Emerging Paradigms and Practices in Construction Equipment Management
Journal of Construction Engineering and Management,
Год журнала:
2025,
Номер
151(5)
Опубликована: Фев. 25, 2025
Язык: Английский
Ontology-Based Framework for Enhancing Safety in Earthmoving Operations: A Socio-Technical System Approach in the Industry 4.0 Era
Lecture notes in civil engineering,
Год журнала:
2025,
Номер
unknown, С. 1207 - 1216
Опубликована: Янв. 1, 2025
Язык: Английский
Machine Learning Based on Eye Movement Indicators to Detect Fatigue in Coal Mine Monitoring Dispatcher
Iranian Journal of Science and Technology Transactions of Electrical Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 26, 2025
Язык: Английский
Monitoring Mental Fatigue of Construction Equipment Operators: A Smart Cushion–Based Method with Deep Learning Algorithms
Journal of Management in Engineering,
Год журнала:
2024,
Номер
40(5)
Опубликована: Июль 10, 2024
Язык: Английский
Applying Systems Thinking to Research into Risk Factors Influencing Earthmoving Equipment Operation Safety in Construction Sites
Buildings,
Год журнала:
2024,
Номер
14(7), С. 1978 - 1978
Опубликована: Июнь 30, 2024
Earthmoving
operations
in
the
construction
process
are
complex
environments
that
involve
interactions
between
equipment,
workforce,
and
materials
within
an
overarching
plan.
Over
past
two
decades,
researchers
have
focused
on
improving
safety
of
earthmoving
equipment
due
to
their
omnipresence
environment.
Although
previous
studies
explored
risks
causes
accidents
involving
approaches
were
common
lacked
a
comprehensive
perspective.
Hence,
this
systematic
literature
review
applies
Rasmussen’s
(1997)
risk
management
framework
using
systems
thinking
approach
identify
classify
factors
influencing
operation
sites.
Utilizing
multistep
methodology,
research
first
identifies
38
pertinent
then
classifies
them
based
thinking.
Social
network
analysis
(SNA)
is
employed
analyze
data.
The
results
show
most
focuses
monitoring
sites,
but
very
little
government
regulatory
roles.
When
considering
interdependencies
factors,
training
important
factor,
followed
by
largely
overlooked
machinery
characteristics
manufacturer’s
performance.
inform
both
community
industry
practitioners
regarding
less-understood
aspects
future
directions.
Язык: Английский
FatigueSense: Multi-Device and Multi-Modal Wearable Sensing for Detecting Mental Fatigue
ACM Transactions on Computing for Healthcare,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 21, 2024
Mental
fatigue
is
a
crucial
aspect
that
has
gained
attention
across
various
disciplines
due
to
its
impact
on
overall
well-being.
While
previous
research
explored
the
use
of
wearable
devices
for
detecting
mental
fatigue,
limited
investigation
been
conducted
into
effectiveness
these
in
different
body
positions
or
multi-device
setups.
To
address
this,
our
study
utilizes
unique
public
dataset
containing
over
13
hours
sensor
data
collected
36
sessions,
with
four
(Earable,
Chestband,
Wristband,
and
Headband).
We
propose
several
machine
learning-based
approaches
assess
both
psychological
physiological
levels
multimodal
environment.
Specifically,
we
introduce
device
type-specific
(trained
tested
single
device)
multiple
devices)
inference
tasks.
Our
findings
show
models
perform
well,
AUC
scores
ranging
from
0.63
0.69
0.74
0.80
fatigue.
The
approach
shows
improved
performance
(AUC
0.74)
0.81
0.88).
Hence,
this
presents
in-depth
analysis
wearables,
demonstrating
potential
setups
are
prevalent
today’s
emerging
lifestyles.
Язык: Английский
Contactless Vital-Sign Monitoring of Construction Machinery Operators Using Millimeter-Wave Technology
Journal of Construction Engineering and Management,
Год журнала:
2024,
Номер
151(1)
Опубликована: Окт. 16, 2024
Язык: Английский
Human Operator Mental Fatigue Assessment Based on Video: ML-Driven Approach and Its Application to HFAVD Dataset
Applied Sciences,
Год журнала:
2024,
Номер
14(22), С. 10510 - 10510
Опубликована: Ноя. 14, 2024
The
detection
of
the
human
mental
fatigue
state
holds
immense
significance
due
to
its
direct
impact
on
work
efficiency,
specifically
in
system
operation
control.
Numerous
approaches
have
been
proposed
address
challenge
detection,
aiming
identify
signs
and
alert
individual.
This
paper
introduces
an
approach
assessment
based
application
machine
learning
techniques
video
a
working
operator.
For
validation
purposes,
was
applied
dataset,
“Human
Fatigue
Assessment
Based
Video
Data”
(HFAVD)
integrating
data
with
features
computed
by
using
our
computer
vision
deep
models.
incorporated
encompass
head
movements
represented
Euler
angles
(roll,
pitch,
yaw),
vital
(blood
pressure,
heart
rate,
oxygen
saturation,
respiratory
rate),
eye
mouth
states
(blinking
yawning).
integration
these
eliminates
need
for
manual
calculation
or
parameters,
it
obviates
requirement
sensors
external
devices,
which
are
commonly
employed
existing
datasets.
main
objective
is
advance
research
particularly
academic
settings.
this
reason,
we
conducted
series
experiments
utilizing
analyze
dataset
assess
predicted
results
reveal
that
random
forest
technique
consistently
achieved
highest
accuracy
F1-score
across
all
experiments,
predominantly
exceeding
90%.
These
findings
suggest
highly
promising
task
prove
strong
connection
association
among
used
annotate
videos
fatigue.
Язык: Английский
An advanced exploration of technological functionalities addressing risk factors in earthmoving equipment operation on construction sites: a systematic literature review
Smart and Sustainable Built Environment,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 24, 2024
Purpose
This
paper
aims
to
analyze
the
current
state
of
technological
advancements
research
in
addressing
diverse
risk
factors
involved
earthmoving
equipment
operations
through
Rasmussen's
(1997)
management
framework.
It
examines
how
existing
technologies
capture,
manage
and
disseminate
information
across
various
levels
safety
by
defining
their
core
functionalities.
The
highlights
gaps
solutions
regarding
flow
emphasizes
need
for
an
integrated
approach
enhance
holistic
capable
capturing
risks
different
management.
Design/methodology/approach
employs
a
multistep
approach.
Initially,
functionalities
were
identified
systematic
review
scholarly
works.
Subsequently,
social
network
analysis
(SNA)
Pareto
applied
evaluate
determine
importance
improving
them.
Findings
findings
highlight
multilevel
approaches
that
expand
address
all
combination
focuses
primarily
on
on-site
monitoring,
congested
work
sites,
site
layout/path
planning,
utility
problems,
training,
blind
spot
visibility.
Site
monitoring
warning
systems,
supported
sensors
computer
vision
(CV),
are
pivotal
identifying
enabling
data-driven
However,
workforce-level
cognitive
(W1-W6),
which
influence
behavior,
remain
underexplored
enhancing
functionality
anticipation
response
during
operation.
Prevention
is
function
solutions,
emphasizing
human
such
as
sources
hazards
operations.
Learning:
AI
IoT
systems
key
future
development,
when
grounded
ontology-based
knowledge
earthwork,
they
gain
structured
types,
interactions
earthwork
activities.
enhances
capabilities
these
capture
complex
between
hazard
(human
equipment),
supporting
comprehensive
Originality/value
elucidates
require
more
approach—grounded
understanding
technologies—to
effectively
Rasmussen
should
not
only
isolated
but
also
ensure
continuous
multiple
Язык: Английский