With
buildings
accounting
for
a
significant
portion
of
global
energy
consumption
and
greenhouse
gas
emissions,
the
application
artificial
intelligence
(AI)
holds
promise
enhancing
sustainability
in
building
lifecycle.
This
systematic
literature
review
addresses
current
understanding
AI's
potential
to
optimize
efficiency
minimize
environmental
impact
design,
construction,
operation.
A
comprehensive
synthesis
were
conducted
identify
AI
technologies
applicable
sustainable
practices,
examine
their
influence,
analyze
challenges
implementation.
The
was
guided
by
meticulous
search
strategy
utilizing
keywords
related
findings
reveal
capabilities
optimizing
through
intelligent
control
systems,
enabling
predictive
maintenance,
aiding
design
simulation.
Advanced
machine
learning
algorithms
facilitate
data-driven
analysis
prediction,
while
digital
twins
provide
real-time
insights
informed
decision-making.
Furthermore,
identifies
barriers
adoption,
including
cost
concerns,
data
security
risks,
presents
transformative
opportunity
enhance
built
environment,
offering
innovative
solutions
optimization
environmentally
conscious
practices.
However,
addressing
technical
practical
will
be
crucial
successful
integration
Buildings,
Journal Year:
2024,
Volume and Issue:
14(4), P. 1113 - 1113
Published: April 16, 2024
Carbon
emissions
present
a
pressing
challenge
to
the
traditional
construction
industry,
urging
fundamental
shift
towards
more
sustainable
practices
and
materials.
Recent
advances
in
sensors,
data
fusion
techniques,
artificial
intelligence
have
enabled
integrated
digital
technologies
(e.g.,
twins)
as
promising
trend
achieve
emission
reduction
net-zero.
While
twins
sector
shown
rapid
growth
recent
years,
most
applications
focus
on
improvement
of
productivity,
safety
management.
There
is
lack
critical
review
discussion
state-of-the-art
improve
sustainability
this
sector,
particularly
reducing
carbon
emissions.
This
paper
reviews
existing
research
where
been
directly
used
enhance
throughout
entire
life
cycle
building
(including
design,
construction,
operation
maintenance,
renovation,
demolition).
Additionally,
we
introduce
conceptual
framework
for
which
involves
elements
twin
implementation
process,
discuss
challenges
faced
during
deployment,
along
with
potential
opportunities.
A
proof-of-concept
example
also
presented
demonstrate
validity
proposed
enhanced
sustainability.
study
aims
inspire
forward-thinking
innovation
fully
exploit
transform
industry
into
sector.
Buildings,
Journal Year:
2024,
Volume and Issue:
14(7), P. 2137 - 2137
Published: July 11, 2024
Buildings
significantly
contribute
to
global
energy
consumption
and
greenhouse
gas
emissions.
This
systematic
literature
review
explores
the
potential
of
artificial
intelegence
(AI)
enhance
sustainability
throughout
a
building’s
lifecycle.
The
identifies
AI
technologies
applicable
sustainable
building
practices,
examines
their
influence,
analyses
implementation
challenges.
findings
reveal
AI’s
capabilities
in
optimising
efficiency,
enabling
predictive
maintenance,
aiding
design
simulation.
Advanced
machine
learning
algorithms
facilitate
data-driven
analysis,
while
digital
twins
provide
real-time
insights
for
decision-making.
also
barriers
adoption,
including
cost
concerns,
data
security
risks,
While
offers
innovative
solutions
optimisation
environmentally
conscious
addressing
technical
practical
challenges
is
crucial
its
successful
integration
practices.
Smart and Sustainable Built Environment,
Journal Year:
2024,
Volume and Issue:
13(3), P. 711 - 736
Published: March 22, 2024
Purpose
Implementing
blockchain
in
sustainable
development
goals
(SDGs)
and
environmental,
social
governance
(ESG)-aligned
infrastructure
involves
intricate
strategic
factors.
Despite
technological
advancements,
a
significant
research
gap
persists,
particularly
emerging
economies.
This
study
aims
to
address
the
challenges
related
SDGs
ESG
objectives
during
delivery
remain
problematic,
identifying
evaluating
critical
factors
for
successful
implementation.
Design/methodology/approach
employs
three-stage
methodology.
Initially,
13
are
identified
through
literature
review
validated
by
conducting
semi-structured
interviews
with
six
experts.
In
second
stage,
data
were
collected
from
nine
additional
final
undergoes
analysis
using
interpretive
structural
modeling
(ISM)–cross-impact
matrix
multiplication
applied
classification
(MICMAC),
aiming
identify
evaluate
independent
dependent
powers
of
driving
implementation
objectives.
Findings
The
study’s
findings
highlight
three
crucial
successfully
integrating
technology
(BT)
into
goals:
security
(F4),
identity
management
(F8)
supply
chain
(F7).
unravels
these
factors,
hierarchical
relationships
dependencies
applying
MICMAC
ISM
techniques,
emphasizing
their
interconnectedness.
Originality/value
highlights
integration
SDG
ESG-aligned
development,
offering
insights
policymakers
practitioners
while
importance
training
support
advancing
practices.
Buildings,
Journal Year:
2024,
Volume and Issue:
14(2), P. 376 - 376
Published: Feb. 1, 2024
In
2011,
the
term
Digital
Twin
was
originally
introduced
by
Michael
Grieves
to
define
synchronization
between
two
realities:
physical
objects
placed
in
a
real
space
and
virtual
within
space,
linked
through
mutual
exchange
of
data
throughout
entire
lifecycle,
both
real-time
asynchronously.
Nowadays,
is
among
principal
emerging
technological
innovations
Industry
4.0
5.0,
enabling
an
interaction
objects,
Big
Data,
Internet
Things,
Artificial
Intelligence.
The
construction
sector,
too,
now
exploring
potentialities
offered
approach
enhancing
innovative,
responsible,
sustainable
governance
buildings’
lifecycles.
Concerning
these
issues,
this
paper
proposes
visualizing
future
challenges
with
specific
focus
on
operation
maintenance
phase
its
related
impact
decarbonization
via
critical
literature
review
current
statements.
applied
methodology
based
three
different
questions
certain
research
issues
performed
Scopus
database.
selected
findings
were
filtered,
classified,
discussed.
Some
have
been
identified,
defining
promoting
novel
ideas.
Developments in the Built Environment,
Journal Year:
2024,
Volume and Issue:
18, P. 100432 - 100432
Published: April 1, 2024
In
the
global
push
for
carbon
neutrality
and
emergence
of
net-zero
buildings
(NZCBs),
lack
substantial
progress
in
achieving
NZCB
goals
due
to
unaddressed
barriers
is
a
pressing
issue.
While
there
are
ongoing
initiatives
NZCB,
crucial
research
gap
exists
thorough
examination
specific
their
interconnected
complexities,
impeding
agenda.
This
study
aims
fill
this
by
identifying
investigating
negating
implementation
Prefabricated
NZCBs
through
systematic
literature
review
analyzing
relationships
using
Interpretative
structural
modeling
(ISM)
analysis.
Additionally,
provide
strategic
roadmap
overcome
that
impede
successful
prefabricated
NZCBs.
The
findings
reveal
regulatory
barriers,
including
regulations,
policies,
efforts,
emerge
as
most
significant,
followed
closely
uncertainties
related
long-term
financial
returns
payback
periods.
concludes
acceleration
depends
on
addressing
these
identified
underscores
imperative
collaboration
among
government
bodies,
supply
demand
sectors,
end
users.
Policymakers
urged
integrate
into
guidelines
promoting
NZCB.
implications
substantial,
offering
path
forward
prioritizes
obstacles
addresses
ultimately
propelling
agenda
contributing
more
sustainable
environmentally
friendly
future.
Machines,
Journal Year:
2025,
Volume and Issue:
13(1), P. 36 - 36
Published: Jan. 7, 2025
The
transition
from
Industry
4.0
to
5.0
gives
more
prominence
human-centered
and
sustainable
manufacturing
practices.
This
paper
proposes
a
conceptual
design
framework
based
on
Vision
Transformers
(ViTs)
digital
twins,
meet
the
demands
of
5.0.
ViTs,
known
for
their
advanced
visual
data
analysis
capabilities,
complement
simulation
optimization
capabilities
which
in
turn
can
enhance
predictive
maintenance,
quality
control,
human–machine
symbiosis.
applied
is
capable
analyzing
multidimensional
data,
integrating
operational
streams
real-time
tracking
application
decision
making.
Its
main
characteristics
are
anomaly
detection,
analytics,
adaptive
optimization,
line
with
objectives
sustainability,
resilience,
personalization.
Use
cases,
including
maintenance
demonstrate
higher
efficiency,
waste
reduction,
reliable
operator
interaction.
In
this
work,
emergent
role
ViTs
twins
development
intelligent,
dynamic,
human-centric
industrial
ecosystems
discussed.
Smart and Sustainable Built Environment,
Journal Year:
2024,
Volume and Issue:
unknown
Published: May 8, 2024
Purpose
Previous
research
has
demonstrated
that
Digital
Twins
(DT)
are
extensively
employed
to
improve
sustainable
construction
methods.
Nonetheless,
their
uptake
in
numerous
nations
is
still
constrained.
This
study
seeks
identify
and
examine
the
digital
twin’s
implementation
barriers
building
projects
augment
operational
performance
sustainability.
Design/methodology/approach
An
iterative
two-stage
approach
was
adopted
explore
phenomena
under
investigation.
General
DT
Implementation
Barriers
were
first
identified
from
extant
literature
subsequently
explored
using
primary
questionnaire
survey
data
Hong
Kong
industry
professionals.
Findings
Survey
results
illustrated
Lack
of
methodologies
tools,
Difficulty
ensuring
a
high
level
real-time
communication,
Impossibility
directly
measuring
all
relevant
DT,
need
share
among
multiple
application
systems
involving
stakeholders
Uncertainties
quality
reliability
main
for
adopting
twins'
technology.
Moreover,
Ginni’s
mean
difference
measure
dispersion
showed
stationary
adoption
needed
stakeholders.
Practical
implications
The
study’s
findings
offer
valuable
guidance
industry.
They
help
adopt
technology,
which,
turn,
improves
cost
efficiency
reduces
project
expenses
enhances
environmental
responsibility,
providing
companies
competitive
edge
Originality/value
rigorously
explores
Twin
industry,
employing
systematic
includes
comprehensive
review,
Ranking
Analysis
(RII)
coefficient
(GM).
With
tailored
focus
on
Kong,
aims
identify,
analyze
provide
novel
insights
into
challenges.
Emphasizing
practical
relevance,
bridges
gap
between
academic
understanding
real-world
application,
offering
actionable
solutions
professionals,
policymakers
researchers.
multifaceted
contribution
feasibility
success
within
Architecture,
Engineering
Construction
(AEC)
sector.
Heliyon,
Journal Year:
2024,
Volume and Issue:
10(8), P. e28585 - e28585
Published: April 1, 2024
In
smart
buildings,
digital
construction
technologies
can
support
more
efficient
management
of
data
and
information
related
to
building
components.
This
paper
aims
draw
a
robust
linking
mechanism
between
that
buildings
city
development
satisfy
users'
expectations.
Data
was
attained
using
qualitative
approach
via
secondary
from
literature
primary
in
the
context
case
study
with
users.
The
suggests
importance
recognising
single/multi-purposed
better
synergy
Buildings,
Journal Year:
2024,
Volume and Issue:
14(6), P. 1774 - 1774
Published: June 12, 2024
The
global
demand
for
energy
is
significantly
impacted
by
the
consumption
patterns
within
building
sector.
As
such,
importance
of
simulation
and
prediction
growing
exponentially.
This
research
leverages
Building
Information
Modelling
(BIM)
methodologies,
creating
a
synergy
between
traditional
software
methods
algorithm-driven
approaches
comprehensive
analysis.
study
also
proposes
method
monitoring
select
management
factors,
step
that
could
potentially
pave
way
integration
digital
twins
in
systems.
grounded
case
newly
constructed
educational
New
South
Wales,
Australia.
physical
model
was
created
using
Autodesk
Revit,
conventional
BIM
methodology.
EnergyPlus,
facilitated
OpenStudio,
employed
software-based
analysis
output
then
used
to
develop
preliminary
algorithm
models
regression
strategies
Python.
In
this
analysis,
temperature
relative
humidity
each
unit
were
as
independent
variables,
with
their
being
dependent
variable.
sigmoid
model,
known
its
accuracy
interpretability,
advanced
simulation.
combined
sensor
data
real-time
prediction.
A
basic
twin
(DT)
example
simulate
dynamic
control
air
conditioning
lighting,
showcasing
adaptability
effectiveness
system.
explores
potential
machine
learning,
specifically
reinforcement
optimizing
response
environmental
changes
usage
conditions.
Despite
current
limitations,
identifies
future
directions.
These
include
enhancing
developing
complex
algorithms
boost
efficiency
reduce
costs.
