[1] Andrei, D., Mirza, W. and Witczak, M.W. (1999). Development of a Revised Predictive Model for the Dynamic (Complex) Modulus of Asphalt Mixtures. NCHRP Report 1-37A.
[2] Bari, J. and Witczak, M.W. (2006). Development of a New Revised Version of the Witczak E* Predictive Model for Hot Mix Asphalt Mixtures. Journal Association of Asphalt Paving Technologists: 75, 381-424.
[3] Christensen, D. W, Pellinen, T., Bonaquist, R.F. (2003). Hirsch Model for Estimating the Modulus of Asphalt Concrete. Journal Association of Asphalt Paving Technologists: 72, 97-121.
[4] Zhang, C. and Shen, S. (2017). Modification of the Hirsch dynamic modulus prediction model for asphalt mixtures. Journal of Materials in Civil Engineering, 29(12): 04017241. https://doi.org/10.1061/(ASCE)MT.1943-5533.0002099
[5] Al-Khateeb G., Shenoy A., Gibson N., and Harman T. (2006). A New Simplistic Model for Dynamic Modulus Predictions of Asphalt Paving Mixtures. Journal of the Association of Asphalt Paving Technologists: 75, 1254–1293.
[6] Picado-Santos, L., Capitao, S. D. and Pais, J. C. (2003). Stiffness modulus and phase angle prediction models for high modulus bituminous mixtures. International Journal of Pavements: Volume 2, issue 3.
[7] Biligiri, K. P., Kaloush, K., and Uzan, J. (2010). Evaluation of asphalt mixtures' viscoelastic properties using phase angle relationships. International Journal of Pavement Engineering, 11(2), 143-152. https://doi.org/10.1080/10298430903033354
[8] Naik, A. K. and Biligiri, K. P. (2014). Predictive Models to Estimate Phase Angle of Asphalt Mixtures. Journal of Materials in Civil Engineering: Volume 27, Issue 8. https://doi.org/10.1061/(ASCE)MT.1943-5533.0001197
[9] Yang, X., and You, Z. (2015). New Predictive Equations for Dynamic Modulus and Phase Angle Using a Nonlinear Least-Squares Regression Model. Journal of Materials in Civil Engineering: 27(3). https://doi.org/10.1061/(ASCE)MT.1943-5533.0001070
[10] Venudharan, V. and Biligiri, K. P. (2015). Estimation of phase angles of asphalt mixtures using resilient modulus test. Construction and Building Materials: Volume 82, 274-286. https://doi.org/10.1016/j.conbuildmat.2015.02.061
[11] Justo-Silva, R., Ferreira, A. and Flintsch, G. (2021). Review on Machine Learning Techniques for Developing Pavement Performance Prediction Models. Sustainability: 13, 5248. https://doi.org/10.3390/su13095248
[12] Chaabene, W. B., Flah, M. and Nehdi, M. L. (2020). Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials: 260(7), 1-18. https://doi.org/10.1016/j.conbuildmat.2020.119889
[13] Khambra, G. and Shukla, P. (2021). Novel machine learning applications on fly ash based concrete: An overview. Materials Today: Proceedings: 80(6245). https://doi.org/10.1016/j.matpr.2021.07.262
[14] Beskopylny, A.N., Stelmakh, S.A., Shcherban, E.M., Mailyan, L.R., Meskhi, B., Razveeva, I., Chernilnik, A. and Beskopylny, N. (2022). Concrete Strength Prediction Using Machine Learning Methods: CatBoost, k-Nearest Neighbors, Support Vector Regression. Applied Sciences. 12, 10864. https://doi.org/10.3390/app122110864
[15] Zhang, D., Hang, J., Zhang, H, Huang, C., Qi, C. and Chang, E. (2020). Machine learning-based prediction of soil compression modulus with application of 1D settlement. Journal of Zhejiang University-Science A (Applied Physics & Engineering): 21(6):430-444. https://doi.org/10.1631/jzus.A1900515
[16] Choi, H.J., Kim, S., Kim, Y. and Won, J. (2022). Predicting Frost Depth of Soils in South Korea Using Machine Learning Techniques. Sustainability: 14, 9767. https://doi.org/10.3390/su14159767
[17] Bajic, M., Pour, S. M., Skar, A., Pettinari, M., Levenberg, E. and Alstrøm, T. S. (2021). Road Roughness Estimation Using Machine Learning. arXiv preprint arXiv:2107.01199.. https://doi.org/10.48550/arXiv.2107.01199
[18] Suleymanov, A., Tuktarova, I., Belan, L. et al. (2023). Spatial prediction of soil properties using random forest, k-nearest neighbors and cubist approaches in the foothills of the Ural Mountains, Russia. Model. Earth Syst. Environ. 9, 3461–3471. https://doi.org/10.1007/s40808-023-01723-4
[19] Huang, Y., Molavi Nojumi, M., Ansari, S., Hashemian, L., & Bayat, A. (2023). Evaluating the use of machine learning for moisture content prediction in base and subgrade layers. Road Materials and Pavement Design, 24(12), 2910–2928. https://doi.org/10.1080/14680629.2023.2182135
[20] Ghorbani, B., Yaghoubi, E., Wasantha, P. L. P., van Staden, R., Guerrieri, M., & Fragomeni, S. (2023). Machine learning-based prediction of resilient modulus for blends of tire-derived aggregates and demolition wastes. Road Materials and Pavement Design, 25(4), 694–715. https://doi.org/10.1080/14680629.2023.2222176
[21] Atakan, M., & Yıldız, K. (2023). Prediction of Marshall design parameters of asphalt mixtures via machine learning algorithms based on literature data. Road Materials and Pavement Design, 25(3), 454–473. https://doi.org/10.1080/14680629.2023.2213774
[22] Tangga, A.A., Mufargi, H.A.L., Milad, A. et al. (2024). Utilising machine learning algorithms to predict the Marshall characteristics of asphalt pavement layers. Innov. Infrastruct. Solut. 9, 381. https://doi.org/10.1007/s41062-024-01698-w
[23] Upadhya, A., Thakur, M.S. & Sihag, P. (2024). Predicting Marshall Stability of Carbon Fiber-Reinforced Asphalt Concrete Using Machine Learning Techniques. Int. J. Pavement Res. Technol. 17, 102–122. https://doi.org/10.1007/s42947-022-00223-5
[24] Alnaqbi, A., Zeiada, W. & Al-Khateeb, G.G. (2024). Machine learning modeling of pavement performance and IRI prediction in flexible pavement. Innov. Infrastruct. Solut. 9, 385. https://doi.org/10.1007/s41062-024-01688-y
[25] Gul, M. A., Islam, M. K., Awan, H. H., Sohail, M., Al Fuhaid, A. F., Arifuzzaman, M. and Qureshi, H. J. (2022). Prediction of Marshall Stability and Marshall Flow of Asphalt Pavements Using Supervised Machine Learning Algorithms. Symmetry: 14, 2324. https://doi.org/10.3390/sym14112324
[26] Uwanuakwa, I.D., Busari, A., Ali, S.I.A. et al. (2022). Comparing Machine Learning Models with Witczak NCHRP 1-40D Model for Hot-Mix Asphalt Dynamic Modulus Prediction. Arab J Sci Eng 47, 13579–13591. https://doi.org/10.1007/s13369-022-06935-x
[27] Martínez, F. and Angelone, S. (2010). The Estimation of the Dynamic Modulus of Asphalt Mixtures Using Artificial Neural Networks. 11th International Conference on Asphalt Pavements: Volume 1, 354-363.
[28] Behnood, A. and Golafshan, E. M. (2021). Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming. Construction and Building Materials, Volume 266, Part A. https://doi.org/10.1016/j.conbuildmat.2020.120983
[29] Leiva-Villacorta, F. and Vargas-Nordcbeck, A. 2019. Neural network based model to estimate dynamic modulus E* for mixtures in Costa Rica. J. Soft Comput. Civ. Eng.: 3(2):01–15. https://doi.org/10.22115/scce.2019.188006.1110
[30] Useche-Castelblanco, J.S., Reyes-Ortiz, O. J. and Alvarez, A. E. (2023). Application of machine learning models for prediction of rheological properties of wax-modified asphalt binders. Construction and Building Materials: Volume 395, 132352. https://doi.org/10.1016/j.conbuildmat.2023.132352
[31] Rahman, S., Bhasin A. and Smit, A. (2021). Exploring the use of machine learning to predict metrics related to asphalt mixture performance. Construction and Building Materials: Volume 295, 123585. https://doi.org/10.1016/j.conbuildmat.2021.123585
[32] Botella, R., Lo Presti, D., Vasconcelos, K. et al. (2022). Machine learning techniques to estimate the degree of binder activity of reclaimed asphalt pavement. Materials and Structures: 55, 112. https://doi.org/10.1617/s11527-022-01933-9
[33] Majidifard, H., Jahangiri, B., Buttlar, W. G. and Alavi, A. H. (2019). New Machine Learning-based Prediction Models for Fracture Energy of Asphalt Mixtures. Measurement: 135, 438–451. https://doi.org/10.1016/j.measurement.2018.11.081
[34] Shu, X., and Huang, B. (2009). Predicting Dynamic Modulus of Asphalt Mixtures with Differential Method. Road Materials and Pavement Design, 10(2), 337–359. https://doi.org/10.1080/14680629.2009.9690198
[35] Rondinella, F., Daneluz, F., Baldo, N. and Hofko, B. (2023). Improved predictions of asphalt concretes’ dynamic modulus and phase angle using decision-tree based categorical boosting model. Construction and Building Materials: 400 (2023) 132709. https://doi.org/10.1016/j.conbuildmat.2023.132709
[36] Rondinella, F., Daneluz, F., Hofko, B., Baldo, N. (2024). A Machine Learning Approach for the Simultaneous Prediction of Dynamic Modulus and Phase Angle of Asphalt Concrete Mixtures. Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2023. Communications in Computer and Information Science, vol 1935. Springer, Cham. . https://doi.org/10.1007/978-3-031-48858-0_40
[37] Ibrahim, A., Osman, M. K. Yusof, N.A., Ahmad, K., Harun, N. H. and Raof. R.A. (2019). Characterization of cracking in pavement distress using image processing techniques and k-Nearest neighbour. Indonesian Journal of Electrical Engineering and Computer Science. Vol. 14, No. 2, pp. 810-818. http://doi.org/10.11591/ijeecs.v14.i2.pp810-818
[38] Ghouchan Nezhad Noor Nia, R.; Jalali, M.; Houshmand, M. A. (2022). Graph-Based k-Nearest Neighbor (KNN) Approach for Predicting Phases in High-Entropy Alloys. Applied Sciences, 12, 8021. https://doi.org/10.3390/app12168021
[39] Aksoy, A., Iskender, E. and Kahraman, H. T. (2012). Application of the intuitive KNN Estimator for prediction of the Marshall Test (ASTM D1559) results for asphalt mixtures. Construction and Building Materials, 34, pp. 561–569. https://doi.org/10.1016/j.conbuildmat.2012.02.091
[40] Al-Dosary, N. M. N, Al-Hamed, S. A. and Aboukarima, A.M. (2019). K-Nearest Neighbors method for prediction of fuel consumption in tractor-chisel plow systems. Eng. Agric. (Online): 39 (6), 729–736. https://doi.org/10.1590/1809-4430-Eng.Agric.v39n6p729-736/2019
[41] Wang, Y. and Wang, Z. (2005). Text categorization rule extraction based on fuzzy decision tree. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Vol 4, pp. 2122-2127, China. https://doi.org/10.1109/ICMLC.2005.1527296
[42] Ahmed, MS., N’diaye, M., Attouch, M.K. et al. (2023). K-nearest neighbors prediction and classification for spatial data. J. Spat. Econometrics. 4, 12. https://doi.org/10.1007/s43071-023-00041-2
[43] Ikeagwuani, C.C., Nweke, C.C. & Onah, H.N. (2023) Prediction of resilient modulus of fine-grained soil for pavement design using KNN, MARS, and random forest techniques. Arabian Journal of Geosciences. 16, 388. https://doi.org/10.1007/s12517-023-11469-z
[44] Weiss, S.M., and Kulikowski, C.A. (1991). Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems. Morgan Kaufmann Publishing, San Mateo, San Mateo, CA. United States. ISBN: 978-1-55860-065-2
[45] Dhar, V., and Stein, R. (1997). Intelligent Decision Support Methods: The Science of Knowledge Work. Prentice-Hall, Upper Saddle River, NJ. ISBN-13978-0135199350
[46] Witczak, M. (2005). NCHRP Project No. 9-19, Superpave Support and Performance Models Management, Database Dynamic Modulus (E*) Test and Master Curves (AC Mixture Simple Performance Test). Final Report.
[47] Witczak, M. W., and Fonseca, O. A. (1996). Revised Predictive Model for Dynamic (Complex) Modulus of Asphalt Mixtures. Transportation Research Record: 1540 (1), 15–23. https://doi.org/10.1177/036119819615400010
[48] Fintsch, G., Loulizi, A., Diefenderfer, S. D., Galal, K.A. and Diefenderfer, B. K. (2007). Asphalt Materials Characterization in Support of Implementation of the Proposed Mechanistic-Empirical Pavement Design Guide. Report No.: VTRC 07-CR10, Virginia Tech Transportation Institute.
[49] Birgisson, B., Roque, R., Kim, J. and Pham, L. J. (2004). The use of complex modulus to characterize the performance of asphalt mixtures and pavements in Florida. UF Project No.: 4910-4504-784-12, Department of Civil and Coastal Engineering, College of Engineering, University of Florida.
[50] ASTM International. (2009). ASTM D2493. Standard Viscosity–Temperature Chart for Asphalts. American Society for Testing and Materials, Pennsylvania, USA.
[51] Imandoust, S. B. and Bolandraftar, M. (2013). Application of knearest neighbor (KNN) approach for predicting economic events: theoretical background. International Journal of Engineering Research and Applications: 3(5).605-610.
[52] Seman, P., Shoop, S., McGrath and Rollings, R. (2006). Soil strength prediction with K-nearest neighbour method. Proceedings of the 59th Canadian Geotechnical Conference. Canadian Geotechnical Society.
[53] Singh, D., Zaman, M. and Commuri, S. 2011. Evaluation of predictive models for estimating dynamic modulus of Hot‐Mix Asphalt in Oklahoma. Transportation Research Record: 2210, 57-72. https://doi.org/10.3141/2210-07
[54] Tran, N. and Hall, K. (2005). Evaluating the Predictive Equation in Determining Dynamic Moduli of Typical Asphalt Mixtures Used in Arkansas. Journal of the Association of Asphalt Paving Technologists, 74, 1–17.
[55] Abdo, A., Bayomy, F., Nielsen, R., Weaver, T. and Jung, S. (2009). Prediction of the Dynamic Modulus of Superpave Mixes. Proc., 8th International Conference on the Bearing Capacity of Roads, Railways and Airfields. 305–314.