
powerful
capacity
of
image‐wise
deep
neural
networks.
However,
it
still
faces
challenge
information
loss
raw
GPR
signals
to
two‐
and
three‐dimensional
images,
such
as
frequency‐domain
when
normalizing
into
gray‐scale
images
spatial
stacked
B‐
C‐scan
replace
inputs.
To
solve
challenge,
this
study
has
proposed
an
ENNreg‐transformer
model,
directly
3D
perform
detection.
In
are
first
converted
sequential
voxelization
obtain
spatiotemporal
features.
The
features
then
aggregated
by
intuition‐guided
feature
aggregation
layer
simulate
expert
behavior
analyze
data.
Finally,
evidential
header
outputs
interval‐based
bounding
boxes
for
experiment
on
two
road
datasets
demonstrates
that
model
exceeds
other
state‐of‐the‐art
models
tasks
thanks
aggregation.
addition,
box
represents
bounding‐box
uncertainty,
which
derives
inherent
limitations
International Journal of Neural Systems,
Journal Year:
2024,
Volume and Issue:
34(11)
Published: July 5, 2024
Typically,
deep
learning
models
for
image
segmentation
tasks
are
trained
using
large
datasets
of
images
annotated
at
the
pixel
level,
which
can
be
expensive
and
highly
time-consuming.
A
way
to
reduce
amount
required
training
is
adopt
a
semi-supervised
approach.
In
this
regard,
generative
models,
concretely
Generative
Adversarial
Networks
(GANs),
have
been
adapted
tasks.
This
work
proposes
MaskGDM,
architecture
combining
some
ideas
from
EditGAN,
GAN
that
jointly
their
segmentations,
together
with
diffusion
model.
With
careful
integration,
we
find
model
improve
EditGAN
performance
results
in
multiple
datasets,
both
multi-class
binary
labels.
According
quantitative
obtained,
proposed
improves
when
compared
DatasetGAN
respectively,
by
[Formula:
see
text]
text].
Moreover,
ISIC
dataset,
our
proposal
other
up
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2024,
Volume and Issue:
39(22), P. 3412 - 3434
Published: May 20, 2024
Abstract
Advancements
in
infrastructure
management
have
significantly
benefited
from
automatic
pavement
crack
detection
systems,
relying
on
image
processing
enhanced
by
high‐resolution
imaging
and
machine
learning.
However,
motion
blur
substantially
challenge
the
accuracy
of
analysis.
Nevertheless,
research
mitigating
remains
sparse.
This
study
introduces
an
effective
system
adept
at
deblurring
segmentation,
employing
a
generative
adversarial
network
(GAN)
with
UNet
as
generator
Wasserstein
GAN
Gradient
Penalty
(WGAN‐gp)
loss
function.
approach
performs
exceptionally
images
improves
segmentation
accuracy.
Models
were
trained
sharp
artificially
blurred
images,
WGAN‐gp
surpassing
other
functions
effectiveness.
innovatively
suggests
assessing
quality
through
addition
to
peak
signal‐to‐noise
ratio
(PSNR)
structural
similarity
(SSIM),
revealing
that
PSNR
SSIM
may
not
fully
capture
effectiveness
for
images.
An
extensive
evaluation
various
generators,
including
UNet,
lightweight
TransUNet,
DeblurGAN,
DeblurGAN‐v2,
MIMO‐UNet,
identifies
superior
performance
simulated
blur.
Validation
actual
motion‐blurred
confirms
proposed
model.
These
findings
demonstrate
GAN‐based
models
great
potential
overcoming
challenges
marking
notable
advancement
field.
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2024,
Volume and Issue:
39(15), P. 2350 - 2366
Published: March 31, 2024
Abstract
Selecting
or
generating
ground
motions
(GMs)
that
elicit
seismic
responses
matching
specific
standards
expected
benchmarks
for
nonlinear
time‐history
analysis
(NLTHA)
is
crucial
ensuring
the
rationality
of
structural
design
and
analysis.
Typical
GM
inputs
NLTHA,
either
natural
artificial,
are
normally
spectrum‐compatible,
which
may
produce
significant
variations
in
results,
even
using
multiple
GMs.
This
paper
introduces
a
response‐compatible
motion
generation
(RCGMG)
method
GMs
tailored
to
be
response‐compatible.
NLTHA
results
only
few
these
artificial
can
closely
approximate
mean
from
large
set
spectrum‐compatible
target
responses.
The
RCGMG
adopts
response
diagram
time
domain
(RDTD)
characterize
nonstationary
features
their
impacts
on
dynamic
A
physics‐guided
conditional
generative
adversarial
network
developed
RDTDs
with
These
generated
then
mapped
into
through
feedforward
neural
network.
To
verify
effectiveness
RCGMG,
different
structure
models
under
various
site
conditions
spectra
conducted.
Seismic
RCGMG‐generated
compared
demonstrate
closer
responses,
fewer
robust
generalization
performance.
Journal of Wind Engineering and Industrial Aerodynamics,
Journal Year:
2024,
Volume and Issue:
247, P. 105698 - 105698
Published: March 18, 2024
An
analytical
solution
for
the
gyroscopic
effect
of
spinning
rotor-blades
assembly
on
dynamic
response
offshore
wind
turbines
(OWT)
is
presented.
A
continuous
coupled
model
rigorously
developed
to
form
partial
differential
equations
fore-aft,
side-side,
and
yaw
motions.
The
moments
caused
by
angular
momentum
are
formulated
handled
into
three
boundary
conditions
at
nacelle.
procedure
obtaining
operational
natural
frequencies
structure
including
these
developed.
Furthermore,
a
function
each
fore-aft
motions
obtained
solving
motion
under
wave
load
applied
in
only
direction.
Finally,
calculated
compared
idling
ones
considered
example
OWT.
different
values
assembly's
velocity
investigated.
proposed
this
study
unfolds
revelational
capturing
turbine
industries
which
also
can
be
guideline
developing
floating
OWT
models.
Computer-Aided Civil and Infrastructure Engineering,
Journal Year:
2023,
Volume and Issue:
39(10), P. 1431 - 1451
Published: Nov. 6, 2023
Abstract
Bridge
inspection
ensures
that
in‐service
bridges
are
managed
and
maintained
in
conformity.
To
enhance
the
accuracy
efficiency
of
bridge
inspection,
an
automatic
hierarchical
model
is
proposed,
which
enables
classification
correlation
surface
images
at
three
levels,
namely,
structure,
component,
defect
type
level.
Thus,
impact
both
types
affected
components
on
safety
can
be
simultaneously
considered.
The
proposed
uses
a
group
sub‐models
instead
common
flat
network
to
realize
multiple
tasks,
advantageous
accuracy,
training
simplicity,
scalability.
levels
has
reached
96%,
92%,
81%.
Results
demonstrate
effectiveness
method
multi‐scale
targets.
This
study
may
provide
new
strategy
for
developing
systematic
easily
adaptable
detection
framework
practical
engineering.