Skip to main content
Article | Civil and Sustainable Urban Engineering | Volume 5 - Issue 2 - 2025 | 117-129 | https://doi.org/10.53623/csue.v5i2.831
© 2025 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Assessment of Pavement Performance Using the Pavement Condition Index (PCI): A Case Study of the Bengrah II Project in Palembang, Indonesia

Author(s): Aldi Armahedi , Ely Mulyati
Author(s) information:
Program Studi Teknik Sipil, Fakultas Sains dan Teknologi, Universitas Bina Darma, Jalan Jenderal Ahmad Yani No. 12, Plaju, Palembang 30264, Sumatera Selatan, Indonesia.

Corresponding author

Civil and Sustainable Urban Engineering

Volume 5 - Issue 2 - 2025

 About the journal
Submit your article

Road pavement condition is essential for ensuring the smooth mobilization of materials, heavy equipment, and personnel in large-scale construction projects. Excessive loading from project vehicles often accelerates pavement deterioration, as observed along Mayor Memet Sastra Wirya Street, the main access road for the multi-year Bengrah II Project in Palembang. This study evaluated pavement distress types  types and severity levels using the Pavement Condition Index (PCI) method based on field observations, photographic documentation, and dimensional measurements of visible defects. The analysis produced PCI values ranging from 62 to 70, with an average PCI of 67, classifying the pavement as Good. The dominant types of distress were alligator cracking and surface deformation (bumps and sags), primarily caused by repetitive heavy vehicle loading and inadequate drainage conditions. Despite the overall good classification, localized structural deterioration indicates early functional decline of the pavement. These results highlight that continuous heavy traffic associated with construction activities significantly affects pavement performance. Therefore, preventive maintenance actions such as surface overlays, shallow patching, and stricter vehicle load control, are recommended to sustain road functionality and extend service life. The findings contribute to pavement management strategies and policy formulation for maintaining construction access roads under intensive loading conditions.

Read Full-Text
Previous article
Next article

Ali, A. A.; Milad, A.; Hussein, A.; Yusoff, N. I. M.; Heneash, U. (2023). Predicting pavement condition index based on the utilization of machine learning techniques: A case study. Journal of Road Engineering, 3(3), 266–278. https://doi.org/10.1016/j.jreng.2023.04.002

Ali, A.; Heneash, U.; Hussein, A.; Eskebi, M. (2022). Predicting pavement condition index using fuzzy logic technique. Infrastructures, 7(7), 91. https://doi.org/10.3390/infrastructures7070091

Al-Rubaee, R. H.; Shubber, A. A.; Khaleefah, H. S. (2020). Evaluation of rigid pavement using the Pavement Condition Index: A case study. IOP Conference Series: Materials Science and Engineering, 737(1), 012128. https://doi.org/10.1088/1757-899X/737/1/012128

Amanah, T. (2023). Functional evaluation of runway pavement using Pavement Condition Index (PCI) method: A case study at Fatmawati Soekarno Airport, Bengkulu. E3S Web of Conferences, 429, 05011. https://doi.org/10.1051/e3sconf/202342905011

Ashqar, H. I.; Issa, A.; Masri, S. (2025). A software for predicting pavement condition index (PCI) using machine learning for practical decision-making with an exclusion approach. SoftwareX, 31, 102304. https://doi.org/10.1016/j.softx.2025.102304

Danyo, A.; Dontoh, A.; Aboah, A. (2025). An improved ResNet50 model for predicting pavement condition index (PCI) directly from pavement images. Road Materials and Pavement Design, 1–18. https://doi.org/10.1080/14680629.2025.2498632

Delfina, Y.; Ishak, I.; Dewi, S. (2023). Evaluation of road damage using the Pavement Condition Index (PCI) method on Jalan Raya Waringin, Palasah District, Majalengka Regency. LEADER Journal of Civil Engineering, 1(4), Article 8952. https://doi.org/10.37253/leader.v1i4.8952

Elhadidy, A. A.; El-Badawy, S. M.; Elbeltagi, E. E. (2021). A simplified pavement condition index regression model for pavement evaluation. International Journal of Pavement Engineering, 22(5), 643–652. https://doi.org/10.1080/10298436.2019.1633579

Elmansouri, O.; Alossta, A.; Badi, I. (2022). Pavement condition assessment using pavement condition index and multi-criteria decision-making model. Mechatronics and Intelligent Transportation Systems, 1(1), 57–68. https://doi.org/10.56578/mits010107

Guo, L.; Wang, H.; Gagnon, J. (2021). Comparison analysis of airfield pavement life estimated from different pavement condition indexes. Journal of Transportation Engineering, Part B: Pavements, 147(2). https://doi.org/10.1061/JPEODX.0000254

Hermawati, P.; Putri, N. P. A. S. (2024). Evaluation of flexible pavement using PCI and Bina Marga methods. IOP Conference Series: Earth and Environmental Science, 1294, 012030. https://doi.org/10.1088/1755-1315/1294/1/012030

Ibrahim, E. M.; El-Badawy, S. M.; Ibrahim, M. H. (2020). A modified pavement condition rating index for flexible pavement evaluation in Egypt. Innovative Infrastructure Solutions, 5, 55. https://doi.org/10.1007/s41062-020-00304-z

Ibragimov, E.; Kim, Y.; Lee, J. H.; Cho, J.; Lee, J.-J. (2024). Automated pavement condition index assessment with deep learning and image analysis: An end-to-end approach. Sensors, 24(7), 2333. https://doi.org/10.3390/s24072333

Imam, R.; Murad, Y.; Asi, I. (2021). Predicting Pavement Condition Index from International Roughness Index using Gene Expression Programming. Innovative Infrastructure Solutions, 6, 139. https://doi.org/10.1007/s41062-021-00504-1

Isradi, M.; Prasetijo, J.; Aden, T. S.; Rifai, A. I. (2023). Relationship of present serviceability index for flexible and rigid pavement in urban road damage assessment using PCI and IRI. E3S Web of Conferences, 429, 03012. https://doi.org/10.1051/e3sconf/202342903012

Issa, A.; Samaneh, H.; Ghanim, M. (2022). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 13(1), 101490. https://doi.org/10.1016/j.asej.2021.04.033

Jakisa Owor, N.; Du, H.; Daud, A.; Aboah, A.; Adu-Gyamfi, Y. (2023). A multitask learning framework for estimating pavement condition indices directly from images. arXiv preprint. https://doi.org/10.48550/arXiv.2310.08538

Kareem, S. A.; Shubber, K. H. H. (2021). Pavement condition assessment using PCI method. Journal of Physics: Conference Series, 1895, 012021. https://doi.org/10.1088/1742-6596/1895/1/012021

Kumar, R.; Suman, S. K.; Prakash, G. (2021). Evaluation of pavement condition index using artificial neural network approach. Transportation in Developing Economies, 7(2), 20. https://doi.org/10.1007/s40890-021-00130-7

Majidifard, H.; Adu-Gyamfi, Y.; Buttlar, W. G. (2020). Deep machine learning approach to develop a new asphalt pavement condition index. Construction and Building Materials, 247, 118513. https://doi.org/10.1016/j.conbuildmat.2020.118513

Miladiyah, S.; Mawardi, A. F. (2023). Evaluation and maintenance of flexible pavement on the Sampan–Ketapang Highway using the PCI and Bina Marga methods. Jurnal Transportasi: Sistem, Material, dan Infrastruktur, 5(1), A219–A226. https://journal.its.ac.id/index.php/transportasi/article/view/726

Paz e Albuquerque, T.; Almeida de Melo, R.; Bezerra de Morais, L. M.; Quintino Lira Oliveira, L.; Cirne de Azevedo Filho, A. (2022). Desenvolvimento de um índice de condição de pavimento flexível para rede de via urbana. Transportes, 30(2), 2553. https://doi.org/10.14295/transportes.v30i2.2553

Pietersen, R. A.; Beauregard, M. S.; Einstein, H. H. (2022). Automated method for airfield pavement condition index evaluations. Automation in Construction, 141, 104408. https://doi.org/10.1016/j.autcon.2022.104408

Pinatt, J. M.; Chicati, M. L.; Ildefonso, J. S.; Filetti, C. R. G. D. A. (2020). Evaluation of pavement condition index by different methods: Case study of Maringá, Brazil. Transportation Research Interdisciplinary Perspectives, 4, 100100. https://doi.org/10.1016/j.trip.2020.100100

Piryonesi, S. M.; El-Diraby, T. E. (2021). Examining the relationship between two road performance indicators: Pavement condition index and international roughness index. Transportation Geotechnics, 26, 100441. https://doi.org/10.1016/j.trgeo.2020.100441

Radwan, M. M.; Mousa, A.; Zahran, E. M. M. (2024). Enhancing pavement sustainability: Prediction of PCI in arid urban climates using IRI. Sustainability, 16(8), 3158. https://doi.org/10.3390/su16083158

Rahmawati, A.; Adly, E.; Setiawan, D.; Dewi, S.; Widodo, W. (2021). Functional damage assessment visually on the road using Pavement Condition Index (PCI). In Proceedings of the 4th International Conference on Sustainable Innovation 2020 – ICoSITEA 2020 (pp. 67–73). Atlantis Press. https://doi.org/10.2991/aer.k.210204.015

Rifai, M.; Setyawan, A.; Handayani, F. S.; Arun, A. D. (2023). Evaluation of functional and structural conditions on flexible pavements using PCI and IRI methods. E3S Web of Conferences, 429, 05011. https://doi.org/10.1051/e3sconf/202342905011

Sidess, A.; Ravina, A.; Oged, E. (2020). A model for predicting the deterioration of the Pavement Condition Index. International Journal of Pavement Engineering, 22(13), 1625–1636. https://doi.org/10.1080/10298436.2020.1714044

Utami, R.; Ruchiat, I.; Astor, Y.; Sihombing, A. V. R.; Prayogo, R. D. R. B.; Pertiwi, D. A.; Sari, R. M. (2024). Pavement condition evaluation based on PCI guidelines assisted by UAV. AIP Conference Proceedings, 3077(1), 050022. https://doi.org/10.1063/5.0201093

Mora-Ruiz, J.; Mora-Cabrera, J. A.; Aguirre-Bielschowsky, I. (2021). Damage importance analysis for pavement condition index using decision-tree models. Buildings, 9(9), 157. https://doi.org/10.3390/buildings9090157

Kyem, P.; Alhassan, I.; Li, S. (2024). The first multimodal framework for comprehensive pavement condition assessment with dense captioning and PCI estimation (PaveCap). arXiv preprint, arXiv:2408.04110. https://doi.org/10.48550/arXiv.2408.04110

Zhang, L.; Wang, X.; Zhao, Y. (2025). Machine-learning-based highway pavement performance prediction in Xinjiang. Infrastructures, 10(7), 189. https://doi.org/10.3390/infrastructures10070189

Piryonesi, S. M.; El-Diraby, T. E. (2021). Examining the relationship between pavement condition index and international roughness index. Transportation Geotechnics, 26, 100441. https://doi.org/10.1016/j.trgeo.2020.100441

Owor, N. J.; Du, H.; Daud, A.; Aboah, A.; Adu-Gyamfi, Y. (2023). A multitask learning framework for estimating pavement condition indices directly from images. arXiv preprint, arXiv:2310.08538. https://doi.org/10.48550/arXiv.2310.08538

Pietersen, R. A.; Beauregard, M. S.; Einstein, H. H. (2022). Automated method for airfield pavement condition index evaluations. Automation in Construction, 141, 104408. https://doi.org/10.1016/j.autcon.2022.104408

Ibragimov, E.; Kim, Y.; Lee, J. H.; Cho, J.; Lee, J.-J. (2024). Automated pavement condition index assessment with deep learning and image analysis: An end-to-end approach. Sensors, 24(7), 2333. https://doi.org/10.3390/s24072333

Osman, S. A.; Almoshaogeh, M.; Jamal, A.; Alharbi, F.; Al Mojil, A.; Dalhat, M. A. (2023). Intelligent assessment of pavement condition indices using artificial neural networks. Sustainability, 15(1), 561. https://doi.org/10.3390/su15010561

Radwan, M. M.; Mousa, A.; Zahran, E. M. M. (2024). Enhancing pavement sustainability: Prediction of the pavement condition index in arid urban climates using the international roughness index. Sustainability, 16(8), 3158. https://doi.org/10.3390/su16083158

Danyo, A.; Dontoh, A.; Aboah, A. (2025). An improved ResNet50 model for predicting pavement condition index (PCI) directly from pavement images. Road Materials and Pavement Design, 1–18. https://doi.org/10.1080/14680629.2025.2498632

Zaid, M.; Sulistyorini, R.; Ofrial, S. A. M. P. (2021). Analysis of road pavement damage level using the Pavement Condition Index (PCI) method: Case study of Jalan P. Tirtayasa, Bandar Lampung. Jurnal Rekayasa Sipil dan Desain, 9(2), 201–212. https://doi.org/10.33322/jrsd.v9i2.1842

Jannah, A.; Rasyid, M.; Pratama, A. (2022). Road condition assessment using the Pavement Condition Index (PCI) method in Bandung City. Jurnal Rekayasa Jalan, 14(1), 12–20. https://doi.org/10.35472/jrj.v14i1.1870

Rahman, M. A.; Arifin, H.; Sowolino, B. O. (2022). Comparison of the International Roughness Index (IRI) and Pavement Condition Index (PCI) methods for national road condition assessment in Wamena–Habema. Rang Teknik Journal, 5(1), 1–7. https://doi.org/10.36986/rangteknik.v5i1.1370

Yunus, A.; Said, L. B.; Alifuddin, A. (2022). Evaluation of national road maintenance using the International Roughness Index (IRI) and Pavement Condition Index (PCI): A case study of the Kalukku–Mamuju section. Jurnal Konstruksi: Teknik, Infrastruktur dan Sains, 1(1), 10–21. https://doi.org/10.55676/jktis.v1i1.24

Read Full-Text
About this article

SUBMITTED: 18 September 2025
ACCEPTED: 02 November 2025
PUBLISHED: 4 November 2025
SUBMITTED to ACCEPTED: 45 days
DOI: https://doi.org/10.53623/csue.v5i2.831

Cite this article
Armahedi, A. ., & Mulyati, E. . (2025). Assessment of Pavement Performance Using the Pavement Condition Index (PCI): A Case Study of the Bengrah II Project in Palembang, Indonesia . Civil and Sustainable Urban Engineering, 5(2), 117–129. https://doi.org/10.53623/csue.v5i2.831
Keywords
Pavement Distress Pavement Condition Index (PCI) Flexible Pavement Heavy Vehicle Loading Palembang
Accessed
380
Citations
0
Share this article