A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete


  • Ilham Yahya Amir Department of Civil Engineering, Near East University, Cyprus
  • Abdinasir Mohamed Yusuf Department of Civil Engineering, Near East University, Cyprus | Department of Civil Engineering, Aden Adde International University, Somalia
  • Ikenna D. Uwanuakwa Department of Civil Engineering, Near East University, Cyprus
Volume: 14 | Issue: 2 | Pages: 13106-13111 | April 2024 | https://doi.org/10.48084/etasr.6808


The prediction of the asphalt dynamic modulus (E*), which measures the material's ability to withstand changes in shape or structure, is important. Previous studies indicated that the well-known Witczak 1-40D model for E* is outperformed by machine learning models. Additionally, the application of machine learning algorithms requires manual fine-tuning of their hyperparameters. In this study, the artificial Hummingbird and Harris Hawks optimization algorithms were employed in the automatic calibration of the Random Forest and Gradient Boost algorithms' hyperparameters for modeling E* using the Witczak 1-40D model and additional parameters. In addition, the model was interpreted using the Shapley value and permutation feature importance. The results indicate that the optimized artificial hummingbird algorithm model performed better, with R² reaching 0.97. The interpretability of the model suggests that the binder parameters exhibited the highest effect on the variance of E*.


asphalt dynamic modulus, automatic calibration, modeling, machine learning


Download data is not yet available.


Y. R. Kim, Y. Seo, M. King, and M. Momen, "Dynamic Modulus Testing of Asphalt Concrete in Indirect Tension Mode," Transportation Research Record, vol. 1891, no. 1, pp. 163–173, Jan. 2004.

M. W. Witczak and O. A. Fonseca, "Revised Predictive Model for Dynamic (Complex) Modulus of Asphalt Mixtures," Transportation Research Record, vol. 1540, no. 1, pp. 15–23, Jan. 1996.

J. F. Shook et al., "Factors Influencing Dynamic Modulus of Asphalt Concrete," in Association of Asphalt Paving Technologists Proceedings, Feb. 1969, vol. 38, pp. 140–178.

I. D. Uwanuakwa, A. Busari, S. I. A. Ali, M. R. Mohd Hasan, A. Sani, and S. I. Abba, "Comparing Machine Learning Models with Witczak NCHRP 1-40D Model for Hot-Mix Asphalt Dynamic Modulus Prediction," Arabian Journal for Science and Engineering, vol. 47, no. 10, pp. 13579–13591, Oct. 2022.

A. K. Dubey, A. K. Sinhal, and R. Sharma, "An Improved Auto Categorical PSO with ML for Heart Disease Prediction," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8567–8573, Jun. 2022.

P. Dhaka, R. Sehrawat, and P. Bhutani, "An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12396–12403, Dec. 2023.

S. R. Gopi and M. Karthikeyan, "Effectiveness of Crop Recommendation and Yield Prediction using Hybrid Moth Flame Optimization with Machine Learning," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11360–11365, Aug. 2023.

T. H. Le, H. L. Nguyen, and C. T. Pham, "Artificial intelligence approach to predict the dynamic modulus of asphalt concrete mixtures," Journal of Science and Transport Technology, pp. 22–31, Jun. 2022.

A. Behnood and E. Mohammadi Golafshani, "Predicting the dynamic modulus of asphalt mixture using machine learning techniques: An application of multi biogeography-based programming," Construction and Building Materials, vol. 266, Jan. 2021, Art. no. 120983.

T. H. Le et al., "Artificial Intelligence-Based Model for the Prediction of Dynamic Modulus of Stone Mastic Asphalt," Applied Sciences, vol. 10, no. 15, Jan. 2020, Art. no. 5242.

J. Huang, G. Shiva Kumar, J. Ren, J. Zhang, and Y. Sun, "Accurately predicting dynamic modulus of asphalt mixtures in low-temperature regions using hybrid artificial intelligence model," Construction and Building Materials, vol. 297, Aug. 2021, Art. no. 123655.

W. Xu, X. Huang, Z. Yang, M. Zhou, and J. Huang, "Developing Hybrid Machine Learning Models to Determine the Dynamic Modulus (E*) of Asphalt Mixtures Using Parameters in Witczak 1-40D Model: A Comparative Study," Materials, vol. 15, no. 5, Jan. 2022, Art. no. 1791.

I. Lahmar, A. Zaier, M. Yahia, and R. Boaullegue, "A Novel Improved Binary Harris Hawks Optimization For High dimensionality Feature Selection," Pattern Recognition Letters, vol. 171, pp. 170–176, Jul. 2023.

W. Zhao, L. Wang, and S. Mirjalili, "Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications," Computer Methods in Applied Mechanics and Engineering, vol. 388, Jan. 2022, Art. no. 114194.

B. Rozemberczki et al., "The Shapley Value in Machine Learning." arXiv, May 26, 2022.

A. Altmann, L. Toloşi, O. Sander, and T. Lengauer, "Permutation importance: a corrected feature importance measure," Bioinformatics, vol. 26, no. 10, pp. 1340–1347, May 2010.

S. El-Badawy, R. Abd El-Hakim, and A. Awed, "Comparing Artificial Neural Networks with Regression Models for Hot-Mix Asphalt Dynamic Modulus Prediction," Journal of Materials in Civil Engineering, vol. 30, no. 7, Jul. 2018, Art. no. 04018128.


How to Cite

I. Y. Amir, A. M. Yusuf, and I. D. Uwanuakwa, “A Metaheuristic Approach of predicting the Dynamic Modulus in Asphalt Concrete”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 2, pp. 13106–13111, Apr. 2024.


Abstract Views: 283
PDF Downloads: 388

Metrics Information