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All Journal Prosiding PESAT Teknosains: Media Informasi Sains dan Teknologi REKAYASA Fondasi: Jurnal Teknik Sipil JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Aplikasi Teknik Sipil Borneo Engineering: Jurnal Teknik Sipil Kumawula: Jurnal Pengabdian Kepada Masyarakat Civilla : Jurnal Teknik Sipil Universitas Islam Lamongan Jurnal Keselamatan Transportasi Jalan (Indonesian Journal of Road Safety) Jurnal Teknologi Transportasi dan Logistik TRANSFORMASI : JURNAL PENGABDIAN PADA MASYARAKAT Jurnal Riset Teknik Sipil dan Sains Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Keselamatan Transportasi Jalan Jurnal Ilmiah Dinamika Rekayasa
Yogi Oktopianto
Politeknik Keselamatan Transportasi Jalan
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Physics Social Sciences Transportation Other

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Exploration of Road Damage Parameters Using Surface Distress Index (SDI) in Enhancing Urban Road Infrastructure Maintenance Planning Yogi Oktopianto; Antonius Antonius; Abdul Rochim
Rekayasa Vol 19, No 1: January - April 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/rekayasa.v19i1.31746

Abstract

Effective road infrastructure maintenance is essential to ensure the sustainability and safety of the transportation system, particularly in urban areas. This study focuses on the application of the Surface Distress Index (SDI) in road condition assessment using a regression model to explore various factors affecting road damage. The methodology used included data collection through field surveys on 42 urban road segments with 2,467 observational data points. The collected data comprised information on crack length and width, potholes, and rutting. Multiple linear regression analysis and the exploration of non-linear models were conducted to evaluate the relationship between these variables and road conditions. The results showed that the number of potholes had the strongest correlation with the SDI, followed by crack width and rutting. The logarithmic model proved to be the most efficient in predicting road conditions, with an R2 value of 0.75, an Akaike Information Criterion (AIC) of 22,856.35, and a Bayesian Information Criterion (BIC) of 22,879.59, indicating a balance between simplicity and the ability to explain variance in the data. This study contributes to the development of data-driven road maintenance methodologies, which can be applied in planning road maintenance that is more accurate, efficient, and sustainable.
An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance Yogi Oktopianto; Antonius; Abdul Rochim
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2133

Abstract

The Surface Distress Index (SDI) is a crucial parameter to consider when determining road conditions as part of an effective maintenance strategy. This study aims to develop an SDI prediction model using road surface distress data to enhance maintenance planning. The developed Artificial Neural Network (ANN) model resulted in an optimal structure with two hidden layers comprising 6 neurons and 4 neurons, respectively. The model was trained using two years of surface distress data collected from 40 road sections managed by the city’s road maintenance division. Variables used included Composition, Condition, Depression, Patches, Damage types, Crack Area, and Crack Width. The results demonstrated high accuracy in predicting SDI, with model performance achieving an R² of 0.87. This model can be applied to optimize the efficiency of road maintenance strategies.
Classification of Urban Road Damage Levels Using Surface Distress Index Parameters and K-means Algorithm Yogi Oktopianto; Antonius Antonius; Abdul Rochim
Jurnal Ilmiah Dinamika Rekayasa Vol. 22 No. 1 (2026): Jurnal Ilmiah Dinamika Rekayasa - Januari
Publisher : Engineering Faculty, UNSOED

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jidr.2026.22.1.61

Abstract

Road damage is a significant problem in many countries, including Indonesia, and it can affect safety, transportation efficiency, and the quality of life of communities, especially on urban roads. This study aims to develop a data-based model using the K-Means algorithm to detect and classify the levels of urban road damage based on the Surface Distress Index (SDI) parameter. The data used comprised 2,467 road segments containing information on the types and levels of damage over the past five years. The clustering model was designed with two and four clusters, and the results indicated that the four-cluster model provided a clearer and more representative separation of road conditions. The Silhouette Coefficient value of the four-cluster model is 0.513, indicating a more detailed and clearer separation compared to the two-cluster model with a Silhouette value of 0.423. The four cluster model is better at telling apart complex data structures than the two cluster model. The results of this study contribute to the development of a road condition monitoring system based on maintenance priority data categorized by road condition, which can be adapted to improve infrastructure policies in Indonesia and other developing countries.
Co-Authors Aat Eska Fahmadi Abdul Rochim Ahmad, Rahmat Antonius antonius antonius Antonius, Antonius Aris Budi Sulistyo Ayu, Brasie Pradana Sela Bunga Riska Budi Sulistyo, Aris Dewi Agushinta R. Dwi Wahyu Hidayat Dwi Wahyu Hidayat, Dwi Wahyu Eka Krisdayanti Eska Fahmadi, Aat Fahmadi, Aat Eska Faishal Andhi Rokhman Fathullah, M Fathullah, M Febrianta, Arjuna Rizky Hadi, Suprapto I Made Sukmayasa Joko Siswanto Kanthi Pangestu Wijayanthi Kartono Wibowo, Kartono Kurniawan, Moch. Aziz M Jauhar Nabil Mardikawati, Budi Neneng Winarsih Oktavianus Sasue, Riz Rifai Pangesty, Sindy Pipit Rusmandani, Pipit Prasetiyo, Wahyu Jati Relly Andayani Remi Senjaya Riza Pahlevi Marwanto Riza Phahlevi Marwanto Rizky Dwi Anggara Rizky, Matthew Rochim, Abdul Rukman Rukman Rukman Rukman Rupaka, Anggun Prima Gilang S Imam Wahyudi Setyadi Gunawan Siti Shofiah Soimun, Ahmad Suprapto Hadi Tiffany, Adinda Tri Prasetyo Tri Susila Hidayati Wahyu Hidayat, Dwi Widi Yau Hastuti Widya Anggraeni, Inneke Yoga Anindita, Reza Yurista Vipriyanti Yusuf Maulana Arief Yusuf Maulana Arief