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Rifai, Andri I.
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Civil Engineering, Building, Construction & Architecture

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Development of Pavement Deterioration Models Using Markov Chain Process Isradi, Muhammad; Rifai, Andri I.; Prasetijo, Joewono; Kinasih, Reni K.; Setiawan, Muhammad I.
Civil Engineering Journal Vol 10, No 9 (2024): September
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-09-012

Abstract

A common phenomenon in developing countries is that the function of the pavement in the road network will experience structural damage before the completion of life is reached, and the uncertainty of pavement damage is difficult to predict. Planning for maintenance treatment depends on the accuracy of predicting future pavement performance and observing current conditions. This study aims to apply the Markovian probability operational research process to develop a decision support system predicting future pavement conditions. Furthermore, it determines policies and effectiveness in managing and maintaining roads. A standard approach that can be used by observing the history of pavement damage from year to year is to estimate the transition probability as a Markovian-based performance prediction model. The results show that the application of the model is quite optimal, changes in pavement conditions after repair can be easily compared with an increase in good condition, reaching 92.8%. Routinely and consistently handling road deterioration will give favorable results regarding pavement condition value. This will ease in the management of the road network and the accomplishment of the optimal maintenance and repair policies. Doi: 10.28991/CEJ-2024-010-09-012 Full Text: PDF
Data Mining Approach-Based Damage Identification for Asphalt Pavement Under Natural Disaster Conditions Rifai, Andri I.; Isradi, Muhammad; Prasetijo, Joewono; Sari, Yusra A.; Zolkepli, Muhammad F.
Civil Engineering Journal Vol 10, No 12 (2024): December
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2024-010-12-015

Abstract

Road performance can also decline due to natural disasters such as earthquakes, often in Indonesia. Given the high risk of natural disasters in Indonesia, it is important to consider their impact. Therefore, it is necessary to prepare for road rehabilitation and reconstruction quickly and accurately. This research aims to identify potential factors causing road damage by developing an approach to obtain predictions of road damage levels due to natural disasters by utilizing the availability of historical data, developing a decision support system to rehabilitate and reconstruct roads after disasters, and developing a road damage model due to earthquakes using data mining. The data was used to assess the condition of the national road pavement in Central Sulawesi and identified the disaster events as earthquakes that originated from the USGS. Data processing uses a data mining (DM) approach, which includes three models. The results found that the SVM modeling with the DM approach had a high accuracy rate of 0.91 ± 0.01, RMSE 0.70 ± 0.02, and MAD 0.42 ± 0.01. SVM achieves the highest accuracy after 20 runs. The best hyperparameters to accomplish a fit SVM model are ϵ = 0.07 ± 0.01 and γ = 0.05 ± 0.00. Meanwhile, for ANN, the hyperparameters are H = 3 ± 1. The earthquake’s magnitude (27%) and depth (24%) contribute to road damage. Doi: 10.28991/CEJ-2024-010-12-015 Full Text: PDF
Co-Authors Kinasih, Reni K. Muhammad Isradi Prasetijo, Joewono Sari, Yusra A. Setiawan, Muhammad I. Zolkepli, Muhammad F.