Buildings,
Journal Year:
2024,
Volume and Issue:
14(8), P. 2349 - 2349
Published: July 30, 2024
Digital
twin
(DT)
is
recognized
as
a
pillar
in
the
transition
from
traditional
to
digital
construction,
yet
risks
(opportunities
and
threats)
associated
with
its
implementation
have
not
been
thoroughly
determined
literature.
In
addition,
there
scarcity
of
research
relating
DT
maturity
levels,
which
has
hindered
optimum
consideration
such
when
adopted
at
different
levels.
To
address
these
gaps,
this
study
conducted
literature
review
1889
documents
Scopus
Web
Science
databases.
After
rigorous
filtration,
72
were
selected
comprehensively
reviewed.
A
total
47
risk
factors
(RFs)
identified
categorized
into
opportunities
(economic,
technical,
environmental
sustainability,
monitoring
safety,
management)
threats
policy
management).
Subsequently,
RFs
mapped
onto
five-level
model,
providing
users
insights
on
each
level.
The
exhaustive
list
proposed
integration
model
corresponding
enables
stakeholders
identify
their
specific
use
cases
facilitate
decision-making
success
across
various
levels
real-life
construction
projects.
Computers and Geotechnics,
Journal Year:
2024,
Volume and Issue:
174, P. 106657 - 106657
Published: Aug. 12, 2024
In
geotechnical
engineering,
the
precise
identification
of
essential
soil
parameters
from
sensing
and
experimental
data
is
vital
for
accuracy
constitutive
finite
element
models.
However,
complexity
sophisticated
models
often
makes
this
task
challenging.
Traditional
optimization
methods
that
rely
on
gradient
information
fall
short
in
class
problems,
due
to
their
struggle
with
black
box
lacking
clear
pathways.
Gradient-free
methods,
though
circumventing
need
direct
data,
can
still
miss
out
integrating
previous
insights
when
faced
new
information.
To
tackle
these
issues,
our
study
presents
a
cutting-edge
method
inspired
by
mechanisms
underlying
AlphaZero,
DeepMind's
acclaimed
algorithm
excels
mastering
complex
strategic
games
through
autonomous
learning.
By
adopting
comparable
self-learning
technique,
approach
reinvents
parameter
advanced
as
game.
It
draws
parallel
between
optimizing
model
developing
victorious
chess
tactics.
This
utilizes
blend
deep
learning
initial
estimations
Monte
Carlo
Tree
Search
(MCTS)
finer
adjustments,
promoting
self-enhancing
calibration
process.
Such
an
paves
way
more
self-reliant
intelligent
methodology
data.
The
outcomes
demonstrate
robustness
versatility
across
various
models,
ranging
applications
involving
inverse
analyses
using
include
interactions
mechanical
devices
unsaturated
soils.
Buildings,
Journal Year:
2024,
Volume and Issue:
14(8), P. 2349 - 2349
Published: July 30, 2024
Digital
twin
(DT)
is
recognized
as
a
pillar
in
the
transition
from
traditional
to
digital
construction,
yet
risks
(opportunities
and
threats)
associated
with
its
implementation
have
not
been
thoroughly
determined
literature.
In
addition,
there
scarcity
of
research
relating
DT
maturity
levels,
which
has
hindered
optimum
consideration
such
when
adopted
at
different
levels.
To
address
these
gaps,
this
study
conducted
literature
review
1889
documents
Scopus
Web
Science
databases.
After
rigorous
filtration,
72
were
selected
comprehensively
reviewed.
A
total
47
risk
factors
(RFs)
identified
categorized
into
opportunities
(economic,
technical,
environmental
sustainability,
monitoring
safety,
management)
threats
policy
management).
Subsequently,
RFs
mapped
onto
five-level
model,
providing
users
insights
on
each
level.
The
exhaustive
list
proposed
integration
model
corresponding
enables
stakeholders
identify
their
specific
use
cases
facilitate
decision-making
success
across
various
levels
real-life
construction
projects.
