Exploration of Road Damage Parameters Using Surface Distress Index (SDI) in Enhancing Urban Road Infrastructure Maintenance Planning

Yogi Oktopianto; Antonius Antonius; Abdul Rochim

doi:10.21107/rekayasa.v19i1.31746

Exploration of Road Damage Parameters Using Surface Distress Index (SDI) in Enhancing Urban Road Infrastructure Maintenance Planning

Yogi Oktopianto, Antonius Antonius, Abdul Rochim

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.

Keywords

Surface Distress Index; Road Maintenance; Multiple Linear Regression; Logarithmic Model; AIC; BIC; Road Condition

Full Text:

PDF

References

Abaza, K. A. (2023). The Use of IRI Data in Developing Optimal Pavement Rehabilitation Plan for Developing Countries: Palestine as a Case Study. https://doi.org/10.21203/rs.3.rs-3247799/v1

Armağan, K. (2022). Reimagining the Future of Transport and Forever Open Road Program. Journal of Innovative Transportation, 3(2), 39–44. https://doi.org/10.53635/jit.1201096

Assagaf, I., Sukandi, A., Abdillah, A. A., Arifin, S., & Ga, J. L. (2023). Machine Predictive Maintenance by Using Support Vector Machines. Recent in Engineering Science and Technology, 1(01), 31–35. https://doi.org/10.59511/riestech.v1i01.6

Bina Marga, Pub. L. No. SMD-03/RCS, Indonesian Integrated Road Management Systems (IIRMS) (2011).

Che, C., & Tian, J. (2024). Methods Comparison for Neural Network-Based Structural Damage Recognition and Classification. Aorpm, 3(1), 20–26. https://doi.org/10.54254/3029-0880/3/2024022

Chen, K., Torbaghan, M. E., Thom, N., & Faramarzi, A. (2025). Physics-guided neural network for predicting international roughness index on flexible pavements considering accuracy, uncertainty and stability. Engineering Applications of Artificial Intelligence, 142. https://doi.org/10.1016/j.engappai.2024.109922

Chen, L., Li, H., Wang, S., Shan, F., Han, Y., & Zhong, G. (2024). Imporved model for pavement performance prediction based on recurrent neural network using LTPP database. International Journal of Transportation Science and Technology. https://doi.org/10.1016/j.ijtst.2024.08.005

Chen, X., Yongchareon, S., & Knoche, M. (2023). A Review on Computer Vision and Machine Learning Techniques for Automated Road Surface Defect and Distress Detection. Journal of Smart Cities and Society, 1(4), 259–275. https://doi.org/10.3233/scs-230001

Domitrović, J., Dragovan, H., Rukavina, T., & Dimter, S. (2018). Application of an Artificial Neural Network in Pavement Management System. Tehnicki Vjesnik - Technical Gazette, 25(Supplement 2). https://doi.org/10.17559/tv-20150608121810

Elshamy, M. M. M., Tiraturyan, A. N., Uglova, E. V., & Zakari, M. (2020). Development of the non-destructive monitoring methods of the pavement conditions via artificial neural networks. Journal of Physics: Conference Series, 1614(1). https://doi.org/10.1088/1742-6596/1614/1/012099

Feitosa, I., Santos, B., & Almeida, P. G. (2024). Pavement Inspection in Transport Infrastructures Using Unmanned Aerial Vehicles (UAVs). In Sustainability (Switzerland) (Vol. 16, Issue 5). https://doi.org/10.3390/su16052207

Gharehbaghi, K., & McManus, K. (2019). TIS condition monitoring using ANN integration: an overview. Journal of Engineering, Design and Technology, 17(1). https://doi.org/10.1108/JEDT-07-2018-0117

Grum, B., Tsegaye, D., Tariku, Z., Gebremariam, D., Gebremicael, H., Kuhilen, T., Yemane, S., Aregawi, A., & Abebe, B. A. (2023). Applicability and Cost Implication of Labor-Based Methods for Sustainable Road Maintenance (SRM) in Developing Countries. Advances in Civil Engineering, 2023, 1–12. https://doi.org/10.1155/2023/1529148

Issa, A., Samaneh, H., & Ghanim, M. (2022). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 13(1). https://doi.org/10.1016/j.asej.2021.04.033

Jeong, S., Ko, J., Lee, S., Kang, J., Kim, Y., Park, S. Y., & Mun, S. (2024). Optimizing Lane Departure Warning System Towards AI-Centered Autonomous Vehicles. Sensors, 24(8), 2505. https://doi.org/10.3390/s24082505

Jurkevičius, M., Puodžiukas, V., & Laurinavičius, A. (2020). Implementation of Road Performance Calculation Models Used in Strategic Planning Systems for Lithuania Conditions. The Baltic Journal of Road and Bridge Engineering, 15(3), 146–156. https://doi.org/10.7250/bjrbe.2020-15.489

Kaiser, N., & Barstow, C. K. (2022). Rural Transportation Infrastructure in Low-and Middle-Income Countries: A Review of Impacts, Implications, and Interventions. In Sustainability (Switzerland) (Vol. 14, Issue 4). MDPI. https://doi.org/10.3390/su14042149

Kamel, H. (2022). Artificial Intelligence for Predictive Maintenance. Journal of Physics Conference Series, 2299(1), 012001. https://doi.org/10.1088/1742-6596/2299/1/012001

Kheirati, A., & Golroo, A. (2022). Machine learning for developing a pavement condition index. Automation in Construction, 139. https://doi.org/10.1016/j.autcon.2022.104296

Kravcovas, I., Vaitkus, A., & Kleizienė, R. (2020). Comparison of Pavement Performance Models for Urban Road Management System. The Baltic Journal of Road and Bridge Engineering, 15(3), 111–129. https://doi.org/10.7250/bjrbe.2020-15.487

Kwon, K., Choi, H., Pham, K., Kim, S., & Bae, A. (2024). Influence Analysis of Pavement Distress on International Roughness Index Using Machine Learning. KSCE Journal of Civil Engineering. https://doi.org/10.1007/s12205-024-0093-9

Lin, J. D., Hung, C. T., Peng, C. H., Tsao, J. H., Ho, M. C., & Shen, C. J. (2013). Research of building pavement maintenance management systemof downtown area - Zhongli City, Taoyuan County for example. Advanced Materials Research, 671–674. https://doi.org/10.4028/www.scientific.net/AMR.671-674.3007

Liu, S., Arifin, M. F. A., & Chen, W. T. (2022). An Integrated Optimization Model for Life Cycle Pavement Maintenance Budgeting Problems. Ieee Access, 10, 13884–13900. https://doi.org/10.1109/access.2022.3147465

Llopis-Castelló, D., Paredes, R., Parreño-Lara, M., García-Segura, T., & Pellicer, E. (2021). Automatic Classification and Quantification of Basic Distresses on Urban Flexible Pavement Through Convolutional Neural Networks. Journal of Transportation Engineering Part B Pavements, 147(4). https://doi.org/10.1061/jpeodx.0000321

Loprencipe, G., & Pantuso, A. (2017). A Specified Procedure for Distress Identification and Assessment for Urban Road Surfaces Based on PCI. Coatings, 7(5), 65. https://doi.org/10.3390/coatings7050065

Mohaisen, A. H., & Rashid, H. A. (2024). Study the Causes and Effects of Frequent Failures of Roads Pavement in Iraq. Iop Conference Series Earth and Environmental Science, 1374(1), 012045. https://doi.org/10.1088/1755-1315/1374/1/012045

Muhammed, A. Y., Sulaiman, S., Bello, S., Abdulkadir, H., & Baba, S. U. (2023). Resilient Infrastructure: Impacts of Roads on Population and Economic Growth in Kaduna Metropolis, Kaduna State, Nigeria. Fudma Journal of Sciences, 7(3), 127–132. https://doi.org/10.33003/fjs-2023-0703-1856

Oktopianto, Y., Antonius, A., & Rochim, A. (2025a). An Artificial Neural Network Approach for Predicting Pavement Distress: A Case Study Toward Sustainable Road Maintenance. Advance Sustainable Science, Engineering and Technology (ASSET), 7(3). https://doi.org/10.26877/asset.v7i3.2133

Oktopianto, Y., Antonius, A., & Rochim, A. (2025b). Evaluation of Urban Road Stability Through the Integration of the Surface Distress Index and International Roughness Index. Fondasi: Jurnal Teknik Sipil, 14(1), 1–12. https://doi.org/10.62870/fondasi

Ragnoli, A., Blasiis, M. R. D., & Benedetto, A. D. (2018). Pavement Distress Detection Methods: A Review. Infrastructures, 3(4), 58. https://doi.org/10.3390/infrastructures3040058

Rifai, M. (2023). Evaluation of Functional and Structural Conditions on Flexible Pavements Using Pavement Condition Index (PCI) and International Roughness Index (IRI) Methods. E3s Web of Conferences, 429, 05011. https://doi.org/10.1051/e3sconf/202342905011

Roberts, R., Inzerillo, L., & Di Mino, G. (2020). Exploiting low-cost 3D imagery for the purposes of detecting and analyzing pavement distresses. Infrastructures, 5(1). https://doi.org/10.3390/infrastructures5010006

Ruiz, A., & Guevara, J. (2020). Sustainable Decision-Making in Road Development: Analysis of Road Preservation Policies. Sustainability, 12(3), 872. https://doi.org/10.3390/su12030872

Shao-jie, H. (2019). The Relationship Between Transportation Infrastructure Investment and Economic Growth ——Based on Provincial Spatial Panel Data Model. https://doi.org/10.2991/icfied-19.2019.14

Sharma, A., Sachdeva, S. N., & Aggarwal, P. (2023). Predicting IRI Using Machine Learning Techniques. International Journal of Pavement Research and Technology, 16(1). https://doi.org/10.1007/s42947-021-00119-w

Sigdel, T., Pradhananga, R., & Shrestha, S. (2024). Artificial Neural Network-based model to predict the International Roughness Index of national highways in Nepal. Transportation Research Interdisciplinary Perspectives, 25. https://doi.org/10.1016/j.trip.2024.101128

Sirhan, M., Bekhor, S., & Sidess, A. (2022). Implementation of Deep Neural Networks for Pavement Condition Index Prediction. Journal of Transportation Engineering, Part B: Pavements, 148(1). https://doi.org/10.1061/jpeodx.0000333

Tan, C., Uddin, N., & Mohammed, Y. M. (2019). Deep learning-based crack detection using mask R-CNN technique. 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII 2019 - Conference Proceedings, 1.

Wang, C., Xu, S., & Yang, J. (2021). Adaboost Algorithm in Artificial Intelligence for Optimizing the IRI Prediction Accuracy of Asphalt Concrete Pavement. Sensors, 21(17), 5682. https://doi.org/10.3390/s21175682

Yao, L., Dong, Q., Jiang, J., & Ni, F. (2020). Deep Reinforcement Learning for Long‐term Pavement Maintenance Planning. Computer-Aided Civil and Infrastructure Engineering, 35(11), 1230–1245. https://doi.org/10.1111/mice.12558

Zhang, K., Che, X., Wu, Y., & Xu, Q. (2025). Study on Calculation Method of Pavement Condition Index (PCI) of Urban Road. Ce/Papers, 8(2), 1509–1521. https://doi.org/10.1002/cepa.3236