Smart and Sustainable Built Environment,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 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
Chinese Journal of Mechanical Engineering,
Journal Year:
2024,
Volume and Issue:
37(1)
Published: Feb. 22, 2024
Abstract
The
human
digital
twin
(HDT)
emerges
as
a
promising
human-centric
technology
in
Industry
5.0,
but
challenges
remain
modeling
and
simulation.
Digital
(DHM)
provides
solutions
for
simulating
physical
cognitive
aspects
to
support
ergonomic
analysis.
However,
it
has
limitations
real-time
data
usage,
personalized
services,
timely
interaction.
emerging
HDT
concept
offers
new
possibilities
by
integrating
multi-source
artificial
intelligence
continuous
monitoring
assessment.
Hence,
this
paper
reviews
the
evolution
from
DHM
proposes
unified
framework
factors
perspective.
comprises
twin,
virtual
linkage
between
these
two.
integrates
AI
engines
enable
model-data-hybrid-enabled
can
potentially
upgrade
traditional
methods
intelligent
services
through
analysis,
feedback,
bidirectional
interactions.
Finally,
future
perspectives
of
industrial
applications
well
technical
social
are
discussed.
In
general,
study
outlines
perspective
on
first
time,
which
is
useful
cross-disciplinary
research
innovation
enhance
development
industry.
Engineering Construction & Architectural Management,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 14, 2025
Purpose
Construction
projects
are
characterized
by
large
construction
scale,
long
period,
high
uncertainty,
etc.
and
a
amount
of
data
is
generated
in
the
process.
Through
an
in-depth
exploration
value,
value
addition
can
be
achieved
based
on
co-creation.
The
purpose
this
study
to
analyze
laws
strategic
choices
participating
subjects
process
co-creation
factors
that
influence
stability
system.
Design/methodology/approach
Based
prospect
theory
evolutionary
game
theory,
constructs
model
among
owner,
constructor
designer
analyzes
dynamic
evolution
law
behavior
key
elements
system
stability.
Findings
shows
effort
cost,
profit
sensitivity,
participation
willingness
owner’s
punishment
for
non-participating
have
significant
impact
co-creation;
adjusting
punishment,
reducing
cost
improving
subject’s
benefit
perception
effectively
promote
system;
meanwhile,
subjects’
complementarity
interdependence.
Originality/value
research
results
reveal
provide
support
optimization
practice
mechanism.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: May 6, 2025
Cognitive
fatigue
is
a
psychological
condition
characterized
by
opinions
of
and
weakened
cognitive
functioning
owing
to
constant
stress.
critical
that
can
significantly
impair
attention
performance,
among
other
abilities.
Monitoring
this
in
real-world
settings
crucial
for
detecting
managing
adequate
break
periods.
Bridging
research
gap
significant,
as
it
has
substantial
implications
developing
more
effectual
less
intrusive
wearable
devices
track
fatigue.
Many
models
consider
intricate
biosignals,
like
electrooculogram
(EOG),
electroencephalogram
(EEG),
or
detection
basic
heart
rate
inconstancy
parameters.
Artificial
Intelligence
(AI)-driven
methods
aid
handling
categorizing
these
recognizing
fatigue-related
patterns
with
higher
accuracy.
This
technique
essential
high-demand
surroundings
such
education,
healthcare,
workplaces
where
may
affect
decision-making
performance.
Therefore,
the
study
presents
an
Exploratory
Analysis
Longitudinal
Fatigue
Detection
Using
Neurophysiological
Based
Biosignal
Data
(EALAI-CFDNBD)
approach.
The
main
aim
EALAI-CFDNBD
model
detect
using
neurophysiological-based
biosignal
data.
Primarily,
utilized
linear
scaling
normalization
(LSN)
ensure
input
features
were
appropriately
scaled
subsequent
analysis.
Furthermore,
binary
olympiad
optimization
algorithm
(BOOA)-based
feature
selection
extract
most
informative
features,
reducing
data
dimensionality.
graph
convolutional
autoencoder
(GCA)
classifier
employed
classify
detection.
Finally,
multi-objective
hippopotamus
(MOHO)
method
parameter
tuning,
optimizing
model's
hyperparameters
enhance
overall
An
extensive
range
simulations
accomplished
MEFAR
dataset
establish
good
classification
outcome
method.
experimental
validation
portrayed
superior
accuracy
value
97.59%
over
recent
methods.
