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Submitted
22 August 2025
Accepted
21 November 2025
Published
30 November 2025
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Abstract

Acquiring accurate condition out of pavement evaluation can prove to be a challenge, particularly for local road agencies in Indonesia with limited resources. Conventionally, road condition is expressed in terms of the Surface Distress Index (SDI) and the International Roughness Index (IRI) that need time-consuming and labor-intensive road surveys. Advancements in smartphone technology has paved the way to a lower-cost and more rapid pavement evaluation by using applications such as Roadroids for IRI measurements. This study is aimed at exploring the viability of Roadroids-based IRI for pavement evaluation purposes. Klangon-Tempel road section in Yogyakarta Special Province was selected as the study area, on which a manual SDI and two Roadroid-IRI surveys were conducted. The two Roadroid surveys involved two different vehicle types: a sport utility vehicle (SUV) and a multi-purpose vehicle. The results showed that MPV-survey produced higher IRI values and were more consistent with pavement distresses observed through SDI survey, demonstrating a strong correlation coefficient of r=0.813. In contrast, SUV-survey showed significantly lower IRI values that overestimate overall pavement condition of the study area. No detailed investigation was made, but MPV features such as lower ground clearance and softer suspension system may contribute to cause the different outcomes. Complementing this, a range-based SDI–IRI analysis showed that SDI and IRI are consistent at low-distress levels but display substantial overlap across medium-to-high SDI categories, reflecting their inherently non-linear relationship. The findings suggests that, with appropriate type of vehicle, Roadroid can be a viable choice to conduct rapid IRI-based pavement evaluation, and thereby complement the traditional SDI surveys.

Keywords

Pavement surface evaluation Roadroid International Roughness Index (IRI) Surface Distress Index (SDI) Klangon-Tempel Yogyakarta

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Copyright (c) 2025 Fauji Ahmad, Berlian Kushari

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How to Cite
Ahmad, F., & Kushari, B. (2025). Evaluating pavement condition using roadroid and Surface Distress Index (SDI): a case study of Klangon-Tempel Road, Yogyakarta Special Province. Teknisia, 30(2), 84–93. https://doi.org/10.20885/teknisia.vol30.iss2.art3
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References

  1. Azhary, N. Y., Ratnaningsih, A., Rohman, A. (2023). Road performance relationship analysis using PCI and IRI methods on Tukum East Ring Road. International Journal of Science: Basic and Applied Research, 69 (1), 112-123.
  2. Bina Marga (2011). Survey Guide for Road Condition Data (in Bahasa Indonesia). Jakarta: Directorate General of Highway Administration (Bina Marga), Ministry of Public Works and Settlements.
  3. Cafiso, S., Di Graziano, A, Battiato, Sebastiano. (2006). Evaluation of Pavement Surface Distress Using Digital Image Collection And Analysis. Proc. Seventh International Congress on Advances in Civil Engineering, pp. 1-10, October 11-13, 2006 Yildiz Technical University, Istanbul, Turkey
  4. Douangphachanh, V., & Oneyama, H. (2013). Estimation of road roughness condition from smartphones under realistic settings. In Proceedings of the 13th International Conference on ITS Telecommunications (ITST) (pp. 433-439). IEEE.
  5. Fawwaz, N. (2023). Evaluation of Pavement through Surface Distress Index (SDI) and International Roughness Index (IRI) using RoadLab Pro application on Kepanjen-Pagak road section in Malang Regency. Final Project Report (in Bahasa Indonesia). Universitas Islam Malang.
  6. Forslöf, L., & Jones, H. (2015). Roadroid: Continuous road condition monitoring with smart phones. Journal of Civil Engineering and Architecture, 9(4), 485-496. doi: 10.17265/1934-7359/2015.04.012
  7. Haas, R., Hudson, W. R., & Zaniewski, J. P. (1994). Modern pavement management. ISBN 0894645889. Krieger Publishing Company.
  8. Huang, Y. H. (2004). Pavement analysis and design (2nd ed.). Pearson Prentice Hall.
  9. Islam, S., Buttlar, W. G., Aldunate, R. G., & Vavrik, W. R. (2014). Measurement of pavement roughness using android-based smartphone application. Transportation Research Record, 2457(1), 30-38.
  10. Kirbas, U. (2018). IRI Sensitivity to the influence of Surface Distress Flexible Pavements. Coatings, 8(8), 271-290.
  11. Pangesti, R., & Rahmawati, I. (2020). Evaluasi penilaian jalan menggunakan IRI Roadroid di ruas jalan kabupaten Banyumas. Prosiding Seminar Nasional Inovasi Teknologi Terapan Politeknik Negeri Balikpapan. Balikpapan. ISBN 978-602-51450-2-5 pp. 34-45.
  12. Paterson, W. D. (1987). Road deterioration and maintenance effects: Models for planning and management. ISBN 0801835909. World Bank.
  13. Piryonesi, S. M., El-Diraby, T. E. (2021-02-01). Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling. Journal of Infrastructure Systems. 27 (2): 04021005. doi:10.1061/(ASCE)IS.1943-555X.0000602
  14. Roadroid AB. (2021). Roadroid technical whitepaper. Roadroid AB. Retrieved from https://www.roadroid.com
  15. Rochmawati, A., & Irianto, E. (2020). Studi penilaian kondisi kerusakan jalan dengan metode nilai International Roughness Index (IRI) dan Surface Distress Index (SDI): Studi kasus Jalan Alternatif Waena–Entrop. Dintek: Jurnal Teknik, 13(2), 7–15.
  16. Sayers, M. W., Karamihas, S.M. (1998). The Little Book of Profiling. University of Michigan Transportation Research Institute.
  17. Transportation Research Board (2004). Automated Pavement Distress Collection Techniques A Synthesis of Highway Practices. NCHRP Synthesis 334. The National Academies: Washington D.C.
  18. Wang, W., & Guo, F. (2016). RoadLab: Revamping road condition and road safety monitoring by crowdsourcing with smartphone app. Transportation Research Board 95th Annual Meeting (No. 16-2116).
  19. Yegulla, V. P., & Sravana, P. (2023). Testing and evaluation of Roadbounce-mobile phone app based technology for road roughness measurement. i-Manager's Journal on Civil Engineering, 13(3).
  20. Yu, Q., Fang, Y., & Wix, R. (2023). Evaluation framework for smartphone-based road roughness index estimation systems. International Journal of Pavement Engineering, 24(1), 2183402.