Innovative Infrastructure Solutions,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: Dec. 20, 2024
Abstract
This
research
examines
the
role
of
two-phase
flow
formation
in
crown
control
performance
and
orifice
Morning
Glory
spillways.
The
impact
an
aerator
was
investigated
through
3D
simulations
pattern
within
spillway,
focusing
on
optimal
installation
positions
to
mitigate
negative
pressure
prevent
cavitation.
ANSYS
Fluent
software
employed
for
simulations.
Results
revealed
significant
pressures
vertical
shaft,
with
impacting
only
a
small
portion
this
area.
Geometric
adjustments
led
reduction
around
connection
area,
shifting
them
toward
beginning
horizontal
shaft.
Additionally,
these
modifications
resulted
50%
decrease
final
design
demonstrated
81.6
cavitation
index
elbow
respectively,
compared
initial
design.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: March 28, 2025
The
accurate
prediction
of
the
strength
enhancement
ratio
([Formula:
see
text])
and
strain
(εcc/εco)
in
FRP-wrapped
elliptical
concrete
columns
is
crucial
for
optimizing
structural
performance.
This
study
employs
machine
learning
(ML)
techniques
to
enhance
accuracy
reliability.
A
dataset
181
samples,
derived
from
experimental
studies
finite
element
modeling,
was
utilized,
with
a
70:30
train-test
split
(127
training
samples
54
testing
samples).
Four
ML
models:
Decision
Tree
(DT),
Adaptive
Boosting
(ADB),
Stochastic
Gradient
(SGB),
Extreme
(XGB)
were
trained
optimized
using
Bayesian
Optimization
refine
their
hyperparameters
improve
performance.Results
demonstrate
that
SGB
achieved
best
performance
predicting
[Formula:
text],
an
R2
0.850,
lowest
RMSE
(0.190),
highest
generalization
capability,
making
it
most
reliable
model
predictions.
For
(εcc/εco),
XGB
outperformed
other
models,
achieving
0.779
(2.162),
indicating
better
balance
between
accuracy,
generalization,
minimal
overfitting.
DT
ADB
exhibited
lower
predictive
performance,
higher
residual
errors
capacity.
Furthermore,
Shapley
Additive
exPlanations
analysis
identified
FRP
thickness-elastic
modulus
product
(tf
×
Ef)
compressive
as
influential
features
impacting
both
ratios.
To
facilitate
real-world
applications,
interactive
graphical
user
interface
developed,
enabling
engineers
input
ten
parameters
obtain
real-time
Knowledge-Based Engineering and Sciences,
Journal Year:
2024,
Volume and Issue:
5(3), P. 43 - 60
Published: Dec. 31, 2024
Accurately
estimating
seepage
losses
from
unlined
and
lined
trapezoidal
channels
is
essential
for
effective
water
management,
especially
in
water-scarce
regions.
This
study
combined
experimental
numerical
approaches
to
evaluate
losses,
focusing
on
the
influence
of
channel
geometry
liner
properties,
including
hydraulic
conductivity
(KL)
thickness
(tL).
Firstly,
a
physical
model
was
constructed,
materials
were
prepared,
testing
procedures
performed
estimate
soil
cement
mixture.
Secondly,
five-channel
geometries
adjusted
experiments.
Finally,
Slide2
results
compared
with
data.
Results
revealed
that
accurately
estimated
high
determination
coefficient
(R2)
0.99
low
root-mean-squared-error
(RMSE)
values
2.85
0.03
cm3
s-1,
respectively.
For
channels,
increased
larger
bed
width-to-water
depth
ratios
due
extended
wetted
perimeter.
lining
ineffective
when
KL
exceeded
0.01,
while
0.05
increase
tL
reduced
by
15%.
Furthermore,
design
charts
equations
developed
considering
dimensions,
conductivity,
thickness.
Innovative Infrastructure Solutions,
Journal Year:
2025,
Volume and Issue:
10(5)
Published: April 28, 2025
Abstract
Concrete
Compressive
Strength
(CCS)
is
a
critical
parameter
in
structural
engineering,
influencing
durability,
safety,
and
load-bearing
capacity.
This
study
explores
the
classification
of
CCS
using
hybrid
Machine
Learning
(ML)
techniques
an
interactive
Graphical
User
Interface
(GUI).
Advanced
ML
algorithms:
Random
Forest
(RF),
Adaptive-Boosting
(AdaBoost),
Extreme-Gradient-Boosting
(XGBoost),
Light-Gradient
Boosting
(LightGBM),
Categorical-Boosting
(CatBoost)
were
applied
to
categorize
strength
into
Low,
Normal,
High
classes.
The
dataset,
comprising
1298
samples,
was
split
80%
training
20%
testing
for
evaluation.
Hyperparameter
tuning
Bayesian
Optimization
with
fivefold
stratified
cross-validation,
resulting
greatly
improved
model’s
performance.
Results
showed
that
LightGBM
achieved
highest
accuracy,
scores
0.931
(Low),
0.865
(Normal),
0.935
(High),
corresponding
area
under
curve
values
0.967,
0.938,
0.981.
CatBoost
also
performed
well,
particularly
Normal
classes,
while
XGBoost
slight
overfitting
class.
RF
AdaBoost
had
acceptable
performance
but
struggled
boundary
cases.
To
interpret
model
predictions,
SHapley-Additive-exPlanations
(SHAP)
analysis
used.
Curing
duration
cement
content
most
influential
factors
across
all
water
superplasticizer
played
secondary
roles.
Coarse
aggregate
became
more
significant
High-Strength
(HSC).
A
GUI
developed
allow
practitioners
input
data
receive
real-time
classifications,
bridging
gap
between
machine
learning
practical
applications
concrete
design.
Innovative Infrastructure Solutions,
Journal Year:
2024,
Volume and Issue:
10(1)
Published: Dec. 20, 2024
Abstract
This
research
examines
the
role
of
two-phase
flow
formation
in
crown
control
performance
and
orifice
Morning
Glory
spillways.
The
impact
an
aerator
was
investigated
through
3D
simulations
pattern
within
spillway,
focusing
on
optimal
installation
positions
to
mitigate
negative
pressure
prevent
cavitation.
ANSYS
Fluent
software
employed
for
simulations.
Results
revealed
significant
pressures
vertical
shaft,
with
impacting
only
a
small
portion
this
area.
Geometric
adjustments
led
reduction
around
connection
area,
shifting
them
toward
beginning
horizontal
shaft.
Additionally,
these
modifications
resulted
50%
decrease
final
design
demonstrated
81.6
cavitation
index
elbow
respectively,
compared
initial
design.
