Benchmarking Classical and Deep Machine Learning Models for Predicting Hot Mix Asphalt Dynamic Modulus
Abstract
Doi: 10.28991/CEJ-2025-011-01-06
Full Text: PDF
Keywords
References
Bi, Y., Guo, F., Zhang, J., Pei, J., & Li, R. (2021). Correlation analysis between asphalt binder/asphalt mastic properties and dynamic modulus of asphalt mixture. Construction and Building Materials, 276, 122256. doi:10.1016/j.conbuildmat.2021.122256.
Bhattacharjee, S., & Mallick, R. (2012). Determining damage development in hot-mix asphalt with use of continuum damage mechanics and small-scale accelerated pavement test. Transportation Research Record, 2296, 125–134. doi:10.3141/2296-13.
Jamshidi, A., White, G., & Hosseinpour, M. (2021). Revisiting the correlation between the dynamic modulus and the flexural modulus of hot mixture asphalt. Construction and Building Materials, 296, 123697. doi:10.1016/j.conbuildmat.2021.123697.
Rodezno, M. C., & Kaloush, K. E. (2009). Comparison of asphalt rubber and conventional mixture properties: Considerations for mechanistic-empirical pavement design guide implementation. Transportation Research Record, 2126, 132–141. doi:10.3141/2126-16.
El-Hakim, R. A., El-Badawy, S. M., Gabr, A. R., & Azam, A. M. (2016). Influence of Unbound Material Type and Input Level on Pavement Performance Using Mechanistic–Empirical Pavement Design Guide. Transportation Research Record, 2578(1), 21–28. doi:10.3141/2578-03.
ARMAĞAN, K., SALTAN, M., TERZİ, S., & KIRAÇ, N. (2021). Comparison of dynamic elastisty modulus with different prediction approaches for Karaman – Konya highway pavement. Journal of Innovative Transportation, 2(1), 2102. doi:10.53635/jit.849544.
Singh, D., Zaman, M., & Commuri, S. (2013). Artificial Neural Network Modeling for Dynamic Modulus of Hot Mix Asphalt Using Aggregate Shape Properties. Journal of Materials in Civil Engineering, 25(1), 54–62. doi:10.1061/(asce)mt.1943-5533.0000548.
Rahman, A. S. M. A., & Tarefder, R. A. (2016). Dynamic modulus and phase angle of warm-mix versus hot-mix asphalt concrete. Construction and Building Materials, 126, 434–441. doi:10.1016/j.conbuildmat.2016.09.068.
Zhang, M., Zhao, H., Fan, L., & Yi, J. (2022). Dynamic modulus prediction model and analysis of factors influencing asphalt mixtures using gray relational analysis methods. Journal of Materials Research and Technology, 19, 1312–1321. doi:10.1016/j.jmrt.2022.05.120.
Barugahare, J., Amirkhanian, A. N., Xiao, F., & Amirkhanian, S. N. (2020). Predicting the dynamic modulus of hot mix asphalt mixtures using bagged trees ensemble. Construction and Building Materials, 260, 120468. doi:10.1016/j.conbuildmat.2020.120468.
Khattab, A. M., El-Badawy, S. M., Al Hazmi, A. A., & Elmwafi, M. (2014). Evaluation of Witczak E* predictive models for the implementation of AASHTOWare-Pavement ME Design in the Kingdom of Saudi Arabia. Construction and Building Materials, 64, 360–369. doi:10.1016/j.conbuildmat.2014.04.066.
Yu, H., & Shen, S. (2012). An investigation of dynamic modulus and flow number properties of asphalt mixtures in Washington State. Report No. TNW, 709867.
Khattab, A. M., El-Badawy, S. M., Al Hazmi, A. A., & Elmwafi, M. (2015, April). Comparing Witczak NCHRP 1-40D with Hirsh E* predictive models for Kingdom of Saudi Arabia asphalt mixtures. The 3rd Middle East Society of Asphalt Technologists (MESAT) Conference, 6-8 April, 2015, Dubai, United Arab Emirates.
El-Badawy, S., Abd El-Hakim, R., & Awed, A. (2018). Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction. Journal of Materials in Civil Engineering, 30(7), 1–11. doi:10.1061/(asce)mt.1943-5533.0002282.
Al-Tawalbeh, A., Sirin, O., Sadeq, M., Sebaaly, H., & Masad, E. (2022). Evaluation and calibration of dynamic modulus prediction models of asphalt mixtures for hot climates: Qatar as a case study. Case Studies in Construction Materials, 17, 1580. doi:10.1016/j.cscm.2022.e01580.
Uwanuakwa, I. D., Amir, I. Y., & Umba, L. N. (2024). Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees. Journal of Road Engineering, 4(2), 224–233. doi:10.1016/j.jreng.2024.05.001.
Acharjee, P. K., Souliman, M. I., Freyle, F., & Fuentes, L. (2024). Development of Dynamic Modulus Prediction Model Using Artificial Neural Networks for Colombian Mixtures. Journal of Transportation Engineering, Part B: Pavements, 150(1), 1402. doi:10.1061/jpeodx.pveng-1402.
Owais, M. (2024). Preprocessing and postprocessing analysis for hot-mix asphalt dynamic modulus experimental data. Construction and Building Materials, 450, 138693. doi:10.1016/j.conbuildmat.2024.138693.
Sakhaeifar, M. S., Richard Kim, Y., & Kabir, P. (2015). New predictive models for the dynamic modulus of hot mix asphalt. Construction and Building Materials, 76, 221–231. doi:10.1016/j.conbuildmat.2014.11.011.
Singh, D., Zaman, M., & Commuri, S. (2011). Evaluation of predictive models for estimating dynamic modulus of hot-mix asphalt in Oklahoma. Transportation Research Record, 2210(2210), 57–72. doi:10.3141/2210-07.
Chen, H., Saba, R. G., Liu, G., Barbieri, D. M., Zhang, X., & Hoff, I. (2023). Influence of material factors on the determination of dynamic moduli and associated prediction models for different types of asphalt mixtures. Construction and Building Materials, 365, 130134. doi:10.1016/j.conbuildmat.2022.130134.
Behnood, A., & Daneshvar, D. (2020). A machine learning study of the dynamic modulus of asphalt concretes: An application of M5P model tree algorithm. Construction and Building Materials, 262, 120544. doi:10.1016/j.conbuildmat.2020.120544.
Daneshvar, D., & Behnood, A. (2022). Estimation of the dynamic modulus of asphalt concretes using random forests algorithm. International Journal of Pavement Engineering, 23(2), 250–260. doi:10.1080/10298436.2020.1741587.
Awed, A. M., Awaad, A. N., Kaloop, M. R., Hu, J. W., El-Badawy, S. M., & Abd El-Hakim, R. T. (2023). Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques. Sustainability (Switzerland), 15(19). doi:10.3390/su151914464.
Liu, J., Liu, F., Zheng, C., Zhou, D., & Wang, L. (2022). Optimizing asphalt mix design through predicting effective asphalt content and absorbed asphalt content using machine learning. Construction and Building Materials, 325(December), 126607. doi:10.1016/j.conbuildmat.2022.126607.
Hu, X., & Solanki, P. (2021). Predicting Resilient Modulus of Cementitiously Stabilized Subgrade Soils Using Neural Network, Support Vector Machine, and Gaussian Process Regression. International Journal of Geomechanics, 21(6), 04021073. doi:10.1061/(asce)gm.1943-5622.0002029.
Uwanuakwa, I. D., Busari, A., Ali, S. I. A., Mohd Hasan, M. R., Sani, A., & Abba, S. I. (2022). Comparing Machine Learning Models with Witczak NCHRP 1-40D Model for Hot-Mix Asphalt Dynamic Modulus Prediction. Arabian Journal for Science and Engineering, 47(10), 13579–13591. doi:10.1007/s13369-022-06935-x.
Ceylan, H., Gopalakrishnan, K., & Kim, S. (2008). Advanced approaches to hot-mix asphalt dynamic modulus prediction. Canadian Journal of Civil Engineering, 35(7), 699–707. doi:10.1139/L08-016.
Ceylan, H., Gopalakrishnan, K., & Kim, S. (2009). Looking to the future: The next-generation hot mix asphalt dynamic modulus prediction models. International Journal of Pavement Engineering, 10(5), 341–352. doi:10.1080/10298430802342690.
Ceylan, H., Schwartz, C. W., Kim, S., & Gopalakrishnan, K. (2009). Accuracy of Predictive Models for Dynamic Modulus of Hot-Mix Asphalt. Journal of Materials in Civil Engineering, 21(6), 286–293. doi:10.1061/(asce)0899-1561(2009)21:6(286).
Gong, H., Sun, Y., Dong, Y., Han, B., Polaczyk, P., Hu, W., & Huang, B. (2020). Improved estimation of dynamic modulus for hot mix asphalt using deep learning. Construction and Building Materials, 263, 119912. doi:10.1016/j.conbuildmat.2020.119912.
Ghasemi, P., Aslani, M., Rollins, D. K., & Williams, R. C. (2019). Principal component neural networks for modeling, prediction, and optimization of hot mix asphalt dynamics modulus. Infrastructures, 4(3), 2019. doi:10.3390/infrastructures4030053.
Rezazadeh Eidgahee, D., Jahangir, H., Solatifar, N., Fakharian, P., & Rezaeemanesh, M. (2022). Data-driven estimation models of asphalt mixtures dynamic modulus using ANN, GP and combinatorial GMDH approaches. Neural Computing and Applications, 34(20), 17289–17314. doi:10.1007/s00521-022-07382-3.
Zhang, C., Ildefonzo, D. G., Shen, S., Wang, L., & Huang, H. (2023). Implementation of ensemble Artificial Neural Network and MEMS wireless sensors for In-Situ asphalt mixture dynamic modulus prediction. Construction and Building Materials, 377, 131118. doi:10.1016/j.conbuildmat.2023.131118.
Barugahare, J., Amirkhanian, A. N., Xiao, F., & Amirkhanian, S. N. (2022). ANN-based dynamic modulus models of asphalt mixtures with similar input variables as Hirsch and Witczak models. International Journal of Pavement Engineering, 23(5), 1328–1338. doi:10.1080/10298436.2020.1799209.
Mohammadi Golafshani, E., Behnood, A., & Karimi, M. M. (2021). Predicting the dynamic modulus of asphalt mixture using hybridized artificial neural network and grey wolf optimizer. International Journal of Pavement Engineering, 1–11. doi:10.1080/10298436.2021.2005056.
Moussa, G. S., & Owais, M. (2020). Pre-trained deep learning for hot-mix asphalt dynamic modulus prediction with laboratory effort reduction. Construction and Building Materials, 265, 120239. doi:10.1016/j.conbuildmat.2020.120239.
Moussa, G. S., & Owais, M. (2021). Modeling Hot-Mix asphalt dynamic modulus using deep residual neural Networks: Parametric and sensitivity analysis study. Construction and Building Materials, 294, 123589. doi:10.1016/j.conbuildmat.2021.123589.
Liu, J., Liu, F., Wang, Z., Fanijo, E. O., & Wang, L. (2023). Involving prediction of dynamic modulus in asphalt mix design with machine learning and mechanical-empirical analysis. Construction and Building Materials, 407, 133610. doi:10.1016/j.conbuildmat.2023.133610.
Broothaerts, W., Cordeiro, F., Corbisier, P., Robouch, P., & Emons, H. (2020). Log transformation of proficiency testing data on the content of genetically modified organisms in food and feed samples: is it justified? Analytical and Bioanalytical Chemistry, 412(5), 1129–1136. doi:10.1007/s00216-019-02338-4.
Bridges, W. C., Calkin, N. J., Kenyon, C. M., & Saltzman, M. J. (2022). Log transformations: What not to expect when you’re expecting. Communications in Statistics - Theory and Methods, 51(5), 1514–1521. doi:10.1080/03610926.2020.1771368.
Manandhar, B., & Nandram, B. (2021). Hierarchical Bayesian models for continuous and positively skewed data from small areas. Communications in Statistics - Theory and Methods, 50(4), 944–962. doi:10.1080/03610926.2019.1645853.
Jayalakshmi, T., & Santhakumaran, A. (2011). Statistical Normalization and Back Propagationfor Classification. International Journal of Computer Theory and Engineering, 3(1), 89–93. doi:10.7763/ijcte.2011.v3.288.
Shalabi, L. Al, Shaaban, Z., & Kasasbeh, B. (2006). Data Mining: A Preprocessing Engine. Journal of Computer Science, 2(9), 735–739. doi:10.3844/jcssp.2006.735.739.
Yi, B. J., Lee, D. G., & Rim, H. C. (2015). The Effects of Feature Optimization on High-Dimensional Essay Data. Mathematical Problems in Engineering, 421642. doi:10.1155/2015/421642.
Alnaqbi, A. J., Zeiada, W., Al-Khateeb, G. G., Hamad, K., & Barakat, S. (2023). Creating Rutting Prediction Models through Machine Learning Techniques Utilizing the Long-Term Pavement Performance Database. Sustainability (Switzerland), 15(18), 13653. doi:10.3390/su151813653.
Zeiada, W., Hamad, K., Omar, M., Underwood, B. S., Khalil, M. A., & Karzad, A. S. (2019). Investigation and modelling of asphalt pavement performance in cold regions. International Journal of Pavement Engineering, 20(8), 986–997. doi:10.1080/10298436.2017.1373391.
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. doi:10.1007/s13369-020-04398-6.
Mirou, S. M., Elawady, A. T., Ashour, A. G., Zeiada, W., & Abuzwidah, M. (2023). Visibility Prediction through Machine Learning: Exploring the Role of Meteorological Factors. 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, 1–6. doi:10.1109/ASET56582.2023.10180539.
Dabous, S. A., Hamad, K., Al-Ruzouq, R., Zeiada, W., Omar, M., & Obaid, L. (2022). a Case-Based Reasoning and Random Forest Framework for Selecting Preventive Maintenance of Flexible Pavement Sections. Baltic Journal of Road and Bridge Engineering, 17(2), 107–134. doi:10.7250/bjrbe.2022-17.562.
Hamad, K., Obaid, L., Haridy, S., Zeiada, W., & Al-Khateeb, G. (2023). Factorial design–machine learning approach for predicting incident durations. Computer-Aided Civil and Infrastructure Engineering, 38(5), 660–680. doi:10.1111/mice.12883.
Navid, M. (2018). Multiple Linear Regressions for Predicting Rainfall for Bangladesh. Communications, 6(1), 11. doi:10.11648/j.com.20180601.11.
Alsheyab, M. A., & Khasawneh, M. A. (2024). Statistical Modeling of Asphalt Pavement Surface Friction Based on Aggregate Fineness Modulus and Asphalt Mix Volumetrics. International Journal of Pavement Research and Technology, 17(5), 1093–1111. doi:10.1007/s42947-023-00289-9.
Kang, M., Kim, M., & Lee, J. H. (2010). Analysis of rigid pavement distresses on interstate highway using decision tree algorithms. KSCE Journal of Civil Engineering, 14(2), 123–130. doi:10.1007/s12205-010-0123-7.
Madeh Piryonesi, S., & El-Diraby, T. E. (2021). Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling. Journal of Infrastructure Systems, 27(2), 62. doi:10.1061/(asce)is.1943-555x.0000602.
Hamad, K., Obaid, L., Nassif, A. B., Abu Dabous, S., Al-Ruzouq, R., & Zeiada, W. (2023). Comprehensive evaluation of multiple machine learning classifiers for predicting freeway incident duration. Innovative Infrastructure Solutions, 8(6), 177. doi:10.1007/s41062-023-01138-1.
Babagoli, R., & Rezaei, M. (2022). Development of prediction models for moisture susceptibility of asphalt mixture containing combined SBR, waste CR and ASA using support vector regression and artificial neural network methods. Construction and Building Materials, 322, 126430. doi:10.1016/j.conbuildmat.2022.126430.
Obaid, L., Hamad, K., Khalil, M. A., & Nassif, A. B. (2024). Effect of feature optimization on performance of machine learning models for predicting traffic incident duration. Engineering Applications of Artificial Intelligence, 131, 107845. doi:10.1016/j.engappai.2024.107845.
Molavi Nojumi, M., Huang, Y., Hashemian, L., & Bayat, A. (2022). Application of Machine Learning for Temperature Prediction in a Test Road in Alberta. International Journal of Pavement Research and Technology, 15(2), 303–319. doi:10.1007/s42947-021-00023-3.
Justo-Silva, R., Ferreira, A., & Flintsch, G. (2021). Review on machine learning techniques for developing pavement performance prediction models. Sustainability (Switzerland), 13(9), 5248. doi:10.3390/su13095248.
Sadat Hosseini, A., Hajikarimi, P., Gandomi, M., Moghadas Nejad, F., & Gandomi, A. H. (2021). Optimized machine learning approaches for the prediction of viscoelastic behavior of modified asphalt binders. Construction and Building Materials, 299(January), 124264. doi:10.1016/j.conbuildmat.2021.124264.
Luo, Z., & Li, S. (2023). An interpretable prediction model for pavement performance prediction based on XGBoost and SHAP. Proc. SPIE, March 2023, 55. doi:10.1117/12.2671361.
Nhat-Duc, H., & Van-Duc, T. (2023). Computer Vision-Based Severity Classification of Asphalt Pavement Raveling Using Advanced Gradient Boosting Machines and Lightweight Texture Descriptors. Iranian Journal of Science and Technology - Transactions of Civil Engineering, 47(6), 4059–4073. doi:10.1007/s40996-023-01138-2.
Pei, L., Yu, T., Xu, L., Li, W., & Han, Y. (2022). Prediction of Decay of Pavement Quality or Performance Index Based on Light Gradient Boost Machine. Advances in Intelligent Automation and Soft Computing. IASC 2021, Lecture Notes on Data Engineering and Communications Technologies, 80, Springer, Cham, Switzerland. doi:10.1007/978-3-030-81007-8_135.
Heidarabadizadeh, N., Ghanizadeh, A. R., & Behnood, A. (2021). Prediction of the resilient modulus of non-cohesive subgrade soils and unbound subbase materials using a hybrid support vector machine method and colliding bodies optimization algorithm. Construction and Building Materials, 275, 122140. doi:10.1016/j.conbuildmat.2020.122140.
Huang, Y., Molavi Nojumi, M., Hashemian, L., & Bayat, A. (2023). Evaluation of a Machine Learning Approach for Temperature Prediction in Pavement Base and Subgrade Layers in Alberta, Canada. Journal of Transportation Engineering, Part B: Pavements, 149(1), 1–12. doi:10.1061/jpeodx.pveng-1010.
Deng, Y., & Shi, X. (2022). An Accurate, Reproducible and Robust Model to Predict the Rutting of Asphalt Pavement: Neural Networks Coupled with Particle Swarm Optimization. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22063–22072. doi:10.1109/TITS.2022.3149268.
Nassif, A. B., Elnagar, A., Shahin, I., & Henno, S. (2021). Deep learning for Arabic subjective sentiment analysis: Challenges and research opportunities. Applied Soft Computing, 98, 106836. doi:10.1016/j.asoc.2020.106836.
Nassif, A. B., Shahin, I., Attili, I., Azzeh, M., & Shaalan, K. (2019). Speech Recognition Using Deep Neural Networks: A Systematic Review. IEEE Access, 7(February), 19143–19165. doi:10.1109/ACCESS.2019.2896880.
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2323. doi:10.1109/5.726791.
Li, H., Peng, W., Adumene, S., & Yazdi, M. (2023). An Improved LeNet-5 Convolutional Neural Network Supporting Condition-Based Maintenance and Fault Diagnosis of Bearings. Intelligent Reliability and Maintainability of Energy Infrastructure Assets. Studies in Systems, Decision and Control, 473, Springer, Cham, Switzerland. doi:10.1007/978-3-031-29962-9_4.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. doi:10.1145/3065386.
Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations (ICLR 2015), 7-9 May, 2015, San Diego, United States.
Szegedy, C., Wei Liu, Yangqing Jia, Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. doi:10.1109/cvpr.2015.7298594.
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 90. doi:10.1109/cvpr.2016.90.
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017). Aggregated Residual Transformations for Deep Neural Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 634. doi:10.1109/cvpr.2017.634.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 243. doi:10.1109/cvpr.2017.243.
Tan, M., & Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. International conference on machine learning, 9-15 June, 2019, California, United States.
Denny Prabowo, Y., Warnars, H. L. H. S., Budiharto, W., Kistijantoro, A. I., Heryadi, Y., & Lukas. (2018). LSTM and Simple Rnn Comparison In The Problem Of Sequence To Sequence On Conversation Data Using Bahasa Indonesia. 2018 Indonesian Association for Pattern Recognition International Conference (INAPR), 51–56. doi:10.1109/inapr.2018.8627029.
Werbos, P. J. (1988). Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4), 339–356. doi:10.1016/0893-6080(88)90007-X.
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8), 1735–1780. doi:10.1162/neco.1997.9.8.1735.
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014). On the properties of neural machine translation: Encoder–decoder approaches. Proceedings of SSST 2014 - 8th Workshop on Syntax, Semantics and Structure in Statistical Translation, 1409(1), 103–111. doi:10.3115/v1/w14-4012.
Ali, A., & Milad, A. (2023). Application of Machine Learning Techniques for Asphalt Pavement Performance Prediction. Journal of Pure & Applied Sciences, 22(3), 35–40. doi:10.51984/jopas.v22i3.2733.
DOI: 10.28991/CEJ-2025-011-01-06
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Waleed Zeiada, Lubna Obaid, Sherif El-Badawy, Ragaa Abd El-Hakim, Ahmed Awed

This work is licensed under a Creative Commons Attribution 4.0 International License.