[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.
