Development of a Hybrid Model for Risk Assessment and Management in Complex Road Infrastructure Projects
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(5), P. 2736 - 2736
Published: March 4, 2025
During
the
execution
of
road
infrastructure
projects,
project
managers
face
significant
challenges,
including
financial,
technical,
regulatory,
and
operational
risks.
More
than
90%
projects
have
incurred
costs
exceeding
initial
estimates,
impacting
both
completion
timelines
efficiency
infrastructure.
Effectively
assessing
managing
these
risks
is
crucial
for
improving
outcomes
ensuring
sustainability
investments.
To
address
this
study
developed
a
hybrid
model
risk
assessment
management
in
projects.
The
quantifies
across
seven
key
categories:
Design,
External,
Resource,
Employer,
Contractor,
Engineer,
Project,
based
on
three
primary
input
factors:
Environment
coefficient,
Contractual
Design
coefficient.
Initially,
various
machine
learning
models,
linear
regression,
Random
Forest,
Gradient
Boosting,
Stacking
Models,
neural
networks,
were
applied
to
assess
predictions.
However,
due
specific
nature
dataset,
models
did
not
achieve
satisfactory
predictive
accuracy.
As
result,
fuzzy
logic
systems
(Mamdani
Sugeno)
employed,
demonstrating
superior
performance
modeling
occurrence
probabilities.
Comparative
analysis
between
two
approaches
revealed
that
Sugeno
provided
most
accurate
findings
highlight
benefits
applying
complex
providing
structured
framework
enhancing
decision-making
processes.
This
provides
methodology
accurately
predicting
safety,
efficiency,
long-term
sustainability.
Language: Английский
Predicting Extension of Time and Increasing Contract Price in Road Infrastructure Projects Using a Sugeno Fuzzy Logic Model
Mathematics,
Journal Year:
2024,
Volume and Issue:
12(18), P. 2852 - 2852
Published: Sept. 13, 2024
Road
infrastructure
plays
a
crucial
role
in
the
development
of
countries,
significantly
influencing
economic
growth,
social
progress,
and
environmental
sustainability.
Major
projects
are
frequently
challenged
by
substantial
risks
uncertainties,
leading
to
delays,
budget
overruns,
compromised
quality.
These
issues
can
undermine
viability
efficiency
projects,
making
effective
risk
management
essential
for
minimizing
negative
impacts
ensuring
project
success.
For
these
reasons,
study
was
conducted
using
Sugeno
fuzzy
logic
system
applied
completed
projects.
The
resulting
model
is
based
on
10
characteristics
provides
highly
accurate
predictions
Extension
Time
(EoT)
Increasing
Contract
Price
(ICP).
By
utilizing
this
model,
be
improved
through
more
forecasting
potential
delays
cost
overruns.
high
precision
enables
better
assessment
proactive
decision-making,
allowing
managers
implement
targeted
strategies
mitigate
optimize
outcomes.
Language: Английский
A Fuzzy Inertia-Based Virtual Synchronous Generator Model for Managing Grid Frequency Under Large-Scale Electric Vehicle Integration
Yajun Jia,
No information about this author
Zhijian Jin
No information about this author
Processes,
Journal Year:
2025,
Volume and Issue:
13(1), P. 287 - 287
Published: Jan. 20, 2025
The
rapid
proliferation
of
EVs
has
ushered
in
a
transformative
era
for
the
power
industry,
characterized
by
increased
demand
volatility
and
grid
frequency
instability.
In
response
to
these
challenges,
this
paper
introduces
novel
approach
that
combines
fuzzy
logic
with
adaptive
inertia
control
improve
stability
grids
amidst
large-scale
electric
vehicle
(EV)
integration.
proposed
methodology
not
only
adapts
varying
charging
scenarios
but
also
strikes
balance
between
steady-state
dynamic
performance
considerations.
This
research
establishes
solid
theoretical
foundation
inertia-adaptive
virtual
synchronous
generator
(VSG)
concept
pioneering
inertia-based
VSG
methodology.
Additionally,
it
incorporates
output
scaling
factors
enhance
robustness
adaptability
strategy.
These
contributions
offer
valuable
insights
into
evolving
landscape
strategies
provide
pragmatic
solution
pressing
challenges
arising
from
integration
EVs,
ultimately
fostering
resilience
sustainability
contemporary
systems.
Finally,
simulation
results
illustrate
new
method
is
effective
superior
advantages
over
traditional
droop
strategies.
Specifically,
reduces
maximum
change
25%
during
load
transitions,
peak
variation
0.15
Hz
compared
0.2
VSG.
Language: Английский
A Takagi–Sugeno fuzzy controller for minimizing cancer cells with application to androgen deprivation therapy
Priya Dubey,
No information about this author
Surendra Kumar,
No information about this author
Subhendu Kumar Behera
No information about this author
et al.
Healthcare Analytics,
Journal Year:
2023,
Volume and Issue:
4, P. 100277 - 100277
Published: Nov. 2, 2023
Androgen
deprivation
therapy
(ADT)
is
frequently
used
to
treat
prostate
cancer
which
a
widespread
disease
having
very
low
survival
rate.
A
prolonged
course
of
ADT
can
increase
toxicity
and
drug
resistance.
This
study
proposes
an
adaptive
combining
chemotherapy
or
immunotherapy
with
the
discontinuation
hormone
overcome
these
obstacles.
The
super-twisting
sliding
mode
control
(STSMC)
algorithm
found
be
one
effective
approach
as
model
for
obtaining
suitable
dosage
adaptively.
primary
objective
rapidly
reduce
number
cells
duration
exposure.
Takagi–Sugeno
fuzzy
controller-based
active
introduced,
it's
performance
compared
STSMC
algorithm.
While
maintaining
global
asymptotic
stability,
controller
reduces
six
months.
controllers
are
implemented
utilizing
linear
matrix
inequality
(LMI)
yet
another
LMI
(YALMIP)
toolset
MATLAB,
their
efficacy
validated
MATLAB
Simulink
simulations.
presents
novel
improve
treatment
outcomes
by
integrating
nonlinear
algorithms
strategies
minimize
exposure,
thereby
improving
patient
in
management.
Language: Английский
Sugeno Fuzzy Personality Prediction System: An Approach to Overcoming Psychological Measurement Uncertainty
Nadindra Dwi Ariyanta,
No information about this author
Anik Nur Handayani
No information about this author
Indonesian Journal of Data and Science,
Journal Year:
2024,
Volume and Issue:
5(3), P. 216 - 228
Published: Dec. 31, 2024
Personality
prediction
is
a
significant
field
in
psychological
measurement,
yet
it
faces
challenges
due
to
data's
ambiguous
and
uncertain
nature.
This
study
aims
develop
Sugeno-based
fuzzy
logic
system
for
predicting
personality
types
according
the
Myers-Briggs
Type
Indicator
(MBTI).
The
dataset
includes
synthetic
data,
incorporating
age,
introversion,
sensing,
thinking,
judging.
fuzzification
process
converts
crisp
input
values
into
variables,
which
are
then
processed
using
predefined
rules
generate
predictions.
defuzzification
step
yields
outputs
corresponding
MBTI
types,
demonstrating
system's
ability
handle
uncertainty
ambiguity
effectively.
Implementation
evaluation
were
conducted
Python
LabVIEW,
revealing
satisfactory
performance
with
low
error
rate
of
0.445.
highlights
potential
logic,
particularly
Sugeno
method,
enhancing
accuracy
adaptability
prediction,
contributing
applications
education,
human
resource
management,
personalized
digital
services.
Language: Английский
An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory
Tokpa Braxton Ferguson
No information about this author
International Journal of Enterprise Modelling,
Journal Year:
2023,
Volume and Issue:
17(2), P. 14 - 25
Published: May 30, 2023
This
research
presents
an
integrated
approach
for
fuzzy
rule
generation
in
dataset
classification
by
combining
hybrid
grid
partitioning
and
rough
set
theory.
The
objective
is
to
enhance
the
accuracy
interpretability
of
models.
leverages
achieve
localized
generation,
capturing
local
characteristics
patterns
within
different
regions
feature
space.
Furthermore,
theory
applied
attribute
reduction,
identifying
most
relevant
features
reducing
complexity
problem.
generated
rules
provide
interpretable
understandable
that
facilitate
domain
expert
interpretation.
contributes
field
proposing
a
comprehensive
framework
improves
both
classification.
findings
demonstrate
effectiveness
approach,
although
certain
limitations
exist.
Future
should
focus
on
parameter
selection,
scalability
challenges,
applicability
diverse
problem
domains.
promising
methodology
enhancing
classification,
with
potential
applications
various
domains
where
accurate
models
are
crucial.
Language: Английский
Integrating hybrid grid partition and rough set method for fuzzy rule generation: a novel approach for accurate dataset classification
Luke Joseph,
No information about this author
Meiser Llywellenie O'Leary,
No information about this author
Bisani Zagré
No information about this author
et al.
International Journal of Enterprise Modelling,
Journal Year:
2023,
Volume and Issue:
17(2), P. 35 - 45
Published: May 30, 2023
Accurate
dataset
classification
is
a
critical
task
in
various
domains,
and
combining
different
methodologies
can
enhance
performance.
This
research
presents
novel
approach
that
integrates
Hybrid
Grid
Partition
Rough
Set
methods
for
fuzzy
rule
generation,
aiming
to
improve
accuracy
interpretability
classification.
The
proposed
leverages
discretize
continuous
attributes
attribute
reduction
identify
essential
attributes,
enabling
accurate
while
handling
uncertainty
imprecision.
generated
rules
provide
interpretability,
aiding
decision-making
processes
providing
insights
into
factors.
approach's
robustness
generalization
capabilities
are
demonstrated
through
experiments
on
diverse
datasets,
indicating
its
potential
applicability
real-world
scenarios.
However,
limitations
such
as
the
absence
of
specific
evaluation
metrics
need
further
validation
larger
datasets
acknowledged.
Overall,
this
contributes
by
offering
integrated
highlighting
areas
future
investigation
refinement
Language: Английский
A Review of Interval Valued Type 2 Fuzzy Rule-Based Classifiers
Published: July 9, 2023
This
paper
presents
a
review
of
21
past
works
on
developing
interval-valued
type-2
fuzzy
rule-based
classifiers
(IVT2FRBC)
for
various
applications.
Special
attention
is
paid
to
two
major
topics:
design
and
performance
evaluation.
The
first
topic
involves
decisions
made
pertaining
every
relevant
component
IVT2FRBC
such
as
rule
structure,
number
input
variables/features,
type
&
membership
functions
each
variable,
model
construction
method,
tuning,
so
on.
second
includes
measures
used
evaluate
classifier's
performance,
compared,
statistical
tests
carried
out
describe
the
differences
between/among
classifiers.
Based
review,
observations
are
their
soundness
usefulness.
Language: Английский