XIX-B1, 2012
it are attributed to
uction.
uates the condition
and integrates the
[. This MCI varies
MCI indicates that
mpares the results
| MCI.
ement section are
dicate F-value and
he horizontal axis
ical axis shows the
Figure 3 has one
value of that point
ump points are in
also dispersed to
? section in Figure
large number of F-
d point.
[CI evaluated by
y Chow-test. It is
between MCI and
n section range
rvey is 50m). For
| by illustrating an
ximum of F-value
s which has bump
een the maximum
ents the average
amaged point. On
hows the relation
face and MCI, the
th MCI. From the
logy proposed in
ent conditions by
nicro (localized)
late the localized
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B1, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
3599 F-value(max)
F-value(max)
MCI
=
o
O |^ NU P U AN x D
MCI
3895 rate of bump point === MCI
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
rate of bump point
1-0
1-5500
1-11000
1-16500
1-22000
1-27500
1-33000
1-38500
1-44000
1-49500
1-55000
1-60500
=
o
O ^o mu 5 tu Oo «0 uv
MCI
8-23500
10-26000
10-31500
10-37000
000 NSS 90 o0
10-
Figure 6. Comparison between Chow-Test and MCI
# MMS 2 Fwaue c F-value(criterion}
3 E EI 0 1 2 3
F-value
Figure 3. Result(1)
* MMS Q Fvaue c F-value(criterion)
3 E a 0 1 2 3
Figure 4. Result(2)
damage point which was not evaluated by road condition survey
(MCI value).
5. CONCLUSIONS
This research proposed the methodology to find the damaged
pavement section effectively using 3D point clouds as a new
approach for conducting inspection of the road maintenance
work. Also, the case study represented that the bump on surface
can be extracted and the validity of this method was described
by comparing the result of pavement condition survey and
Chow-test.
It is a future subject to apply on the maintenance work of actual
pavement using the methodology proposed in the current study.
When this method will be applied to actual maintenance work,
89
$ MMS O F-value mm F-valuelcriterion)
a à tan od 9 1 2 3
F-value
Figure 5. Result(3)
it would be necessary to estimate the cost for acquiring 3D point
clouds from MMS and analyze the validity. However, when 3D
point clouds are measured for several objectives such as
updating of mapping data and urban planning, it is not
necessary to acquire new 3D point clouds only for the road
maintenance work. By using the already acquired data, it is
possible to reduce inspection cost sharply. In this way, it is also
an important issue that the advantage by sharing 3D point
clouds is clarified and logic to use the data is well prepared.
REFERENCES
Madanat, S. (1993) Incorporating inspection decisions in
pavement management, Transportation Research, Part B,
Vol.27B, pp.425-438.
Madanat, S. and Ben-Akiva, M. (1994) Optimal inspection
and repair policies for infrastructure facilities,
Transportation Science, Vol.28, pp.55-62.
Aoki, K., Mori, H. and Okada, K. (2011) Effect of Pavement
Service Level upon Operation Timing of Periodical
Inspection Policy, 7th International Conference of Pavement
Technology.
Morimune, K. (1999) Econometrics, Toyo Keizai INC.
