Case Studies in Construction Materials,
Journal Year:
2024,
Volume and Issue:
21, P. e03416 - e03416
Published: June 14, 2024
Performance
assessment
of
existing
building
structures,
especially
concrete
compressive
strength
assessment,
is
a
crucial
aspect
engineering
construction
for
most
industrialized
countries.
Non-destructive
testing
(NDT)
techniques
are
commonly
employed
to
assess
the
structures.
However,
methods
predicting
using
NDT
and
machine
learning
do
not
take
into
account
mix
proportion
design.
This
study
proposes
an
effective
method
predict
by
combining
tests
with
different
designs
curing
ages.
Specifically,
support
vector
regression
(SVR)
back
propagation
neural
network
(BPNN)
models
established.
Furthermore,
various
evaluation
indexes
utilized
model
performance.
To
construct
validate
prediction
models,
total
180
datasets
containing
specimens
ages
collected
from
research
literature.
The
results
show
that
coefficients
determination
(R2)
SVR
BPNN
test
set
86.0
%
86.7
without
considering
R2
higher
than
95
when
effects
design
age.
ranged
between
92
97
%.
All
better
those
model.
Consequently,
can
be
accurately
evaluate
during
structural
performance
buildings.
Mathematical Problems in Engineering,
Journal Year:
2021,
Volume and Issue:
2021, P. 1 - 15
Published: Feb. 5, 2021
The
main
objective
of
this
study
is
to
evaluate
and
compare
the
performance
different
machine
learning
(ML)
algorithms,
namely,
Artificial
Neural
Network
(ANN),
Extreme
Learning
Machine
(ELM),
Boosting
Trees
(Boosted)
considering
influence
various
training
testing
ratios
in
predicting
soil
shear
strength,
one
most
critical
geotechnical
engineering
properties
civil
design
construction.
For
aim,
a
database
538
samples
collected
from
Long
Phu
1
power
plant
project,
Vietnam,
was
utilized
generate
datasets
for
modeling
process.
Different
(i.e.,
10/90,
20/80,
30/70,
40/60,
50/50,
60/40,
70/30,
80/20,
90/10)
were
used
divide
into
assessment
models.
Popular
statistical
indicators,
such
as
Root
Mean
Squared
Error
(RMSE),
Absolute
(MAE),
Correlation
Coefficient
(R),
employed
predictive
capability
models
under
ratios.
Besides,
Monte
Carlo
simulation
simultaneously
carried
out
proposed
models,
taking
account
random
sampling
effect.
results
showed
that
although
all
three
ML
performed
well,
ANN
accurate
statistically
stable
model
after
1000
simulations
(Mean
R
=
0.9348)
compared
with
other
Boosted
0.9192)
ELM
0.8703).
Investigation
on
greatly
affected
by
training/testing
ratios,
where
70/30
presented
best
Concisely,
herein
an
effective
manner
selecting
appropriate
predict
strength
accurately,
which
would
be
helpful
phases
construction
projects.
