SSRN Electronic Journal,
Год журнала:
2022,
Номер
unknown
Опубликована: Янв. 1, 2022
Concrete
strength
is
considered
to
be
a
significant
criterion
in
the
construction
and
operation
of
reinforced
concrete
structures.
Reinforced
structures
exposed
corrosive
environmental
effects
are
prone
short
lifetime
due
corrosion
rebar
presence
chloride
Regarding
resolve
or
mitigating
abovementioned
shortcomings,
current
study
conducted
investigate
impact
Xanthan
Gum
(polysaccharide)on
durability
properties
associated
with
an
increased
half-life
self-compacting
concrete.
Durability
mechanical
have
been
evaluated
at
different
concentrations
supplementary
material
(in
0.2
0.25%
by
weight),
microsilica
5,
7,
10%
nanosilica
2,
2
4%
weight).
In
addition
rheological
concrete,
other
factors
including
permeability
coefficient,
depth
ions
infiltration,
electrical
conductivity,
pressure
resistance
taken
into
account.
To
do
so,
service
43
mixes
estimated
using
FIB
modeling.
this
study,
soft
computing
methodologies
(specific
Artificial
Neural
Network
(ANN))
utilized
facilitate
computation
burden
resulting
from
complexity
model
number
variables.
observed
results
confirmed
high
accuracy
low
error
ANN
modeling
terms
compatibility,
nonlinearity,
proper
generalizability,
capability
anticipate
resistance,
as
well
prediction
coefficient
infiltration.
Also,
sensitivity
analysis
was
performed
super
decision
software
determine
ranking
priority
additives.
Our
final
approve
that
additives
can
enhance
life.
Moreover,
experiment
sensitive
additives,
change
additive’s
weight
ratio
may
lead
alteration
rank.
Materials,
Год журнала:
2022,
Номер
15(20), С. 7045 - 7045
Опубликована: Окт. 11, 2022
In
the
21st
century,
numerous
numerical
calculation
techniques
have
been
discovered
and
used
in
several
fields
of
science
technology.
The
purpose
this
study
was
to
use
an
artificial
neural
network
(ANN)
forecast
compressive
strength
waste-based
concretes.
specimens
studied
include
different
kinds
mineral
additions:
metakaolin,
silica
fume,
fly
ash,
limestone
filler,
marble
waste,
recycled
aggregates,
ground
granulated
blast
furnace
slag.
This
method
is
based
on
experimental
results
available
for
1303
mixtures
gathered
from
22
bibliographic
sources
ANN
learning
process.
Based
a
multilayer
feedforward
model,
data
were
arranged
prepared
train
test
model.
model
consists
18
inputs
following
type
cement,
water
content,
binder
ratio,
replacement
quantity
superplasticizer,
etc.
built
applied
with
MATLAB
software
using
module.
According
by
proposed
shows
strong
capacity
predicting
concrete
particularly
precise
satisfactory
accuracy
(R²
=
0.9888,
MAPE
2.87%).
Case Studies in Construction Materials,
Год журнала:
2024,
Номер
21, С. e03478 - e03478
Опубликована: Июль 2, 2024
The
relationship
between
microstructure
and
mechanical
properties
of
multi-component
solid
waste
low-carbon
cementitious
materials
has
been
widely
pay
attention
to.
However,
industrial
is
a
complex
system
with
many
variable
factors,
which
makes
it
difficult
to
design
the
formulation
materials.
This
paper
pioneered
application
machine
learning
(ML)
models,
algorithms
error
rates
analyze
compressive
flexural
strength
fly
ash-based
pastes.
Coefficient
determination
(R2),
mean
squared
(MSE),
root
square
(RMSE),
absolute
(MAE)
a20-index
were
used
evaluate
robustness.
X-ray
diffraction
(XRD),
scanning
electron
microscope
(SEM)
Brunauer-Emmett-Taylor
(BET)
carried
out
evolution
evaluation
results
ML
models
exhibited
that
Gradient
boosting
regression
(GBR)
model
had
best
parameters
steep
normal
distribution
fitting
curve
an
0.861.
GBR
key
factors
identified
by
Pearson's
coefficient,
was
benefit
determine
Furthermore,
experiments
also
demonstrated
optimum
ratio
low
carbon
material
10
%
gypsum,
metakaolin,
45
ash,
15
slag
20
cement,
respectively.
It
worth
noting
this
kind
reached
35
MPa,
superior
P·O
32.5
cement.
phase,
SEM
images
pore
structure
showed
synergistic
effect
effectively
filled
voids
facilitated
formation
variety
gelatinous
through
gelling
reactions
in
late
stage
(14–28
d).
work
will
promote
resource
utilization
waste,
contribute
reduction,
can
accelerate
green
revolution
concrete.
Research Square (Research Square),
Год журнала:
2023,
Номер
unknown
Опубликована: Июнь 1, 2023
Abstract
In
this
study,
we
use
highly
developed
machine
learning
techniques
to
accurately
estimate
the
compressive
strength
(CS)
of
blended
concrete,
considering
its
composition,
including
cement,
SCMs
(ground
granulated
blast
furnace
slag
(GGBFS)
and
fly
ash
(FA)),
water,
superplasticizer,
fine/coarse
aggregate,
curing
age.
addition
these,
examine
an
array
models,
XGBoost,
decision
trees
(DT),
deep
neural
networks
(DNN),
linear
regression
(LR).
Among
them,
XGBoost
has
best
performance
in
every
category.
We
Bayesian
optimization
method
for
hyperparameter
fine-tuning
improve
forecast
accuracy.
Our
in-depth
examination
demonstrates
better
predictive
skills
ensemble
models
like
RF
over
LR,
which
is
limited
ability
capture
data
complexity
beyond
relationships.
With
R
2
0.952,
RMSE
4.88,
MAE
3.24,
MAPE
9.94%,
performs
noticeably
than
rivals.
Using
SHAP
analysis,
determine
that
age,
water
content
cement
concentration
constitute
main
factors
influencing
capacity
model,
with
contributions
superplasticizer
being
minimal.
Curing
age
have
interesting
positive
association
CS,
but
a
negative
link
CS.
These
results
highlight
value
learning,
more
especially
effectiveness
as
potent
device
forecasting
CS
mixed
concrete.
Additionally,
knowledge
gained
from
our
research
provides
designers
researchers
field
concrete
materials
useful
direction,
highlighting
most
important
strength.
Future
studies
should
work
toward
additional
by
attempting
verify
these
across
wider
variety
compositions
test
settings.
Buildings,
Год журнала:
2023,
Номер
13(10), С. 2605 - 2605
Опубликована: Окт. 16, 2023
This
study
comprehensively
investigates
the
rheological
properties
of
self-compacting
concrete
(SCC)
and
their
impact
on
critical
parameters,
including
migration
coefficient,
penetration
depth
chlorine
ions,
specific
electrical
resistance,
compressive
strength.
A
total
43
mix
designs
were
meticulously
examined
to
explore
relationships
between
these
properties.
Quantitative
analysis
employed
a
backpropagation
neural
network
model
with
single
hidden
layer
accurately
predict
resistant
durable
characteristics
concrete.
The
optimal
number
neurons
in
was
determined
using
fitting
component
selection
method,
implemented
MATLAB
software(2021b).
Additionally,
qualitative
conducted
sensitivity
expert
opinions
determine
priority
research
additives.
main
contributions
this
paper
lie
exploration
SCC
properties,
utilization
for
accurate
prediction,
prioritization
additives
through
analysis.
demonstrated
exceptional
performance
predicting
test
results,
achieving
high
accuracy
rate
14
parameters
such
as
depth,
strength,
resistance.
Sensitivity
revealed
that
xanthan
gum
emerged
most
influential
additive,
accounting
43%
observed
effects,
followed
by
nanomaterials
at
35%
micro-silica
21%.
European Journal of Environmental and Civil engineering,
Год журнала:
2024,
Номер
unknown, С. 1 - 24
Опубликована: Дек. 18, 2024
Self-compacting
concrete
improves
fresh-state
fluidity
while
maintaining
mechanical
properties,
and
presents
an
increasing
research
interest
in
fiber
incorporation.
However,
the
effects
of
fibers
on
rheological
behavior
durability
remain
insufficiently
studied
existing
literature.
This
study
provides
new
insights
effect
polyester
steel
rheological,
mechanical,
durability,
microstructural,
thermal
properties
SCC.
Nine
different
mixtures
were
studied:
one
reference
SCC
(without
fibers),
four
incorporating
fibers,
other
fibers.
The
percentages
for
each
type
0.25%,
0.5%,
0.75%,
1%.
results
showed
that
whatever
their
type,
adding
reduces
workability
improving
compressive
strength
Incorporating
1%
increased
flexural
by
97%,
whereas
had
no
significant
effect.
In
terms
porosity
but
reduced
its
sorptivity.
For
instance,
9.5%
whilst
reducing
sorptivity
23%
compared
to
one.
Polyester
also
improved
conductivity,
inverse
An
proportionality
between
plastic
viscosity
was
identified,
highlighting
importance
influencing
transport
hardened
Based
these
findings,
it
is
recommended
careful
attention
should
be
taken
change
both
when
a
given
percentage
into
SSRN Electronic Journal,
Год журнала:
2022,
Номер
unknown
Опубликована: Янв. 1, 2022
Concrete
strength
is
considered
to
be
a
significant
criterion
in
the
construction
and
operation
of
reinforced
concrete
structures.
Reinforced
structures
exposed
corrosive
environmental
effects
are
prone
short
lifetime
due
corrosion
rebar
presence
chloride
Regarding
resolve
or
mitigating
abovementioned
shortcomings,
current
study
conducted
investigate
impact
Xanthan
Gum
(polysaccharide)on
durability
properties
associated
with
an
increased
half-life
self-compacting
concrete.
Durability
mechanical
have
been
evaluated
at
different
concentrations
supplementary
material
(in
0.2
0.25%
by
weight),
microsilica
5,
7,
10%
nanosilica
2,
2
4%
weight).
In
addition
rheological
concrete,
other
factors
including
permeability
coefficient,
depth
ions
infiltration,
electrical
conductivity,
pressure
resistance
taken
into
account.
To
do
so,
service
43
mixes
estimated
using
FIB
modeling.
this
study,
soft
computing
methodologies
(specific
Artificial
Neural
Network
(ANN))
utilized
facilitate
computation
burden
resulting
from
complexity
model
number
variables.
observed
results
confirmed
high
accuracy
low
error
ANN
modeling
terms
compatibility,
nonlinearity,
proper
generalizability,
capability
anticipate
resistance,
as
well
prediction
coefficient
infiltration.
Also,
sensitivity
analysis
was
performed
super
decision
software
determine
ranking
priority
additives.
Our
final
approve
that
additives
can
enhance
life.
Moreover,
experiment
sensitive
additives,
change
additive’s
weight
ratio
may
lead
alteration
rank.
