Optimizing Facilities Management Through Artificial Intelligence and Digital Twin Technology in Mega-Facilities
Sustainability,
Год журнала:
2025,
Номер
17(5), С. 1826 - 1826
Опубликована: Фев. 21, 2025
Mega-facility
management
has
long
been
inefficient
due
to
manual,
reactive
approaches.
Current
facility
systems
face
challenges
such
as
fragmented
data
integration,
limited
predictive
systems,
use
of
traditional
methods,
and
lack
knowledge
new
technologies,
Building
Information
Modeling
Artificial
Intelligence.
This
study
examines
the
transformative
integration
Intelligence
Digital
Twin
technologies
into
(BIM)
frameworks
using
IoT
sensors
for
real-time
collection
analytics.
Unlike
previous
research,
this
uses
case
studies
simulation
models
dynamic
scenario-based
analyses.
Key
findings
show
a
significant
reduction
in
maintenance
costs
(25%)
energy
consumption
(20%),
well
increased
asset
utilization
operational
efficiency.
With
an
F1-score
more
than
90%,
system
shows
excellent
accuracy
equipment
failures
forecasting.
Practical
applications
hospitals
airports
demonstrate
developed
ability
platform
integrate
Internet
Things
shifting
facilities
from
being
proactive.
paper
presents
demo
that
integrates
BIM
with
Twins
improve
HVAC
equipment,
security
etc.,
by
recording
different
assets,
which
helps
streamline
management,
enhance
efficiency,
support
decision-making
buildings’
critical
systems.
Язык: Английский
From blueprint to reality: how digital twins are shaping the architecture, engineering, and construction landscape
Journal of Innovative Engineering and Natural Science,
Год журнала:
2025,
Номер
5(1), С. 399 - 435
Опубликована: Янв. 30, 2025
Digital
Twin
(DT)
technologies
are
reshaping
the
Architecture,
Engineering,
and
Construction
(AEC)
industry
by
bridging
physical
digital
domains
to
enable
real-time
data
integration,
advanced
simulations,
predictive
analytics.
This
study
systematically
investigates
role
of
DT
in
addressing
persistent
challenges
such
as
inefficiencies,
cost
overruns,
sustainability
goals.
Through
a
detailed
literature
review
95
publications
spanning
2019
2024,
research
identifies
key
contributions,
barriers,
gaps
applications
across
lifecycle
phases
scales,
ranging
from
individual
buildings
urban
infrastructure.
The
findings
emphasize
DT's
transformative
potential
enhancing
operational
efficiency,
maintenance,
energy
optimization,
sustainability.
A
comprehensive
framework
is
proposed
guide
integration
DTs,
technical,
economic,
knowledge-based
while
highlighting
opportunities
leverage
complementary
IoT,
BIM,
AI,
blockchain.
concludes
with
actionable
recommendations
for
advancing
adoption
AEC
industry,
paving
way
smarter,
more
sustainable
built
environments.
Язык: Английский
Synergistic integration of digital twins and zero energy buildings for climate change mitigation in sustainable smart cities: A systematic review and novel framework
Energy and Buildings,
Год журнала:
2025,
Номер
unknown, С. 115484 - 115484
Опубликована: Фев. 1, 2025
Язык: Английский
A dialectical system framework for building occupant energy behavior
Energy and Buildings,
Год журнала:
2025,
Номер
unknown, С. 115649 - 115649
Опубликована: Март 1, 2025
Язык: Английский
The role of metaverse technologies in energy systems towards sustainable development goals
Energy Reports,
Год журнала:
2025,
Номер
13, С. 4459 - 4476
Опубликована: Апрель 14, 2025
Язык: Английский
Digital Twin in the Design and Dynamic Assessment of Energy Performance of Multi-Family Buildings
Energies,
Год журнала:
2024,
Номер
17(23), С. 6150 - 6150
Опубликована: Дек. 6, 2024
The
article
explores
the
potential
of
Digital
Twin
(DT)
technology
in
design
and
dynamic
assessment
energy
performance
multi-family
buildings.
Traditional
approaches
to
building
provide
static
data
that
do
not
account
for
changing
operational
conditions
lack
continuous
consumption-monitoring
capabilities.
use
enables
monitoring
analyzing
building’s
parameters
at
every
stage
its
life
cycle.
presents
application
DT
assessing
conceptual
early
phases
design.
These
must
meet
legal
requirements.
Validation
conducted
on
four
buildings
demonstrated
high
accuracy,
with
average
difference
between
predicted
actual
(EP)
values
below
3.5%.
Thanks
model,
it
is
possible
determine
already
stage,
which
helps
avoid
costly
changes
later
project
phases.
Early
determination
these
also
allows
accurate
estimation
investment
costs.
Tests
proposed
solution
were
several
buildings,
comparing
preliminary
final
results.
research
results
show
precise
planning
stages.
This
reduces
costs,
increases
efficiency,
better
adapts
technological
conditions.
Язык: Английский