Mechanics of Advanced Materials and Structures,
Год журнала:
2023,
Номер
31(23), С. 5737 - 5759
Опубликована: Июнь 11, 2023
The
literature
is
deficit
in
predicting
the
axial
strength
(AS)
and
strain
of
carbon
fiber
reinforced
polymer
(CFRP)-wrapped
normal
concrete
(NSC)
high
(HSC)
compressive
members
using
machine
learning
techniques.
already
proposed
models
for
AS
CFRP-wrapped
NSC
were
developed
a
general
regression
analysis
technique
based
on
small
number
noisy
data
points
by
considering
limited
parameters
specimens.
Therefore,
there
need
refined
accurate
theoretical
model
capturing
members.
main
objective
current
study
to
develop
HSC
methods.
Two
different
approaches
are
employed
securing
present
study.
first
approach
technique,
second
one
employing
artificial
neural
networks
(ANN)
modeling.
testing
database
consists
results
364
subjected
loading.
accuracy
empirical
ANN
evaluated
compared
basis
results.
Three
statistical
indices
determine
performance
currently
presented
with
R2
=
0.984,
RMSE
0.112,
MAE
0.097
0.942,
1.211,
0.978
model.
suggested
0.90,
0.33,
2.45
0.80,
2.05,
5.34
evaluation
showed
that
more
effective
precise
than
ones
circular
IEEE Access,
Год журнала:
2023,
Номер
11, С. 42416 - 42430
Опубликована: Янв. 1, 2023
A
metaheuristic
approach
based
on
the
nature-inspired
and
well-known
Grey
Wolf
Optimization
algorithm
(GWO)
was
employed
in
this
study
to
design
an
for
retrieving
strong
designs
of
8×8
substitution
boxes
(S-boxes).
The
GWO
developed
as
a
novel
inspiration
from
grey
wolves
how
they
hunt.
ability
quickly
explore
search
space
near/optimal
feature
subsets
that
maximize
any
given
fitness
function
(in
consideration
its
distinctive
hierarchical
structure)
aids
construction
S-boxes
can
satisfy
required
criteria.
However,
when
tackling
optimization
problems,
may
experience
problem
premature
convergence.
Therefore,
variant
called
Crossover
Optimizer
(XGWO)
has
been
proposed
study.
performance
evaluated
using
numerous
cryptographic
metrics,
including
bijective
property,
bit
independence,
strict
avalanche,
linear
probability,
I/O
XOR
distribution
result
contrasted
with
couple
existing
S-box
creation
techniques.
Overall,
results
experiment
showed
suggested
had
adequate
features.
Engineering Reports,
Год журнала:
2023,
Номер
5(9)
Опубликована: Май 23, 2023
Abstract
Advanced
concrete
technology
is
the
science
of
efficient,
cost‐effective,
and
safe
design
in
civil
engineering
projects.
Engineers
designers
are
generally
faced
with
slightest
change
conditions
or
objectives
project,
which
makes
it
challenging
to
choose
optimal
among
several
ones.
Besides,
experimental
examination
all
them
requires
time
high
costs.
Hence,
an
efficient
approach
utilize
artificial
intelligence
(AI)
techniques
predict
optimize
real‐world
problems
technology.
Despite
large
body
publications
this
field,
there
few
comprehensive
surveys
that
conduct
scientometric
analysis.
This
paper
provides
a
state‐of‐the‐art
review
lists,
summarizes,
categorizes
most
widely
used
machine
learning
methods,
meta‐heuristic
algorithms,
hybrid
approaches
issues.
To
end,
457
considered
during
recent
decade
highlight
annual
trend/active
journals/top
researchers/co‐occurrence
key
title
words/countries'
participation/research
hotspots.
In
addition,
AI
classified
into
distinct
clusters
using
VOSviewer
clustering
visualization
identify
application
scope
their
relationship
through
link
strength.
The
findings
can
be
beacon
help
researchers
future
research
on
advanced
Mechanics of Advanced Materials and Structures,
Год журнала:
2023,
Номер
31(23), С. 5737 - 5759
Опубликована: Июнь 11, 2023
The
literature
is
deficit
in
predicting
the
axial
strength
(AS)
and
strain
of
carbon
fiber
reinforced
polymer
(CFRP)-wrapped
normal
concrete
(NSC)
high
(HSC)
compressive
members
using
machine
learning
techniques.
already
proposed
models
for
AS
CFRP-wrapped
NSC
were
developed
a
general
regression
analysis
technique
based
on
small
number
noisy
data
points
by
considering
limited
parameters
specimens.
Therefore,
there
need
refined
accurate
theoretical
model
capturing
members.
main
objective
current
study
to
develop
HSC
methods.
Two
different
approaches
are
employed
securing
present
study.
first
approach
technique,
second
one
employing
artificial
neural
networks
(ANN)
modeling.
testing
database
consists
results
364
subjected
loading.
accuracy
empirical
ANN
evaluated
compared
basis
results.
Three
statistical
indices
determine
performance
currently
presented
with
R2
=
0.984,
RMSE
0.112,
MAE
0.097
0.942,
1.211,
0.978
model.
suggested
0.90,
0.33,
2.45
0.80,
2.05,
5.34
evaluation
showed
that
more
effective
precise
than
ones
circular
