Automation in Construction,
Journal Year:
2024,
Volume and Issue:
166, P. 105641 - 105641
Published: July 30, 2024
Artificial
intelligence
and
its
subfields,
such
as
machine
learning,
robotics,
optimisation,
knowledge-based
systems,
reality
capture
extended
reality,
have
brought
remarkable
advancements
transformative
changes
to
various
industries,
including
the
building
deconstruction
industry.
Acknowledging
AI's
benefits
for
deconstruction,
this
paper
aims
investigate
AI
applications
within
domain.
A
systematic
review
of
existing
literature
focused
on
planning,
implementation
post-implementation
activities
context
was
carried
out.
Furthermore,
challenges
opportunities
were
identified
presented
in
paper.
By
offering
insights
into
application
key
activities,
paves
way
realising
potential
sector.
Sustainability,
Journal Year:
2024,
Volume and Issue:
16(17), P. 7805 - 7805
Published: Sept. 7, 2024
Building
energy
consumption
prediction
models
are
powerful
tools
for
optimizing
management.
Among
various
methods,
artificial
neural
networks
(ANNs)
have
become
increasingly
popular.
This
paper
reviews
studies
since
2015
on
using
ANNs
to
predict
building
use
and
demand,
focusing
the
characteristics
of
different
ANN
structures
their
applications
across
phases—design,
operation,
retrofitting.
It
also
provides
guidance
selecting
most
appropriate
each
phase.
Finally,
this
explores
future
developments
in
ANN-based
predictions,
including
improving
data
processing
techniques
greater
accuracy,
refining
parameterization
better
capture
features,
algorithms
faster
computation,
integrating
with
other
machine
learning
such
as
ensemble
hybrid
models,
enhance
predictive
performance.
IntechOpen eBooks,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 15, 2025
This
chapter
explores
how
smart
cities
can
enhance
building
management
through
technologies
like
the
Internet
of
Things
(IoT)
and
advanced
predictive
models,
focusing
on
energy
efficiency
air
quality.
The
escalating
reliance
technology
as
primary
solution
to
contemporary
future
challenges
has
highlighted
(IoT),
digitalization,
machine
learning,
among
others,
new
methodologies
for
assessing
in
cities.
Moreover,
realm
defining
innovative
systems,
pressing
issues
such
climate
change
pandemic
episodes
COVID-19
underscore
need
prioritize
imperative
led
emergence
digital
twins,
a
integrating
3D
models
with
real-time
data,
enabling
comprehensive
understanding
dynamics.
In
addition,
automated
prediction
leveraging
statistical
learning
techniques
contribute
significantly
enhancing
climatization
control,
efficiency,
quality
management.
These
analyze
historical
accurate
forecasts
assess
behavior,
which
is
crucial
effective
maintenance
planning.
application
linear
non-linear
regression
alongside
Support
Vector
Machines
neural
networks,
further
refines
predictions.
Additionally,
monitoring
decision
algorithms
optimize
information
transmission
during
incidents,
ensuring
rapid
response
environmental
factors
or
anomalies,
thereby
mitigating
risks
maximizing
operational
efficiency.
