Rapid Evaluation Method to Vertical Bearing Capacity of Pile Group Foundation Based on Machine Learning
Sensors,
Год журнала:
2025,
Номер
25(4), С. 1214 - 1214
Опубликована: Фев. 17, 2025
With
the
continuous
increase
in
bridge
lifespans,
rapid
check
and
evaluation
of
vertical
bearing
capacity
for
pile
foundations
existing
bridges
have
been
greater
demand.
The
usual
practice
is
to
carry
out
compression
tests
under
static
loads
order
obtain
accurate
ratio
dynamic
stiffness.
However,
it
difficult
costly
conduct
situ
experiments
each
foundation.
Herein,
a
method
measure
proposed.
Firstly,
3D-bearing
cap-pile
group-soil
interaction
model
was
established
simulate
test
foundation
that
subject
loads,
then
numerical
results
were
validated
by
loading
on
an
abandoned
pier
with
same
group
foundation;
dataset
machine
learning
constructed
using
results,
finally,
could
be
predicted
rapidly.
show
following
outcomes:
can
effectively
foundations;
intelligent
prediction
based
predict
stiffness
thus
rapidly
evaluate
residual
designed
ultimate
capacity,
allowing
nondestructive
testing
bridges.
Язык: Английский
Load-deformation prediction of bored piles using sequential soil profile encoding with transformer architecture: A study of Bangkok subsoil
Expert Systems with Applications,
Год журнала:
2025,
Номер
unknown, С. 127085 - 127085
Опубликована: Фев. 1, 2025
Язык: Английский
Borehole Breakout Prediction Based on Multi-Output Machine Learning Models Using the Walrus Optimization Algorithm
Applied Sciences,
Год журнала:
2024,
Номер
14(14), С. 6164 - 6164
Опубликована: Июль 15, 2024
Borehole
breakouts
significantly
influence
drilling
operations’
efficiency
and
economics.
Accurate
evaluation
of
breakout
size
(angle
depth)
can
enhance
strategies
hold
potential
for
in
situ
stress
magnitude
inversion.
In
this
study,
borehole
is
approached
as
a
complex
nonlinear
problem
with
multiple
inputs
outputs.
Three
hybrid
multi-output
models,
integrating
commonly
used
machine
learning
algorithms
(artificial
neural
networks
ANN,
random
forests
RF,
Boost)
the
Walrus
optimization
algorithm
(WAOA)
techniques,
are
developed.
Input
features
determined
through
literature
research
(friction
angle,
cohesion,
rock
modulus,
Poisson’s
ratio,
mud
pressure,
radius,
stress),
501
related
datasets
collected
to
construct
dataset.
Model
performance
assessed
using
Pearson
Correlation
Coefficient
(R2),
Mean
Absolute
Error
(MAE),
Variance
Accounted
For
(VAF),
Root
Squared
(RMSE).
Results
indicate
that
WAOA-ANN
exhibits
excellent
stable
prediction
performance,
particularly
on
test
set,
outperforming
single-output
ANN
model.
Additionally,
SHAP
sensitivity
analysis
conducted
model
reveals
maximum
horizontal
principal
(σH)
most
influential
parameter
predicting
both
angle
depth
breakout.
Combining
results
studies
analyses
conducted,
considered
be
an
effective
size.
Язык: Английский
Enhancing Sustainability of Building Foundations with Efficient Open-End Pile Optimization
Sustainability,
Год журнала:
2024,
Номер
16(16), С. 6880 - 6880
Опубликована: Авг. 10, 2024
Optimizing
open-end
piles
is
crucial
for
sustainability
as
it
minimizes
material
consumption
and
reduces
environmental
impact.
By
improving
construction
efficiency,
less
steel
needed,
reducing
the
carbon
footprint
associated
with
production
transportation.
Improved
pile
performance
also
results
in
more
durable
structures
that
require
frequent
replacement
maintenance,
which
turn
saves
resources
energy.
This
paper
presents
a
parametric
study
on
optimal
designs
open-ended
sand,
presenting
novel
approach
to
directly
compute
using
CPT
results.
It
addresses
challenges
posed
by
soil
variability
layered
conditions,
optimization
model
accounting
interdependencies
among
length,
diameter,
wall
thickness
properties,
including
pile–soil
plug
system.
A
mixed-integer
OPEN-Pile
was
developed,
consisting
of
an
objective
function
mass
CO2
emissions.
The
constrained
set
design
geotechnical
conditions
corresponded
current
codes
practice
recommendations.
efficiency
developed
illustrated
two
case
studies.
In
Blessington
calculation
show
economical
environmentally
friendly
increase
diameter
than
length.
efficient
design,
ratio
between
calculated
at
upper
limit.
For
optimum
ratios
length
are
5,
50
250,
respectively.
profile,
decision
where
place
base
depends
resistance
cone
tip
individual
layers.
To
determine
layer
should
be
placed,
we
need
perform
given
data.
Язык: Английский
Legal Challenges and Responses to Artificial Intelligence-Assisted Decision-Making in the International Economic Law System
Applied Mathematics and Nonlinear Sciences,
Год журнала:
2024,
Номер
9(1)
Опубликована: Янв. 1, 2024
Abstract
Legal
judgment
prediction
is
becoming
a
research
hotspot
in
the
legal
field
as
an
important
artificial
intelligence-assisted
decision-making
tool
case
management,
which
able
to
predict
results.
In
this
paper,
data
from
2018
China
Law
Research
Cup
competition
gathered,
and
dataset
preprocessed
context
of
international
economic
law.
Then,
multi-task
model
for
verdict
proposed,
training
optimization
are
designed
using
CNN,
RNN,
LSTM
semantic
coding
layer.
The
proposed
paper
achieves
significant
improvement
8%
6%
accuracy
charging
task
sentence
task,
respectively.
outcome
prediction,
improved
by
14.6%
on
average
compared
feature
model-based
modeling
approach.
Язык: Английский