Karafan Journal

Karafan Journal

Hybrid model of Least Square-Support Vector Machine and Particle Swarm Optimization for prediction of pavement International Roughness Index

Document Type : Original Article

Authors
Morteza Araghi 1
Mohsen Khatibinia 2
Sadegh Moodi 3
1 Assistant Professor,, Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
2 Associate Professor, Department of Civil Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran
3 Assistant Professor, Department of Civil Engineering, Faculty of Mining, Civil and Chemical Engineering, Birjand University of Technology, Birjand, Iran.
10.48301/kssa.2025.499285.3119
Abstract
The prediction of pavement performance is crucial for the effective management of road infrastructure, because it helps identify and prioritize maintenance and renovation activities. Making informed and appropriate decisions regarding the repair, improvement, and reconstruction of road and highway pavements requires an accurate assessment of the quality of service and performance of the pavement over years of operation. The International Roughness Index (IRI) is used as an efficient index for evaluating and analyzing the surface roughness of pavements. In this study, a hybrid model of the Least Squares-Support Vector Machine (LS-SVM) and Particle Swarm Optimization (PSO) was developed. In the proposed model, the optimal values of the tuning parameters of the LS-SVM were determined using PSO. Furthermore, the k-fold cross-validation technique was used to prevent overfitting of the LS-SVM in the training phase. Two laboratory databases of the Long-Term Pavement Performance program (LTPP) were used to evaluate the performance of the proposed hybrid model in predicting IRI. The accuracy evaluation of the combined LS-SVM and PSO model based on the coefficient of determination (R2) criterion for the two databases was 0.994 and 0.997, respectively. Finally, a comparison of the performance of the hybrid model with other machine learning methods shows that the hybrid model of LS-SVM and PSO has a high accuracy.
Keywords
Road pavement
International Roughness Index
LTPP database
Least Squares-Support Vector Machine
Particle Swarm Optimization
K-fold Cross Validation
Subjects
 
[1] Tamagusko, T., & Ferreira, A. (2023). Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions. Infrastructures, 8(12), 170. https://doi.org/10.3390/infrastructures8120170.
[2] Kulkarni, R. B., & Miller, R. W. (2003). Pavement Management Systems: Past, Present, and Future. Transportation Research Record, 1853(1), 65-71. https://doi.org/10.3141/1853-08.
[3] Gupta, A., Kumar, P., & Rastogi, R. (2014). Critical review of flexible pavement performance models. KSCE Journal of Civil Engineering, 18(1), 142-148. https://doi.org/10.1007/s12205-014-0255-2.
[4] Santos, J., & Ferreira, A. (2012). Pavement design optimization considering costs and preventive interventions. Journal of Transportation Engineering, 138(7), 911-923. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000390.
[5] Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172. https://doi.org/10.1177/1094670517752459.
[6] Wang, W., & Siau, K. (2019). Artificial intelligence, machine learning, automation, robotics, future of work and future of humanity: A review and research agenda. Journal of Database Management (JDM), 30(1), 61-79. https://doi.org/10.4018/JDM.2019010104.
[7] Gong, H., Sun, Y., Hu, W., Polaczyk, P. A., & Huang, B. (2019). Investigating impacts of asphalt mixture properties on pavement performance using LTPP data through random forests. Construction and Building Materials, 204, 203-212. https://doi.org/10.1016/j.conbuildmat.2019.01.198.
[8] Jarrahi, H., Asadi, A., Khatibinia, M., & Etedali, S. (2020). Optimal design of rotational friction dampers for improving seismic performance of inelastic structures. Journal of Building Engineering, 27, 100960. https://doi.org/10.1016/j.jobe.2019.100960.
[9] Khatibinia, M., Akbari, S., Yazdani, H., & Gharehbaghi, S. (2023). Damage-based optimal control of steel moment-resisting frames equipped with tuned mass dampers. Journal of Vibration and Control, 30(3-4), 659-672. https://doi.org/10.1177/10775463221149462.
[10] Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260. https://doi.org/10.1126/science.aaa8415.
[11] Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S. A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), 189. https://doi.org/10.3390/su11010189.
[12] Mehdipour, M., Chitsaz, E., & Etemadi, M. (2024). A Perspective on Human Interaction and Artificial Intelligence: Bibliometric Analysis with Co-occurrence Technique. Karafan Journal, 21(3), 13-33. https://doi.org/10.48301/kssa.2024.428257.2778.
[13] Deypir, M., Maarefi, A., & Zoughi, T. (2024). A Hybrid Multivariate Routing Approach for Improving Efficiency in VANETS. Karafan Journal, 21(1), 39-62. https://doi.org/10.48301/kssa.2024.423334.2752.
[14] Rahimi, K., Agha gholizadeh sayar, A., & Izadyar, M. (2024). Presenting a Sustainable Performance Strategy in the Automotive Supply Chain Using Fuzzy Network Analysis. Karafan Journal, 21(1), 335-354. https://doi.org/10.48301/kssa.2023.414800.2691.
[15] Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc.
[16] Yao, L., Leng, Z., Jiang, J., & Ni, F. (2021). Modelling of pavement performance evolution considering uncertainty and interpretability: a machine learning based framework. International Journal of Pavement Engineering, 23, 5211 - 5226. https://doi.org/10.1080/10298436.2021.2001814.
[17] Justo-Silva, R., Ferreira, A., & Flintsch, G. (2021). Review on machine learning techniques for developing pavement performance prediction models. Sustainability, 13(9), 5248. https://doi.org/10.3390/su13095248.
[18] Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., & Brennan, S. E. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj, 372. https://doi.org/10.1136/bmj.n71.
[19] Sayers, M. W., Gillespie, T. D., & Queiroz, C. A. (1986). The international road roughness experiment: Establishing correlation and a calibration standard for measurements. http://documents.worldbank.org/curated/en/326081468740204115/The-International-Road-Roughness-Experiment-IRRE-establishing-correlation-and-a-calibration-standard-for-measurements.
[20] Hoque, Z. (2006). Highway condition surveys and serviceability evaluation.
[21] Hall, K., & Muñoz, C. (1999). Estimation of present serviceability index from international roughness index. Transportation Research Record, 1655(1), 93-99. https://doi.org/10.3141/1655-13.
[22] Hossain, M. I., Gopisetti, L. S. P., & Miah, M. S. (2019). International Roughness Index Prediction of Flexible Pavements Using Neural Networks. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018058. https://doi.org/10.1061/JPEODX.0000088.
[23] Abdelaziz, N., Abd El-Hakim, R. T., El-Badawy, S. M., & Afify, H. A. (2020). International Roughness Index prediction model for flexible pavements. International Journal of Pavement Engineering, 21(1), 88-99. https://doi.org/10.1080/10298436.2018.1441414.
[24] Zeiada, W., Dabous, S. A., Hamad, K., Al-Ruzouq, R., & Khalil, M. A. (2020). Machine learning for pavement performance modelling in warm climate regions. Arabian journal for science and engineering, 45(5), 4091-4109. https://doi.org/10.1007/s13369-020-04398-6.
[25] Gong, H., Sun, Y., Shu, X., & Huang, B. (2018). Use of random forests regression for predicting IRI of asphalt pavements. Construction and Building Materials, 189, 890-897. https://doi.org/10.1016/j.conbuildmat.2018.09.017.
[26] Naseri, H., Jahanbakhsh, H., Foomajd, A., Galustanian, N., Karimi, M. M., & D. Waygood, E. (2023). A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying Whale Optimization Algorithm and random forest regression. International Journal of Pavement Engineering, 24(2), 2147672. https://doi.org/10.1080/10298436.2022.2147672.
[27] Sharma, A., Sachdeva, S. N., & Aggarwal, P. (2023). Predicting IRI using machine learning techniques. International Journal of Pavement Research and Technology, 16(1), 128-137. https://doi.org/10.1007/s42947-021-00119-w.
[28] Naseri, H., Shokoohi, M., Jahanbakhsh, H., Karimi, M. M., & Waygood, E. (2023). Novel soft-computing approach to better predict flexible pavement roughness. Transportation Research Record, 2677(10), 246-259. https://doi.org/10.1177/036119812311610.
[29] Zhang, M., Gong, H., Jia, X., Xiao, R., Jiang, X., Ma, Y., & Huang, B. (2020). Analysis of critical factors to asphalt overlay performance using gradient boosted models. Construction and Building Materials, 262, 120083. https://doi.org/10.1016/j.conbuildmat.2020.120083.
[30] Damirchilo, F., Hosseini, A., Mellat Parast, M., & Fini, E. H. (2021). Machine learning approach to predict international roughness index using long-term pavement performance data. Journal of Transportation Engineering, Part B: Pavements, 147(4), 04021058. https://doi.org/10.1061/JPEODX.0000312.
[31] Song, Y., Wang, Y. D., Hu, X., & Liu, J. (2022). An efficient and explainable ensemble learning model for asphalt pavement condition prediction based on LTPP dataset. IEEE Transactions on Intelligent Transportation Systems, 23(11),  22084-22093. https://doi.org/10.1109/TITS.2022.3164596.
[32] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018.
[33] Suykens, J. A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural processing letters, 9(3), 293-300. https://doi.org/10.1023/A:1018628609742
[34] Yu, L., Yao, X., Wang, S., & Lai, K. K. (2011). Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection. Expert Systems with Applications, 38(12), 15392-15399. https://doi.org/10.1016/j.eswa.2011.06.023.
[35] Kennedy, J., & Eberhart, R. (1995, 27 Nov.-1 Dec. 1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks.
[36] Shayesteh Bilondi, M. R., Yazdani, H., & Khatibinia, M. (2018). Seismic energy dissipation-based optimum design of tuned mass dampers. Structural and Multidisciplinary Optimization, 58(6), 2517-2531. https://doi.org/10.1007/s00158-018-2033-0.
[37] Shi, Y., & Eberhart, R. (1998, 4-9 May 1998). A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[38] He, S., Wu, Q. H., Wen, J. Y., Saunders, J. R., & Paton, R. C. (2004). A particle swarm optimizer with passive congregation. Biosystems, 78(1), 135-147. https://doi.org/10.1016/j.biosystems.2004.08.003.
[39] Guo, X. C., Yang, J. H., Wu, C. G., Wang, C. Y., & Liang, Y. C. (2008). A novel LS-SVMs hyper-parameter selection based on particle swarm optimization. Neurocomputing, 71(16), 3211-3215. https://doi.org/10.1016/j.neucom.2008.04.027.
[40] Chen, K.-Y. (2007). Forecasting systems reliability based on support vector regression with genetic algorithms. Reliability Engineering & System Safety, 92(4), 423-432. https://doi.org/10.1016/j.ress.2005.12.014.
[41] Duan, K., Keerthi, S. S., & Poo, A. N. (2003). Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing, 51, 41-59. https://doi.org/10.1016/S0925-2312(02)00601-X.
[42] Ozbay, K., & Laub, R. (2001). Models for pavement deterioration using LTPP.
[43] Mazari, M., & Rodriguez, D. D. (2016). Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. Journal of Traffic and Transportation Engineering (English Edition), 3(5), 448-455. https://doi.org/10.1016/j.jtte.2016.09.007.
[44] Mohammadizadeh, M. R., Esfandnia, F., & Khatibinia, M. (2023). Prediction of Shear Strength of Reinforced Concrete Deep Beams Using Neuro-Fuzzy Inference System and Meta-Heuristic Algorithms. Civil Engineering Infrastructures Journal, 56(1), 137-157. https://doi.org/10.22059/ceij.2022.334953.1803.
[45] Terzi, S. (2013). Modeling for pavement roughness using the ANFIS approach. Advances in Engineering Software, 57, 59-64. https://doi.org/10.1016/j.advengsoft.2012.11.013.
Karafan Journal
Volume 22, Issue 3
Technical and Engineering
Autumn 2025
Pages 267-286

  • Receive Date 12 January 2025
  • Revise Date 05 March 2025
  • Accept Date 22 April 2025