Developments in the Built Environment,
Journal Year:
2024,
Volume and Issue:
19, P. 100494 - 100494
Published: July 1, 2024
This
research
explores
the
use
of
machine
learning
to
predict
mechanical
properties
cementitious
materials
enhanced
with
carbon
nanotubes
(CNTs).
Specifically,
study
focuses
on
estimating
elastic
modulus
and
flexural
strength
these
novel
composite
materials,
potential
significantly
impact
construction
industry.
Seven
key
variables
were
analyzed
including
water-to-cement
ratio,
sand-to-cement
curing
age,
CNT
aspect
content,
surfactant-to-CNT
sonication
time.
Artificial
neural
network,
support
vector
regression,
histogram
gradient
boosting,
used
properties.
Furthermore,
a
user-friendly
formula
was
extracted
from
network
model.
Each
model
performance
evaluated,
revealing
be
most
effective
for
predicting
modulus.
However,
boosting
outperformed
all
others
in
strength.
These
findings
highlight
effectiveness
employed
techniques,
accurately
CNT-enhanced
materials.
Additionally,
extracting
formulas
provides
valuable
insights
into
interplay
between
input
parameters
Ain Shams Engineering Journal,
Journal Year:
2024,
Volume and Issue:
15(5), P. 102672 - 102672
Published: Feb. 7, 2024
Corrosion
is
a
pervasive
problem
that
impacts
the
integrity,
safety,
and
longevity
of
structures,
equipment,
infrastructure
across
numerous
industries.
Traditional
corrosion
control
measures
often
rely
on
use
toxic,
hazardous,
environmentally
damaging
inhibitors,
posing
significant
threat
to
both
human
health
environment.
In
recent
years,
there
has
been
growing
interest
in
development
sustainable
inhibitors
are
effective
responsible.
This
review
article
discusses
current
state
art
including
their
classification,
mechanisms
action,
applications
various
The
also
provides
insights
into
challenges
opportunities
associated
with
highlighting
need
for
continued
research
this
critical
area.
Engineering Failure Analysis,
Journal Year:
2024,
Volume and Issue:
159, P. 108065 - 108065
Published: Feb. 6, 2024
Several
environmental
challenges
such
as
corrosion,
low
temperature,
hydrogen-induced
cracking
(HIC),
stress
corrosion
(SCC),
sulfide
(SSCC),
and
various
other
failure
mechanisms
contribute
to
the
deterioration
of
mechanical
properties
pipeline
steels,
ultimately
resulting
in
failure.
In
this
review,
diverse
hydrogen
attack
sources,
their
possible
mechanisms,
strategies
for
mitigation
different
environments
are
explored.
Optimizing
microstructure
steels
can
greatly
improve
resistance
cracking.
This
involves
tailoring
several
microstructural
parameters
like
phase
composition,
dislocation
density,
crystallographic
texture,
grain
size,
boundary,
inclusions/precipitates,
amongst
others
needs
steel's
service
environment.
The
evolving
research
landscape
concerning
role
these
HIC,
SCC,
was
discussed
study.
It
established
that
texture
boundary
characteristics
have
roles
play
improving
SCC
steels.
However,
degree
which
amidst
parameters,
affects
is
not
yet
established.
For
instance,
direct
influence
arrest
propagating
cracks
still
unclear
debated,
while
low-angle
boundaries
CSL
been
seen
also
has
a
more
profound
effect
on
HIC
Furthermore,
review
examines
welds.
investigates
adapt
existing
pipelines'
meet
demands
operations
arctic
environments.
pipelines
designated
cold
applications.
Finally,
explores
recent
advancements
transportation
gaseous
using
(natural
gas
infrastructure).
Ultimately,
study
reinforces
importance
optimization
environments,
detailing
contribution
individual
overall
performance
susceptibility
Materials Today Chemistry,
Journal Year:
2024,
Volume and Issue:
37, P. 101986 - 101986
Published: March 12, 2024
This
review
article
underscores
the
critical
role
of
Density
Functional
Theory
(DFT)
in
prediction
corrosion
defect
structures
based
on
specific
chemical
compositions.
By
integrating
DFT
with
Molecular
Dynamics
(MD)
simulations,
we
gain
a
more
nuanced
understanding
processes.
The
further
explores
how
advanced
computational
approaches,
encompassing
calculations,
MD
and
innovative
application
Machine
Learning
(ML)
Artificial
Intelligence
(AI),
are
revolutionizing
studies.
These
technologies
enhance
our
ability
to
comprehend
predict
progression
depth
across
various
environments.
ML
AI
algorithms
particularly
noted
for
their
capacity
identify
complex
patterns,
thereby
enabling
development
accurate
predictive
models
behavior.
As
resources
continue
evolve,
leveraging
high-performance
computing
has
become
pivotal
simulating
larger
systems
achieving
detailed
insights.
convergence
quantum
mechanics,
molecular
dynamics,
artificial
intelligence
marks
promising
frontier
experiments
research,
offering
profound
implications
maintenance
strategies
protection
infrastructure.
Case Studies in Construction Materials,
Journal Year:
2024,
Volume and Issue:
20, P. e03130 - e03130
Published: April 4, 2024
Ordinary
Portland
cement
(OPC)
is
proving
to
be
hazardous
the
environment.
To
replace
OPC,
geopolymers
(GPs)
are
introduced.
However,
fully
OPC
by
GPs
extensive
laboratory
tests
required
assess
long-term
and
short-term
properties
of
in
different
scenarios.
Given
shortage
time
for
performing
such
testing,
artificial
intelligence
(AI)
used
analyze
GPs.
In
this
study,
AI
techniques
as
neuro
network
(ANN),
adaptive
neuro-fuzzy
inference
system
(ANFIS),
gene
expression
programming
(GEP)
obtain
predictive
models
estimating
compressive
strength
fly
ash
ground
granulated
blast
furnace
slag-based
GP
concrete.
Different
statistical
parameters
evaluate
performance
models.
Similarly,
sensitivity
parametric
analysis
also
conducted
on
input
parameters.
Additionally,
multiple
linear
regression
was
performed
whole
database.
After
comparing
all
results,
it
concluded
that
GEP
best
technique
predict
GP-based
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: March 13, 2024
Abstract
Bentonite
plastic
concrete
(BPC)
demonstrated
promising
potential
for
remedial
cut-off
wall
construction
to
mitigate
dam
seepage,
as
it
fulfills
essential
criteria
strength,
stiffness,
and
permeability.
High
workability
consistency
are
attributes
BPC
because
is
poured
into
trenches
using
a
tremie
pipe,
emphasizing
the
importance
of
accurately
predicting
slump
BPC.
In
addition,
prediction
models
offer
valuable
tools
estimate
various
strength
parameters,
enabling
adjustments
mixing
designs
optimize
project
construction,
leading
cost
time
savings.
Therefore,
this
study
explores
multi-expression
programming
(MEP)
technique
predict
key
characteristics
BPC,
such
slump,
compressive
(
fc
),
elastic
modulus
Ec
).
present
study,
158,
169,
111
data
points
were
collected
from
experimental
studies
,
Ec,
respectively.
The
dataset
was
divided
three
sets:
70%
training,
15%
testing,
another
model
validation.
MEP
exhibited
excellent
accuracy
with
correlation
coefficient
(R)
0.9999
0.9831
fc,
0.9300
Ec.
Furthermore,
comparative
analysis
between
conventional
linear
non-linear
regression
revealed
remarkable
precision
in
predictions
proposed
models,
surpassing
traditional
methods.
SHapley
Additive
exPlanation
indicated
that
water,
cement,
bentonite
exert
significant
influence
on
water
having
greatest
impact
while
curing
cement
exhibit
higher
modulus.
summary,
application
machine
learning
algorithms
offers
capability
deliver
prompt
precise
early
estimates
properties,
thus
optimizing
efficiency
design
processes.
Small,
Journal Year:
2024,
Volume and Issue:
20(29)
Published: Feb. 11, 2024
Functional
nanostructures
build
up
a
basis
for
the
future
materials
and
devices,
providing
wide
variety
of
functionalities,
possibility
designing
bio-compatible
nanoprobes,
etc.
However,
development
new
nanostructured
via
trial-and-error
approach
is
obviously
limited
by
laborious
efforts
on
their
syntheses,
cost
manpower.
This
one
reasons
an
increasing
interest
in
design
novel
with
required
properties
assisted
machine
learning
approaches.
Here,
dataset
synthetic
parameters
optical
important
class
light-emitting
nanomaterials
-
carbon
dots
are
collected,
processed,
analyzed
transitions
red
near-infrared
spectral
ranges.
A
model
prediction
characteristics
these
based
multiple
linear
regression
established
verified
comparison
predicted
experimentally
observed
synthesized
three
different
laboratories.
Based
analysis,
open-source
code
provided
to
be
used
researchers
procedures.
