The Prediction of Road Condition Value during Maintenance Based on Markov Process
The first step in the road handling effort is to survey to get an accurate road condition value to take correct action in implementing maintenance. As pavement performance is known to be probabilistic, various levels of uncertainty must be assumed. Modern pavement management methods are ineffective...
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IJASEIT
2024
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| Online Access: | http://eprints.uthm.edu.my/12529/1/J18021_cfdc59dea7ca9bdcb30a8006dbdd819e.pdf http://eprints.uthm.edu.my/12529/ |
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| author | Isradi, Muhammad Prasetijo, Joewono Rifai, Andri Irfan Andraiko, Heru Zhang, Guohui |
| author_facet | Isradi, Muhammad Prasetijo, Joewono Rifai, Andri Irfan Andraiko, Heru Zhang, Guohui |
| author_sort | Isradi, Muhammad |
| building | UTHM Library |
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| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The first step in the road handling effort is to survey to get an accurate road condition value to take correct action in implementing maintenance. As pavement performance is known to be probabilistic, various levels of uncertainty must be assumed. Modern pavement management methods are ineffective without an effective model to predict pavement performance. Discrete-time Markov chains are the most widely used probabilistic models, and examples from different countries worldwide can be found in pavement management systems. This research aims to predict the value of road conditions during maintenance and compare road assessments with real conditions during road maintenance using the IRI, SDI, and PCI methods using the Markov process. The analysis method used is to collect secondary data from related departments and carry out direct data collection in the field to obtain condition values based on IRI, SDI, and PCI to forecast by making a pavement condition prediction model based on the Markov process and then assessing road conditions by comparing the three index values with the slightest deviation value. The analysis showed that the average value of road conditions with the IRI indicator is 4.45, which is moderate, and the most negligible difference between the probability distribution of pavement condition prediction modeling and the actual survey results was the IRI (International Roughness Index)
method. This model is closest to the actual conditions during implementation, with a difference value of 5.7%. |
| format | Article |
| id | my.uthm.eprints-12529 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| publisher | IJASEIT |
| record_format | eprints |
| spelling | my.uthm.eprints-125292025-03-19T00:36:26Z http://eprints.uthm.edu.my/12529/ The Prediction of Road Condition Value during Maintenance Based on Markov Process Isradi, Muhammad Prasetijo, Joewono Rifai, Andri Irfan Andraiko, Heru Zhang, Guohui TA Engineering (General). Civil engineering (General) The first step in the road handling effort is to survey to get an accurate road condition value to take correct action in implementing maintenance. As pavement performance is known to be probabilistic, various levels of uncertainty must be assumed. Modern pavement management methods are ineffective without an effective model to predict pavement performance. Discrete-time Markov chains are the most widely used probabilistic models, and examples from different countries worldwide can be found in pavement management systems. This research aims to predict the value of road conditions during maintenance and compare road assessments with real conditions during road maintenance using the IRI, SDI, and PCI methods using the Markov process. The analysis method used is to collect secondary data from related departments and carry out direct data collection in the field to obtain condition values based on IRI, SDI, and PCI to forecast by making a pavement condition prediction model based on the Markov process and then assessing road conditions by comparing the three index values with the slightest deviation value. The analysis showed that the average value of road conditions with the IRI indicator is 4.45, which is moderate, and the most negligible difference between the probability distribution of pavement condition prediction modeling and the actual survey results was the IRI (International Roughness Index) method. This model is closest to the actual conditions during implementation, with a difference value of 5.7%. IJASEIT 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/12529/1/J18021_cfdc59dea7ca9bdcb30a8006dbdd819e.pdf Isradi, Muhammad and Prasetijo, Joewono and Rifai, Andri Irfan and Andraiko, Heru and Zhang, Guohui (2024) The Prediction of Road Condition Value during Maintenance Based on Markov Process. International Journal On Advanced Science Engeneering Information Technology, 14 (3). pp. 1083-1090. ISSN 2088-5334 |
| spellingShingle | TA Engineering (General). Civil engineering (General) Isradi, Muhammad Prasetijo, Joewono Rifai, Andri Irfan Andraiko, Heru Zhang, Guohui The Prediction of Road Condition Value during Maintenance Based on Markov Process |
| title | The Prediction of Road Condition Value during Maintenance Based on Markov Process |
| title_full | The Prediction of Road Condition Value during Maintenance Based on Markov Process |
| title_fullStr | The Prediction of Road Condition Value during Maintenance Based on Markov Process |
| title_full_unstemmed | The Prediction of Road Condition Value during Maintenance Based on Markov Process |
| title_short | The Prediction of Road Condition Value during Maintenance Based on Markov Process |
| title_sort | prediction of road condition value during maintenance based on markov process |
| topic | TA Engineering (General). Civil engineering (General) |
| url | http://eprints.uthm.edu.my/12529/1/J18021_cfdc59dea7ca9bdcb30a8006dbdd819e.pdf http://eprints.uthm.edu.my/12529/ |
| url_provider | http://eprints.uthm.edu.my/ |