Analysis and Development of Surface Distress Index Modified Based on Pavement Condition Index Criteria for Pavement Evaluation
Vol. 11 No. 1 (2025): January
Research Articles
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Doi: 10.28991/CEJ-2025-011-01-014
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Gerardo, F., Subagio, B. S., Frazila, R. B., & Wibowo, R. S. S. (2025). Analysis and Development of Surface Distress Index Modified Based on Pavement Condition Index Criteria for Pavement Evaluation. Civil Engineering Journal, 11(1), 230–243. https://doi.org/10.28991/CEJ-2025-011-01-014
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[1] Sholevar, N., Golroo, A., & Esfahani, S. R. (2022). Machine learning techniques for pavement condition evaluation. Automation in Construction, 136(February), 104190. doi:10.1016/j.autcon.2022.104190.
[2] Peraka, N. S. P., & Biligiri, K. P. (2020). Pavement asset management systems and technologies: A review. Automation in Construction, 119(March), 103336. doi:10.1016/j.autcon.2020.103336.
[3] Zukhruf, F., Balijepalli, C., Bona Frazila, R., Suryo Nugroho, T., & Tsavalista Burhani, J. (2023). A new model of pavement maintenance, rehabilitation and reconstruction considering multimodal network development. Engineering Optimization, 55(7), 1118–1132. doi:10.1080/0305215X.2022.2061477.
[4] Issa, A., Samaneh, H., & Ghanim, M. (2022). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 13(1), 101490. doi:10.1016/j.asej.2021.04.033.
[5] Shtayat, A., Moridpour, S., Best, B., Shroff, A., & Raol, D. (2020). A review of monitoring systems of pavement condition in paved and unpaved roads. Journal of Traffic and Transportation Engineering, 7(5), 629–638. doi:10.1016/j.jtte.2020.03.004.
[6] Attoh-Okine, N., & Adarkwa, O. (2013). Pavement condition surveys–overview of current practices. Delaware Center for Transportation, University of Delaware, Newark, United States.
[7] Nautiyal, A., & Sharma, S. (2022). Methods and factors of prioritizing roads for maintenance: a review for sustainable flexible pavement maintenance program. Innovative Infrastructure Solutions, 7(3), 190. doi:10.1007/s41062-022-00771-6.
[8] Hadiwardoyo, S. P., Correia, A. G., & Pereira, P. (2015). Road deterioration analysis for the national roads of Indonesia. Proceedings of the 14th International Conference on QIR (Quality in Research), 20-13 August, 2015, Lombok, Indonesia.
[9] Hamdi, Hadiwardoyo, S. P., Correia, A. G., Pereira, P., & Cortez, P. (2017). Prediction of surface distress using neural networks. AIP Conference Proceedings, 1855. doi:10.1063/1.4985502.
[10] Rosada, A., Arliansyah, J., & Buchari, E. (2019). Evaluation Pavement Deteriorating Condition on Surface Distress Index (SDI) Data Using Radial Basis Function Neural Networks (RBFNN). Journal of Physics: Conference Series, 1198(3), 1–8. doi:10.1088/1742-6596/1198/3/032008.
[11] Setiadji, B. H., Purwanto, D., & Wicaksono, Y. I. (2020). Improvement of Potholes and Rutting Assessment in Surface Distress Index. In 2nd International Symposium on Transportation Studies in Developing Countries (ISTSDC 2019), Atlantis Press, 162-166. doi:10.2991/aer.k.200220.034.
[12] SMD-03/RCS. (2011). Road Condition Survey Guideline. Implementation of Indonesian integrated road management system (IIRMS), Jakarta, Indonesia. (In Indonesian).
[13] Setiadji, B. H. (2019). Proposed SDI equations to improve the effectiveness in evaluating crack damage on the road pavement. IOP Conference Series: Materials Science and Engineering, 650(1), 012007. doi:10.1088/1757-899X/650/1/012007.
[14] Setiadji, B., Supriyono, & Purwanto, D. (2019). Surface Distress Index Updates to Improve Crack Damage Evaluation. Advances in Engineering Research, 186(APTE), 48–55,. doi:10.2991/apte-18.2019.10.
[15] Pinatt, J. M., Chicati, M. L., Ildefonso, J. S., & Filetti, C. R. G. D. arc. (2020). Evaluation of pavement condition index by different methods: Case study of Maringá, Brazil. Transportation Research Interdisciplinary Perspectives, 4, 100100. doi:10.1016/j.trip.2020.100100.
[16] Zhao, K., Ma, X., Zhang, H., & Dong, Z. (2022). Performance zoning method of asphalt pavement in cold regions based on climate Indexes: A case study of Inner Mongolia, China. Construction and Building Materials, 361, 129650. doi:10.1016/j.conbuildmat.2022.129650.
[17] Ali, A. A., Milad, A., Hussein, A., Md Yusoff, N. I., & 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. doi:10.1016/j.jreng.2023.04.002.
[18] 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. doi:10.1016/j.conbuildmat.2020.118513.
[19] Shahin, M. Y. (2005). Pavement management for airports, roads, and parking lots: Second edition. Pavement Management for Airports, Roads, and Parking Lots (2nd Ed.), Springer., Cham, Switzerland.
[20] Nur, W., Subagio, B. S., & Hariyadi, E. S. (2019). Relationship between the Pavement Condition Index (PCI), Present Serviceability Index (PSI), and Surface Distress Index on Soekarno Hatta Road, Bandung. Jurnal Teknik Sipil, 26(2), 111–120. doi:10.5614/jts.2019.26.2.3.
[21] Wang, Z. (2000). Formulation and assessment of a customizable procedure for pavement distress index. Ph.D. Thesis, The University of Tennessee, Knoxville, United States.
[22] Wu, K. (2015). Development of PCI-based pavement performance model for management of road infrastructure system. Master Thesis, Arizona State University, Tempe, United States.
[23] Cho, N. H., Kwon, H. J., Suh, Y. C., & Kim, J. (2022). Development of Korea Airport Pavement Condition Index for Panel Rating. Applied Sciences (Switzerland), 12(16), 8320. doi:10.3390/app12168320.
[24] Guha, S. K., Hossain, K., & Lawlor, M. (2022). An Economic Pavement Management Framework for Extremely Budget-and Resource-Constrained Agencies in Canada. Transportation Research Record, 2676(8), 554–570. doi:10.1177/03611981221084687.
[25] Subagio, B. S., Prayoga, A. B., & Fadilah, S. R. (2022). Implementation of Mechanistic-Empirical Pavement Design Guide against Indonesian Conditions using Arizona Calibration. The Open Civil Engineering Journal, 16(1), 1-11. doi:10.2174/18741495-v16-e221026-2022-45.
[26] Qureshi, W. S., Power, D., Ullah, I., Mulry, B., Feighan, K., McKeever, S., & O'Sullivan, D. (2023). Deep learning framework for intelligent pavement condition rating: A direct classification approach for regional and local roads. Automation in Construction, 153, 104945. doi:10.1016/j.autcon.2023.104945.
[27] Jeong, H., Kim, H., Kim, K., & Kim, H. (2017). Prediction of flexible pavement deterioration in relation to climate change using fuzzy logic. Journal of infrastructure systems, 23(4), 04017008. doi:10.1061/(ASCE)IS.1943-555X.0000363.
[28] Miah, M. T., Oh, E., Chai, G., & Bell, P. (2020). An overview of the airport pavement management systems (APMS). International Journal of Pavement Research and Technology, 13, 581-590. doi:10.1007/s42947-020-6011-8.
[29] ASTM D6433-18. (2020). Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys. ASTM International, Pennsylvania, United States. doi:10.1520/D6433-18.
[30] Ali, A., Dhasmana, H., Hossain, K., & Hussein, A. (2021). Modeling pavement performance indices in harsh climate regions. Journal of Transportation Engineering, Part B: Pavements, 147(4), 04021049. doi:10.1061/JPEODX.0000305.
[31] Setiaputri, H. A., Isradi, M., Rifai, A. I., Mufhidin, A., & Prasetijo, J. (2021). Analysis Of Urban Road Damage With Pavement Condition Index (PCI) And Surface Distress Index (SDI) Methods. World Journal of Innovation and Technology, 2(2), 82-91..
[32] Abu Dabous, S., Zeiada, W., Al-Ruzouq, R., Hamad, K., & Al-Khayyat, G. (2021). Distress-based evidential reasoning method for pavement infrastructure condition assessment and rating. International Journal of Pavement Engineering, 22(4), 455–466. doi:10.1080/10298436.2019.1622012.
[33] Twomey, J. M., & Smith, A. E. (1995). Performance measures, consistency, and power for artificial neural network models. Mathematical and Computer Modelling, 21(1–2), 243–258. doi:10.1016/0895-7177(94)00207-5.
[34] Gharieb, M., Nishikawa, T., Nakamura, S., & Thepvongsa, K. (2022). Modeling of Pavement Roughness Utilizing Artificial Neural Network Approach for Laos National Road Network. Journal of Civil Engineering and Management, 28(4), 261–277. doi:10.3846/jcem.2022.15851.
[35] Cano-Ortiz, S., Pascual-Muñoz, P., & Castro-Fresno, D. (2022). Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139. doi:10.1016/j.autcon.2022.104309.
[36] Tofallis, C. (2013). Measuring Relative Accuracy: A Better Alternative to Mean Absolute Percentage Error. SSRN Electronic Journal, 1-24. doi:10.2139/ssrn.2350688.
[37] Montgomery, D. C., Runger, G. C., & Hubele, N. F. (2015). Engineering Statistics (5th Ed.). John Wiley & Sons, Hoboken, United States.
[38] Loprencipe, G., & Pantuso, A. (2017). A specified procedure for distress identification and assessment for urban road surfaces based on PCI. Coatings, 7(5), 65. doi:10.3390/coatings7050065.
[2] Peraka, N. S. P., & Biligiri, K. P. (2020). Pavement asset management systems and technologies: A review. Automation in Construction, 119(March), 103336. doi:10.1016/j.autcon.2020.103336.
[3] Zukhruf, F., Balijepalli, C., Bona Frazila, R., Suryo Nugroho, T., & Tsavalista Burhani, J. (2023). A new model of pavement maintenance, rehabilitation and reconstruction considering multimodal network development. Engineering Optimization, 55(7), 1118–1132. doi:10.1080/0305215X.2022.2061477.
[4] Issa, A., Samaneh, H., & Ghanim, M. (2022). Predicting pavement condition index using artificial neural networks approach. Ain Shams Engineering Journal, 13(1), 101490. doi:10.1016/j.asej.2021.04.033.
[5] Shtayat, A., Moridpour, S., Best, B., Shroff, A., & Raol, D. (2020). A review of monitoring systems of pavement condition in paved and unpaved roads. Journal of Traffic and Transportation Engineering, 7(5), 629–638. doi:10.1016/j.jtte.2020.03.004.
[6] Attoh-Okine, N., & Adarkwa, O. (2013). Pavement condition surveys–overview of current practices. Delaware Center for Transportation, University of Delaware, Newark, United States.
[7] Nautiyal, A., & Sharma, S. (2022). Methods and factors of prioritizing roads for maintenance: a review for sustainable flexible pavement maintenance program. Innovative Infrastructure Solutions, 7(3), 190. doi:10.1007/s41062-022-00771-6.
[8] Hadiwardoyo, S. P., Correia, A. G., & Pereira, P. (2015). Road deterioration analysis for the national roads of Indonesia. Proceedings of the 14th International Conference on QIR (Quality in Research), 20-13 August, 2015, Lombok, Indonesia.
[9] Hamdi, Hadiwardoyo, S. P., Correia, A. G., Pereira, P., & Cortez, P. (2017). Prediction of surface distress using neural networks. AIP Conference Proceedings, 1855. doi:10.1063/1.4985502.
[10] Rosada, A., Arliansyah, J., & Buchari, E. (2019). Evaluation Pavement Deteriorating Condition on Surface Distress Index (SDI) Data Using Radial Basis Function Neural Networks (RBFNN). Journal of Physics: Conference Series, 1198(3), 1–8. doi:10.1088/1742-6596/1198/3/032008.
[11] Setiadji, B. H., Purwanto, D., & Wicaksono, Y. I. (2020). Improvement of Potholes and Rutting Assessment in Surface Distress Index. In 2nd International Symposium on Transportation Studies in Developing Countries (ISTSDC 2019), Atlantis Press, 162-166. doi:10.2991/aer.k.200220.034.
[12] SMD-03/RCS. (2011). Road Condition Survey Guideline. Implementation of Indonesian integrated road management system (IIRMS), Jakarta, Indonesia. (In Indonesian).
[13] Setiadji, B. H. (2019). Proposed SDI equations to improve the effectiveness in evaluating crack damage on the road pavement. IOP Conference Series: Materials Science and Engineering, 650(1), 012007. doi:10.1088/1757-899X/650/1/012007.
[14] Setiadji, B., Supriyono, & Purwanto, D. (2019). Surface Distress Index Updates to Improve Crack Damage Evaluation. Advances in Engineering Research, 186(APTE), 48–55,. doi:10.2991/apte-18.2019.10.
[15] Pinatt, J. M., Chicati, M. L., Ildefonso, J. S., & Filetti, C. R. G. D. arc. (2020). Evaluation of pavement condition index by different methods: Case study of Maringá, Brazil. Transportation Research Interdisciplinary Perspectives, 4, 100100. doi:10.1016/j.trip.2020.100100.
[16] Zhao, K., Ma, X., Zhang, H., & Dong, Z. (2022). Performance zoning method of asphalt pavement in cold regions based on climate Indexes: A case study of Inner Mongolia, China. Construction and Building Materials, 361, 129650. doi:10.1016/j.conbuildmat.2022.129650.
[17] Ali, A. A., Milad, A., Hussein, A., Md Yusoff, N. I., & 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. doi:10.1016/j.jreng.2023.04.002.
[18] 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. doi:10.1016/j.conbuildmat.2020.118513.
[19] Shahin, M. Y. (2005). Pavement management for airports, roads, and parking lots: Second edition. Pavement Management for Airports, Roads, and Parking Lots (2nd Ed.), Springer., Cham, Switzerland.
[20] Nur, W., Subagio, B. S., & Hariyadi, E. S. (2019). Relationship between the Pavement Condition Index (PCI), Present Serviceability Index (PSI), and Surface Distress Index on Soekarno Hatta Road, Bandung. Jurnal Teknik Sipil, 26(2), 111–120. doi:10.5614/jts.2019.26.2.3.
[21] Wang, Z. (2000). Formulation and assessment of a customizable procedure for pavement distress index. Ph.D. Thesis, The University of Tennessee, Knoxville, United States.
[22] Wu, K. (2015). Development of PCI-based pavement performance model for management of road infrastructure system. Master Thesis, Arizona State University, Tempe, United States.
[23] Cho, N. H., Kwon, H. J., Suh, Y. C., & Kim, J. (2022). Development of Korea Airport Pavement Condition Index for Panel Rating. Applied Sciences (Switzerland), 12(16), 8320. doi:10.3390/app12168320.
[24] Guha, S. K., Hossain, K., & Lawlor, M. (2022). An Economic Pavement Management Framework for Extremely Budget-and Resource-Constrained Agencies in Canada. Transportation Research Record, 2676(8), 554–570. doi:10.1177/03611981221084687.
[25] Subagio, B. S., Prayoga, A. B., & Fadilah, S. R. (2022). Implementation of Mechanistic-Empirical Pavement Design Guide against Indonesian Conditions using Arizona Calibration. The Open Civil Engineering Journal, 16(1), 1-11. doi:10.2174/18741495-v16-e221026-2022-45.
[26] Qureshi, W. S., Power, D., Ullah, I., Mulry, B., Feighan, K., McKeever, S., & O'Sullivan, D. (2023). Deep learning framework for intelligent pavement condition rating: A direct classification approach for regional and local roads. Automation in Construction, 153, 104945. doi:10.1016/j.autcon.2023.104945.
[27] Jeong, H., Kim, H., Kim, K., & Kim, H. (2017). Prediction of flexible pavement deterioration in relation to climate change using fuzzy logic. Journal of infrastructure systems, 23(4), 04017008. doi:10.1061/(ASCE)IS.1943-555X.0000363.
[28] Miah, M. T., Oh, E., Chai, G., & Bell, P. (2020). An overview of the airport pavement management systems (APMS). International Journal of Pavement Research and Technology, 13, 581-590. doi:10.1007/s42947-020-6011-8.
[29] ASTM D6433-18. (2020). Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys. ASTM International, Pennsylvania, United States. doi:10.1520/D6433-18.
[30] Ali, A., Dhasmana, H., Hossain, K., & Hussein, A. (2021). Modeling pavement performance indices in harsh climate regions. Journal of Transportation Engineering, Part B: Pavements, 147(4), 04021049. doi:10.1061/JPEODX.0000305.
[31] Setiaputri, H. A., Isradi, M., Rifai, A. I., Mufhidin, A., & Prasetijo, J. (2021). Analysis Of Urban Road Damage With Pavement Condition Index (PCI) And Surface Distress Index (SDI) Methods. World Journal of Innovation and Technology, 2(2), 82-91..
[32] Abu Dabous, S., Zeiada, W., Al-Ruzouq, R., Hamad, K., & Al-Khayyat, G. (2021). Distress-based evidential reasoning method for pavement infrastructure condition assessment and rating. International Journal of Pavement Engineering, 22(4), 455–466. doi:10.1080/10298436.2019.1622012.
[33] Twomey, J. M., & Smith, A. E. (1995). Performance measures, consistency, and power for artificial neural network models. Mathematical and Computer Modelling, 21(1–2), 243–258. doi:10.1016/0895-7177(94)00207-5.
[34] Gharieb, M., Nishikawa, T., Nakamura, S., & Thepvongsa, K. (2022). Modeling of Pavement Roughness Utilizing Artificial Neural Network Approach for Laos National Road Network. Journal of Civil Engineering and Management, 28(4), 261–277. doi:10.3846/jcem.2022.15851.
[35] Cano-Ortiz, S., Pascual-Muñoz, P., & Castro-Fresno, D. (2022). Machine learning algorithms for monitoring pavement performance. Automation in Construction, 139. doi:10.1016/j.autcon.2022.104309.
[36] Tofallis, C. (2013). Measuring Relative Accuracy: A Better Alternative to Mean Absolute Percentage Error. SSRN Electronic Journal, 1-24. doi:10.2139/ssrn.2350688.
[37] Montgomery, D. C., Runger, G. C., & Hubele, N. F. (2015). Engineering Statistics (5th Ed.). John Wiley & Sons, Hoboken, United States.
[38] Loprencipe, G., & Pantuso, A. (2017). A specified procedure for distress identification and assessment for urban road surfaces based on PCI. Coatings, 7(5), 65. doi:10.3390/coatings7050065.
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