Construction
project
management
involves
proper
scheduling
and
estimation
of
resources
such
that
projects
meet
their
time
budgetary
allocations.
Traditional
methods
rely
on
manual
processes
or
fixed
rules
cannot
keep
up
with
the
dynamics
variability
conditions.
This
paper
proposes
a
data-driven
approach
incorporating
machine
learning
models,
Support
Vector
Machines,
Random
Forests
to
enhance
accuracy
in
resource
allocation.
Several
historical
data
points
need
be
analyzed
terms
task
duration,
usage,
cost
variables
frame
predictive
models.
In
this
direction,
SVM
is
applied
classify
risks
regarding
likely
delays
respect
weather
conditions,
labor
availability,
discrepancies
supply
chain.
Simultaneously,
are
utilized
predict
requirements
possible
fluctuations
costs.
The
framework
also
allows
for
real-time
integration
continuous
updates,
thereby
increasing
reliability
prediction.
A
case
study
based
achieved
reduction
about
18%
delays,
improvement
25%
over
traditional
approaches.
results
demonstrate
into
construction
through
practical
insights
enable
decision-makers
more
proactive
better
informed.
research
highlights
importance
leveraging
advanced
analytics
tools
address
high-level
challenges
within
industry.
Buildings,
Journal Year:
2022,
Volume and Issue:
12(2), P. 90 - 90
Published: Jan. 18, 2022
In
the
era
of
Fourth
Industrial
Revolution,
artificial
intelligence
(AI)
is
a
core
technology,
and
AI-based
applications
are
expanding
in
various
fields.
This
research
explored
influencing
factors
on
end-user’s
intentions
acceptance
technology
construction
companies
using
model
(TAM)
technology–organisation–environment
(TOE)
framework.
The
analysis
end-users’
for
accepting
was
verified
by
applying
structure
equation
model.
According
to
results,
technological
along
with
external
variables
an
individual’s
personality
had
positive
influence
(+)
perceived
usefulness
ease
use
end-users
technology.
Conversely,
environmental
such
as
suggestions
from
others
appeared
be
disruptive
users’
acceptance.
order
effectively
utilise
organisational
support,
culture,
participation
company
whole
were
indicated
important
implementation.
Smart and Sustainable Built Environment,
Journal Year:
2022,
Volume and Issue:
12(3), P. 461 - 487
Published: March 1, 2022
Purpose
The
purpose
of
this
paper
is
to
investigate
the
potential
integration
deep
learning
(DL)
and
digital
twins
(DT),
referred
as
(DDT),
facilitate
Construction
4.0
through
an
exploratory
analysis.
Design/methodology/approach
A
mixed
approach
involving
qualitative
quantitative
analysis
was
applied
collect
data
from
global
industry
experts
via
interviews,
focus
groups
a
questionnaire
survey,
with
emphasis
on
practicality
interoperability
DDT
decision-support
capabilities
for
process
optimization.
Findings
Based
results,
conceptual
model
framework
has
been
developed.
research
findings
validate
that
DL
integrated
DT
facilitating
will
incorporate
cognitive
abilities
detect
complex
unpredictable
actions
reasoning
about
dynamic
optimization
strategies
support
decision-making.
Practical
implications
establish
interoperable
functionality
develop
typologies
models
described
autonomous
real-time
interpretation
decision-making
building
systems
development
based
DT.
Originality/value
explores
how
technologies
work
collaboratively
integrate
different
environments
in
interplay
simulation
during
planning
construction.
step
next
level
automation
control
towards
be
implemented
phases
project
lifecycle
(design–planning–construction).
International Journal of Information Security and Privacy,
Journal Year:
2023,
Volume and Issue:
17(1), P. 1 - 25
Published: Feb. 3, 2023
IoT
devices
generate
enormous
amounts
of
data,
which
deep
learning
algorithms
can
learn
from
more
effectively
than
shallow
algorithms.
The
approach
for
threat
detection
may
ultimately
benefit
fog
computing
or
networking
(fogging).
authors
present
a
cutting-edge
distributed
DL
method
detecting
cyberattacks
and
vulnerability
injection
(CAVID)
in
this
paper.
In
terms
the
evaluation
metrics
tested
tests,
model
performs
better
SL
models.
They
demonstrated
DL-driven
CAVID
using
open-source
NSL-KDD
dataset.
A
pre-trained
SAE
was
utilised
feature
engineering,
whereas
Softmax
employed
categorization.
used
parametric
system
assessment
to
evaluate
comparison
techniques.
For
scalability,
accuracy
across
several
worker
nodes
taken
into
consideration.
addition
robustness,
effectiveness,
optimization
parallel
among
enhancing
accuracy,
findings
demonstrate
models
exceeding
classic
ML
architectures.
Buildings,
Journal Year:
2024,
Volume and Issue:
14(1), P. 220 - 220
Published: Jan. 14, 2024
In
the
last
decade,
despite
rapid
advancements
in
artificial
intelligence
(AI)
transforming
many
industry
practices,
construction
largely
lags
adoption.
Recently,
emergence
and
adoption
of
advanced
large
language
models
(LLMs)
like
OpenAI’s
GPT,
Google’s
PaLM,
Meta’s
Llama
have
shown
great
potential
sparked
considerable
global
interest.
However,
current
surge
lacks
a
study
investigating
opportunities
challenges
implementing
Generative
AI
(GenAI)
sector,
creating
critical
knowledge
gap
for
researchers
practitioners.
This
underlines
necessity
to
explore
prospects
complexities
GenAI
integration.
Bridging
this
is
fundamental
optimizing
GenAI’s
early
stage
within
sector.
Given
unprecedented
capabilities
generate
human-like
content
based
on
learning
from
existing
content,
we
reflect
two
guiding
questions:
What
will
future
bring
industry?
are
delves
into
reflected
perception
literature,
analyzes
using
programming-based
word
cloud
frequency
analysis,
integrates
authors’
opinions
answer
these
questions.
paper
recommends
conceptual
implementation
framework,
provides
practical
recommendations,
summarizes
research
questions,
builds
foundational
literature
foster
subsequent
expansion
its
allied
architecture
engineering
domains.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 31461 - 31487
Published: Jan. 1, 2024
Construction
sector
spending
makes
a
significant
contribution
to
the
global
economy,
with
approximately
$10
trillion
being
spent
on
building
and
construction
activities
annually.
However,
industry
has
traditionally
been
perceived
as
slow
adapt
new
technologies
compared
other
sectors.
Recently,
experienced
substantial
shift
towards
Digital
Transformation.
As
have
emerged,
begun
realize
importance
of
Transformation
in
pre-construction,
construction,
facility
management
phases.
A
high
degree
seen
regarding
site
monitoring,
wearables,
sensors,
identifying
hazards.
This
paper
intends
sketch
picture
digital
implemented
throughout
entire
project
lifecycle.
By
fully
analyzing
more
than
200
papers,
finds
that
various
aspects
industry,
including
technologies,
policies,
regulations,
infrastructures,
are
still
early
stages
The
findings
from
this
review
will
help
researchers
practitioners
understand
technology
implementation
where
stands
process.
also
serves
starting
point
for
future
work
industry.
research
is
limited
vertical
projects
does
not
include
horizontal
integration.
Finally,
study
give
guideline
successful
examples
which
used
specific
phases,
so
can
get
holistic
view
use
environment.
