Microstructural behavior and explainable machine learning aided mechanical strength prediction and optimization of recycled glass-based solid waste concrete
Case Studies in Construction Materials,
Journal Year:
2025,
Volume and Issue:
unknown, P. e04305 - e04305
Published: Jan. 1, 2025
Language: Английский
AI-driven Modeling for the Optimization of Concrete Strength for Low-Cost Business Production in the USA Construction Industry
Engineering Technology & Applied Science Research,
Journal Year:
2025,
Volume and Issue:
15(1), P. 20529 - 20537
Published: Feb. 2, 2025
The
need
to
develop
ecologically
friendly
sustainable
building
materials
is
made
apparent
by
the
worldwide
construction
industry's
substantial
contribution
global
greenhouse
gas
emissions.
use
of
supplemental
in
concrete
one
potential
solution
lessen
environmental
footprint.
Thus,
purpose
this
work
Machine
Learning
(ML)
algorithms
forecast
and
create
an
empirical
formula
for
Compressive
Strength
(CS)
with
materials.
Six
distinct
ML
models—XGBoost,
Linear
Regression,
Decision
Tree,
k-Nearest
Neighbors,
Bagging,
Adaptive
Boosting—were
trained
tested
using
a
dataset
that
included
359
experimental
data
varying
mix
proportions.
most
significant
factors
used
as
input
parameters
are
cement,
aggregates,
water,
superplasticizer,
silica
fume,
ambient
curing,
material.
Several
statistical
measures,
such
Mean
Absolute
Error
(MAE),
coefficient
determination
(R2),
Square
(MSE),
were
evaluate
models.
XGBoost
model
outperformed
other
models
R2
values
0.99
at
training
stage.
To
ascertain
how
affected
outcome,
feature
importance
analysis
Shapely
Additive
exPlanations
(SHAP)
was
conducted.
It
demonstrated
curing
age
cement
type
significantly
strength
high
SHAP
values.
By
eliminating
procedures,
reducing
demand
labor
resources,
increasing
time
efficiency,
offering
insightful
information
enhancing
manufacturing
concrete,
research
advances
low-cost
production
USA
industry.
Language: Английский
Capability readiness model for green design practices for affordable housing delivery in Ghana
International Journal of Housing Markets and Analysis,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 13, 2025
Purpose
Creating
green
design
capability
readiness
has
become
an
emerging
necessity
toward
increasing
sustainable
performance.
However,
the
understanding
of
markers
for
housing
delivery
is
lacking.
The
purpose
this
study
to
highlight
a
model
affordable
delivery.
Design/methodology/approach
Through
use
self-determination
theory
and
Technology–Organization–Environment
framework,
comprehensive
review
related
literature
revealed
23
indicators
on
motivational,
technological,
organizational
environmental
practices
Adopting
deductive
design,
questionnaire
was
developed
from
these
survey
practitioners
with
knowledge
experience
in
sustainability
supply
chain
through
purposive
snowballing
sampling.
Mean
score
analysis
fuzzy
synthetic
evaluation
were
subsequently
used
develop
model.
Findings
This
affirmed
top
each
markers.
accounted
28%,
29%,
17.7%
25.3%
Accordingly,
technological
motivational
had
greatest
contributions
followed
by
marker
being
least.
Practical
implications
findings
will
contribute
developing
right
motivations,
regulatory
factors
optimize
Ghana.
Originality/value
serves
as
valuable
resource
that
could
be
objectively
align
actions
gauge
performance
improvement
It
also
aid
benchmarking
potential
future
regulations,
policies
motivations
practices,
concepts
technologies
Language: Английский
An advanced exploration of technological functionalities addressing risk factors in earthmoving equipment operation on construction sites: a systematic literature review
Smart and Sustainable Built Environment,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 24, 2024
Purpose
This
paper
aims
to
analyze
the
current
state
of
technological
advancements
research
in
addressing
diverse
risk
factors
involved
earthmoving
equipment
operations
through
Rasmussen's
(1997)
management
framework.
It
examines
how
existing
technologies
capture,
manage
and
disseminate
information
across
various
levels
safety
by
defining
their
core
functionalities.
The
highlights
gaps
solutions
regarding
flow
emphasizes
need
for
an
integrated
approach
enhance
holistic
capable
capturing
risks
different
management.
Design/methodology/approach
employs
a
multistep
approach.
Initially,
functionalities
were
identified
systematic
review
scholarly
works.
Subsequently,
social
network
analysis
(SNA)
Pareto
applied
evaluate
determine
importance
improving
them.
Findings
findings
highlight
multilevel
approaches
that
expand
address
all
combination
focuses
primarily
on
on-site
monitoring,
congested
work
sites,
site
layout/path
planning,
utility
problems,
training,
blind
spot
visibility.
Site
monitoring
warning
systems,
supported
sensors
computer
vision
(CV),
are
pivotal
identifying
enabling
data-driven
However,
workforce-level
cognitive
(W1-W6),
which
influence
behavior,
remain
underexplored
enhancing
functionality
anticipation
response
during
operation.
Prevention
is
function
solutions,
emphasizing
human
such
as
sources
hazards
operations.
Learning:
AI
IoT
systems
key
future
development,
when
grounded
ontology-based
knowledge
earthwork,
they
gain
structured
types,
interactions
earthwork
activities.
enhances
capabilities
these
capture
complex
between
hazard
(human
equipment),
supporting
comprehensive
Originality/value
elucidates
require
more
approach—grounded
understanding
technologies—to
effectively
Rasmussen
should
not
only
isolated
but
also
ensure
continuous
multiple
Language: Английский