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Pavement forecasting models
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Pavement forecasting models
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Pavement forecasting models
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Description
Title
Pavement
forecasting
models
Subject
TE251.5.C46
2008
;
Pavements--Performance--Ohio--Forecasting.
;
Markov
processes.
;
OTP
1.23/7:2008-03
Description
"
March
2008.
";
Includes
bibliographical
references.
;
Final
report.
;
Performed
in
cooperation
with
U.S
.
Federal
Highway
Administration
, by
University
of
Toledo
Dept
. of
Civil
Engineering
, for
Ohio
Dept
. of
Transportation
Office
of
Research
and
Development
under
state
job
no.
;
Harvested
from the
web
on
2/3/09
Creator
Chou
,
Eddie
.
Publisher
Ohio
Department
of
Transportation
;
Available
through
the
National
Technical
Information
Service
Contributors
Pulugurta
,
Haricharan.
;
Datta
,
Debargha.
;
United
States
.
Federal
Highway
Administration.
;
University
of
Toledo
.
Dept
. of
Civil
Engineering.
;
Ohio
.
Dept
. of
Transportation
.
Office
of
Research
and
Development
.
Type
Text
Language
eng
Relation
Also
available
online
from the
Dept.
of
Transportation's
Web
site
;
Adobe
Acrobat
Reader
required.
;
http://www2.dot.state.oh.us/research/2008/Pavement/134148-FinalReport.pdf;
http://worldcat.org/oclc/228028463/viewonline
Description-Abstract
The
primary
objectives
of this
study
were to
develop
models
to
forecast
future
pavement
conditions
and to
determine
remaining
service
life
of
pavements
based
on the
forecasted
conditions
.
Based
on
available
data
in the
ODOT
pavement
database
,
which
contains
the
condition
history
of
each
pavement
section
,
along
with its
location
,
year
of
construction
,
thickness
,
materials
used
,
climate
, and
rehabilitation
records
,
individual
regression
,
family
regression
, and
Markov
probabilistic
models
were
developed
. For the
latter
two
models
,
pavements
were
first
grouped
into "
families
" with
similar
characteristics
,
based
on
pavement
type
,
priority
,
District
location
, and
past
performance
.
Forecasting
models
were then
developed
for
each
such
"
family.
" The
developed
models
were
evaluated
by
comparing
the
predicted
conditions
with the
actual
observed
conditions
for the
five
year
period
between
2001
and
2005
. The
Markov
model
was
found
to have the
highest
overall
prediction
accuracy
among
all
the
models
evaluated
, and
it
can
also
predict
future
distresses
in
addition
to the
PCR
values
. As a
result
of this
study
,
ODOT
can
forecast
future
pavement
conditions
and
estimate
the
remaining
service
life
of
pavements
.
Future
rehabilitation
needs
can
also be
determined
.
Such
capabilities
will
significantly
benefit
planning
and
management
decision-makings
at
both
project
and
network
levels
.
Date-Issued
2008]
Format-Extent
1
v
.
(various
pagings)
:
ill.
,
charts
;
28
cm
.
Format-Medium
Government
Publication
OCLC number
228028463
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