ANALYSIS OF FLOOD AFFECTED ROAD PRESERVATION PROGRAM USING THE MARKOV CHAIN MODEL
Roads are one of the most important supports in the development of a region. In order for roads to function properly, it is necessary to manage roads properly and correctly as part of road asset management. In recent years, floods have disrupted the road management system by significantly increas...
Saved in:
| Main Author: | |
|---|---|
| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/72040 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Roads are one of the most important supports in the development of a region. In
order for roads to function properly, it is necessary to manage roads properly and
correctly as part of road asset management. In recent years, floods have disrupted
the road management system by significantly increasing rehabilitation costs. The
intensity and frequency of the flooding that occurs is uncertain, for example
Bengkulu which was hit by floods in 2019 was also hit by another major flood after
high intensity rains in 2021 and 2022.
Pavement performance modeling is an important element of a pavement
management system. Nearly 95% of the service level of road pavement is related to
surface roughness, in other words surface roughness is the main variable that
greatly affects the serviceability of road pavement. In this study, an analysis of the
flooding’s effect, traffic, and maintenance history on the deterioration of pavement
conditions in the future was carried out. The analysis was carried out on all
national road sections in Bengkulu province. Road sections are categorized as
segments affected by flooding and segments not affected by flooding according to
the traffic category and maintenance history. The pavement condition parameters
used in this study are the IRI (International Roughness Index) and PCI (Pavement
Condition Index) values. Analysis of changes in pavement conditions in the future
using the Markov Chain model. Later a comparison will be made between
determining pavement conditions using random numbers from the LCG (Linear
Congruential Generator) and those with the highest probability. In addition, the
results of future pavement conditions with Markov Chain will be compared to the
results of pavement conditions according to IRMS V.3. The determination of the
treatment that will be carried out refer to the IRMS V.3 flexible pavement decision
tree.
Based on statistical analysis, it can be concluded that if handling is carried out, the
flooded segment and the segment not affected by floods have no difference in
pavement condition values. Meanwhile, if no action is taken, the flooded segment
and the segment not affected by floods will have different pavement conditions.
Based on the resulting transition probability matrix (MPT), traffic category
conditions affect the level of decrease or increase in pavement conditions where
high traffic has a higher rate of decline in pavement conditions than medium traffic
and low traffic. The conditions between the flooded and non-flooded segments are
also different in terms of changes in pavement conditions where the flooded
segment has a faster rate of decline than the non-flooded segment.
In the analysis of changes in pavement conditions in the future, the comparison of
the number of handling decisions given to Markov Chain using random numbers
and Markov Chain with high probability has little difference. Likewise, if the
Markov Chain has the highest probability compared to IRMS V.3, it also shows
different results, where the deterioration of pavement conditions, both IRI and PCI,
is faster so that more frequent handling is carried out than using IRMS V.3, which
tends to decrease road conditions more slowly.
|
|---|