Concilium,
Год журнала:
2024,
Номер
24(3), С. 229 - 248
Опубликована: Фев. 22, 2024
This
research
aimed
to
analyze
the
importance
of
artificial
intelligence
and
sustainability
in
higher
education
according
literature
field
present
relationships
this
context
with
United
Nations
Sustainable
Development
Goals
(SDGs).
The
adopted
strategies
included
bibliometric
analysis
using
VOSviewer
software
review,
considering
Web
Science
scientific
database.
resulted
clustering
four
groups.
blue
cluster
highlighted
emergence
interest
studies
on
AI
following
Covid-19
pandemic.
green
emphasized
more
efficient
teaching
methods
adapted
demands
education,
as
well
need
empower
teachers
use
developing
students'
skills
competencies,
emphasizing
sustainability.
yellow
indicated
presence
based
triad
sustainable
innovation,
aiming
prepare
students
for
future
challenges.
red
impact
focusing
student
learning,
efficiency,
performance.
Finally,
identified
main
technologies
their
relationship
SDGs.
reflections
presented
here
can
contribute
expanding
discussions
between
education.
From
a
practical
standpoint,
it
serve
foundation
university
managers
promote
integration
into
processes,
Sustainable Cities and Society,
Год журнала:
2024,
Номер
108, С. 105499 - 105499
Опубликована: Май 4, 2024
While
acknowledging
the
widespread
recognition
of
artificial
intelligence's
(AI)
potential
in
achieving
sustainable
development,
there
remains
a
notable
deficiency
and
thorough
examination
its
specific
applications,
impacts,
challenges,
particularly
within
construction
industry.
A
comprehensive
investigation
is
critical
to
explore
understand
multifaceted
applications
AI
fostering
sustainability
across
all
phases
project.
This
paper
aims
examine
how
can
be
effectively
integrated
key
project
phases—i.e.,
planning,
design,
construction,
operation
maintenance,
through
systematic
literature
review
map
their
adoption
best
practices.
The
findings
revealed:
(a)
Sustainable
development
goals
(SDGs)
pertinent
industry—i.e.,
SDGs
6-9,11-13,15,17;
(b)
that
show
highest
promote
7,9,11;
(c)
Within
spectrum
these
goals,
potentially
transform
industry
contribute
consideration
processes
more
efficient
resilient
ways;
(d)
Ethical
considerations,
data
privacy
security
concerns
must
addressed,
along
with
an
urgent
need
for
specialised
training
maintenance
systems;
(e)
Careful
implementation
management
essential
harness
full
potential,
while
addressing
challenges
sector.
Resources,
Год журнала:
2024,
Номер
13(2), С. 19 - 19
Опубликована: Янв. 24, 2024
In
this
study,
we
examine
Society
5.0,
defined
as
a
future
framework
where
advanced
technologies
like
artificial
intelligence
(AI),
the
Internet
of
Things
(IoT),
and
other
digital
innovations
are
integrated
into
society
for
sustainable
resource
management.
5.0
represents
an
evolution
from
industrial
focus
Industry
4.0,
aiming
harmonious
balance
between
technological
progress
human-centric
values,
consistent
with
United
Nations
Sustainable
Development
Goals.
Our
methodology
involves
detailed
literature
review,
focusing
on
identifying
evaluating
roles
AI,
IoT,
emerging
in
enhancing
efficiency,
particularly
water
energy
sectors,
to
minimize
environmental
impact.
This
approach
allows
us
present
comprehensive
overview
current
advancements
their
potential
applications
5.0.
study’s
added
value
lies
its
synthesis
diverse
strategies,
emphasizing
synergy
circular
economy
practices
economic
development.
We
highlight
necessity
resilience
adaptability
ecological
challenges
advocate
collaborative,
data-informed
decision-making
framework.
findings
portray
holistic
model
addressing
contemporary
global
management
conservation,
projecting
technology
aligns
sustainable,
equitable,
human-centered
Heliyon,
Год журнала:
2024,
Номер
10(2), С. e24313 - e24313
Опубликована: Янв. 1, 2024
The
use
of
supplementary
cementitious
materials
has
been
widely
accepted
due
to
increasing
global
carbon
emissions
resulting
from
demand
and
the
consequent
production
Portland
cement.
Moreover,
researchers
are
also
working
on
complementing
strength
deficiencies
concrete;
fiber
reinforcement
is
one
those
techniques.
This
study
aims
assess
influence
recycling
wheat
straw
ash
(WSA)
as
cement
replacement
material
coir/coconut
fibers
(CF)
ingredients
together
mechanical
properties,
permeability
embodied
concrete.
A
total
255
concrete
samples
were
prepared
with
1:1.5:3
mix
proportions
at
0.52
water-cement
ratio
these
all-concrete
specimens
cured
for
28
days.
It
was
revealed
that
addition
10
%
WSA
2
CF
in
recorded
compressive,
splitting
tensile
flexural
strengths
by
33
MPa,
3.55
MPa
5.16
which
greater
than
control
days
respectively.
it
observed
incorporating
4
coir
20
reduced
63.40
after
can
prevent
propagation
major
minor
cracks.
In
addition,
getting
when
level
along
increases
Furthermore,
based
results
obtained,
optimum
amount
suggested
be
improved
results.
Scientific Reports,
Год журнала:
2025,
Номер
15(1)
Опубликована: Фев. 28, 2025
Physics-informed
modeling
(PIM)
using
advanced
machine
learning
(ML)
represents
a
paradigm
shift
in
the
field
of
concrete
technology,
offering
potent
blend
scientific
rigor
and
computational
efficiency.
By
harnessing
synergies
between
physics-based
principles
data-driven
algorithms,
PIM-ML
not
only
streamlines
design
process
but
also
enhances
reliability
sustainability
structures.
As
research
continues
to
refine
these
models
validate
their
performance,
adoption
promises
revolutionize
how
materials
are
engineered,
tested,
utilized
construction
projects
worldwide.
In
this
work,
an
extensive
literature
review,
which
produced
global
representative
database
for
splitting
tensile
strength
(Fsp)
recycled
aggregate
concrete,
was
indulged.
The
studied
components
such
as
C,
W,
NCAg,
PL,
RCAg_D,
RCAg_P,
RCAg_wa,
Vf,
F_type
were
measured
tabulated.
collected
257
records
partitioned
into
training
set
200
(80%)
validation
57
(20%)
line
with
more
reliable
partitioning
database.
Five
techniques
created
"Weka
Data
Mining"
software
version
3.8.6
applied
predict
Fsp
Hoffman
&
Gardener
method
performance
metrics
used
evaluate
sensitivity
variables
ML
models,
respectively.
results
show
Kstar
model
demonstrates
highest
level
among
achieving
exceptional
accuracy
R2
0.96
Accuracy
94%.
Its
RMSE
MAE
both
low
at
0.15
MPa,
indicating
minimal
deviations
predicted
actual
values.
Additional
WI
(0.99),
NSE
(0.96),
KGE
(0.96)
further
confirm
model's
superior
efficiency
consistent
making
it
most
dependable
tool
practical
applications.
Also
analysis
shows
that
Water
content
(W)
exerts
significant
impact
40%,
demonstrating
amount
water
mix
is
critical
factor
optimal
strength.
This
underscores
need
careful
management
balance
workability
sustainable
production.
Coarse
natural
(NCAg)
has
substantial
38%,
its
essential
role
maintaining
structural
integrity
mix.
