Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete
Results in Engineering,
Год журнала:
2025,
Номер
25, С. 104542 - 104542
Опубликована: Март 1, 2025
Язык: Английский
Tribological behavior of PLA reinforced with boron nitride nanoparticles using Taguchi and Machine learning approaches
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 104772 - 104772
Опубликована: Апрель 1, 2025
Язык: Английский
Geopolymer foam concrete: a review of pore characteristics, compressive strength and artificial intelligence in GFC strength simulations
Mohamed Abdellatief,
Alaa E. Hassanien,
Mohamed Mortagi
и другие.
Опубликована: Апрель 7, 2025
Язык: Английский
Advanced statistical optimization and performance evaluation of self‐compacting geopolymer concrete utilizing industrial by‐products
Structural Concrete,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 10, 2025
Abstract
This
study
optimizes
self‐compacting
geopolymer
concrete
(SCGC)
by
incorporating
industrial
by‐products,
such
as
fly
ash,
ground‐granulated
blast
furnace
slag,
and
rice
husk
ash
(RHA),
sustainable
alternatives
to
conventional
binders.
The
SCGC
mixtures
were
designed
with
varying
binder
compositions
activator
solutions,
their
fresh
hardened
properties
systematically
evaluated.
Workability
tests,
including
slump
flow,
J‐ring,
V‐funnel
conducted
assess
the
flowability
stability,
while
compressive,
tensile,
flexural
strength
tests
performed
at
3,
7,
28
days
evaluate
mechanical
performance.
Advanced
statistical
tools,
response
surface
methodology
analysis
of
variance,
employed
identify
key
influencing
factors
develop
predictive
models.
results
indicate
that
an
optimal
RHA
replacement
5%–10%
combined
a
silica‐rich
solution
significantly
enhances
SCGC,
achieving
28‐day
compressive
55.6
MPa
maintaining
excellent
workability.
models
demonstrated
high
accuracy
in
predicting
performance
trends,
validating
experimental
findings.
provides
robust
framework
for
optimizing
mix
designs
advancing
high‐performance
construction
materials.
Язык: Английский
Performance enhancement of recycled HDPE reinforced with granite micro particles for sustainable low-impact body armor systems
Discover Materials,
Год журнала:
2025,
Номер
5(1)
Опубликована: Апрель 23, 2025
Язык: Английский
Influence of Silicate Modulus and Eggshell Powder on the Expansion, Mechanical Properties, and Thermal Conductivity of Lightweight Geopolymer Foam Concrete
Materials,
Год журнала:
2025,
Номер
18(9), С. 2088 - 2088
Опубликована: Май 2, 2025
To
address
the
demands
of
low-carbon
era,
this
study
proposed
a
solution
by
using
eggshell
powder
(ESP),
fly
ash,
and
ground
granulated
blast
furnace
slag
together
with
alkaline
in
preparation
lightweight
geopolymer
foam
concrete
(LWGFC).
The
aim
is
to
investigate
influence
replacing
precursor
materials
5–20%
ESP
on
expansion
behavior,
physical,
mechanical
characteristics,
thermal
conductivity
LWGFC.
Additionally,
examines
effect
varying
silicate
modulus
(SiO2/Na2O
ratios
1.0,
1.25,
1.5)
properties
Incorporating
from
5%
20%
constant
SiO2/Na2O
ratio
reduced
initial
setting
time,
while
high
controlled
time
volume.
decreased
porosity
enhanced
compressive
strength
LWGFC
but
increased
conductivity.
inclusion
more
than
10%
content
negatively
affected
strength;
however,
can
mitigate
detrimental
effect.
optimal-content
mixtures
1.0
was
about
0.84
W/m·K,
which
2.1%
lower
1.25
18.6%
those
1.5.
High-content
had
density
1707
kg/m3,
0.97
18.9
MPa
at
low
ratio.
Finally,
LWGFC,
along
use
an
appropriate
modulus,
resulted
improved
development
decreasing
porosity.
Язык: Английский
Sustainable earthen plasters: surface resistance enhancement via thermal treatments
Journal of Building Engineering,
Год журнала:
2025,
Номер
unknown, С. 112867 - 112867
Опубликована: Май 1, 2025
Язык: Английский
Experimental and Machine Learning-Based Analysis of Oxide Ratios in Alumina-Silicate Geopolymer Formation
Iranian Journal of Science and Technology Transactions of Civil Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 8, 2025
Язык: Английский
High-performance glass classification using advanced machine learning and deep learning algorithms with a comprehensive feature analysis
AIP Advances,
Год журнала:
2025,
Номер
15(5)
Опубликована: Май 1, 2025
Glass
classification
with
accuracy
is
highly
required
in
construction,
automotive,
and
electronics
industries,
where
material
properties
like
transparency
strength
are
vital.
Traditional
practices,
though
effective,
time-consuming
non-scalable.
This
paper
proposes
a
solution
based
on
Machine
Learning
Deep
to
automate
scale
up
the
of
glass
classification.
The
work
uses
dataset
214
samples
nine
chemical
physical
properties.
Exploratory
Data
Analysis
provides
significant
patterns
verifies
pre-determined
classes
through
clustering
techniques
Gaussian
Mixture
Models.
Advanced
learning
algorithms
Random
Forest
(RF),
XGBoost,
Support
Vector
Machines,
Bidirectional
Long
Short-Term
Memory
(BiLSTM)
networks
applied
for
Findings
prove
RF
XGBoost
provide
highest
accuracy,
BiLSTM
be
best
recognizing
complex
data
patterns.
Feature
importance
analysis
pinpoints
features
identifies
magnesium
barium
among
those
used
distinguish
between
types.
detailed
evaluation
highlights
potential
AI-based
methods
revolutionize
classifying
increased
efficacy,
details.
Язык: Английский
Optimizing sustainable self-compacting mortar properties using natural pozzolan and glass powder as cementitious materials: a combination of artificial neural networks and statistical mixture design approach
Asian Journal of Civil Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 14, 2025
Язык: Английский