Integrating Extreme Vertices Design and Genetic Algorithm to Enhance the Moisture Resistance of Bituminous Concrete Modified with Recycled High-Density Polyethylene
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This study combined Extreme Vertices Design (EVD) and Genetic Algorithm (GA) to improve the moisture resistance of HDPE-modified bituminous concrete by enhancing its tensile strength ratio (TSR). EVD was employed for mixture design, sensitivity analysis, and model development using regression techniques, which were validated through F-statistics, R², and mean absolute percentage deviation (MAP.D). GA was used to optimize the mixture components to further enhance the TSR. The TSR of HDPE-modified mixtures ranged from 82.73% to 95.82%, surpassing the ASTM and AASHTO thresholds, while the unmodified mixture had a TSR of 82.43%. HDPE-modified mixtures met or exceeded the 80% TSR requirement, with several exceeding 85%. The analysis identified key interactions, such as the positive effect of granite content on TSR and the negative impact of excess sand and bitumen. Optimal HDPE content was found to improve TSR up to a certain point, beyond which it reduced the mixture's cohesion. ANOVA indicated significant interactions between granite, bitumen, and HDPE in influencing TSR. The regression model demonstrated high predictive capability with an R² of 93.27% and a low MAP.D of 0.74%, confirming its adequacy. GA optimization yielded a TSR of 97.88%, significantly improving upon the unmodified mixture's performance. The optimized mixture, with specific proportions of granite, sand, bitumen, and HDPE, surpassed ASTM and AASHTO standards, showing the effectiveness of HDPE in enhancing both performance and sustainability in asphalt mixtures.
AASHTO T283. (2020). Standard Method of Test for Resistance of Compacted Asphalt Mixtures to Moisture-Induced Damage. American Association of State Highway and Transportation Officials.
Abdel-Raheem, A. M., Mohamed, M. F., & El-Badawy, S. M. (2023). Optimization of asphalt binder modification using crumb rubber and nano-silica based on mixture experimental design. Construction and Building Materials, 374, 130889. https://doi.org/10.1016/j.conbuildmat.2023.130889
Al-Busaltan, S., Thom, N. H., & Hainin, M. R. (2022). Optimisation of polymer-modified asphalt mixtures using statistical mixture design approaches. International Journal of Pavement Engineering, 23(2), 467–479.
https://doi.org/10.1080/10298436.2020.1748112
Al-Mansour, A., & Al-Hadidy, A. (2023). Developing sustainable asphalt mixtures using high-density polyethylene. Sustainability, 15(13), 9897. https://doi.org/10.3390/su15139897
Aly, A. A., Hassan, H. U., & Ismail, M. (2022). Application of evolutionary algorithms in optimizing modified asphalt mixtures: A comprehensive review. Construction and Building Materials, 328, 127078.
https://doi.org/10.1016/j.conbuildmat.2022.127078
Ameur, A. B., Valentin, J., & Baldo, N. (2025). A review on the use of plastic waste as a modifier of asphalt mixtures for road constructions. CivilEng, 6(2), 17. https://doi.org/10.3390/civileng6020017
Asif, M., Ahmad, M., Rafiq, W., & Qureshi, N. A. (2023). Performance assessment of recycled plastic-modified asphalt: A statistical approach using mixture design. Journal of Cleaner Production, 410, 137254. https://doi.org/10.1016/j.jclepro.2023.137254
ASTM D4867. (2021). Standard Test Method for Effect of Moisture on Asphalt Concrete Paving Mixtures. ASTM International. https://www.astm.org/d4867-21.html
Bakare, H., Ouma, Y. O., & Laryea, S. (2020). Application of extreme vertices mixture design for performance optimization of polymer-modified asphalt. Materials Today: Proceedings, 33, 785–790. https://doi.org/10.1016/j.matpr.2020.04.531
Ghafoori, N., & Kouchaki, S. (2019). Optimization of warm mix asphalt for balanced mechanical performance and environmental impact using genetic algorithms. Journal of Cleaner Production, 231, 1015–1026.
https://doi.org/10.1016/j.jclepro.2019.05.282
Hamedi, G. H. (2023). Utilizing waste polyethylene for improved properties of asphalt. Advances in Science and Technology Research Journal, 17(1), 110–121. https://www.astrj.com/pdf-195657-117142
Huang, T., Yin, H., & Huang, X. (2024). Improved genetic algorithm for multi-threshold optimization in digital pathology image segmentation. Scientific Reports, 14, Article 22454.
https://doi.org/10.1038/s41598-024-73335-6:contentReference[oaicite:17]{index=17}
Huang, Y., Yin, J., & Huang, J. (2024). Application of genetic algorithms in machine learning and engineering optimization. Journal of Computational Intelligence and Applications, 36(1), 15–28. https://doi.org/10.1016/j.jcia.2024.01.002
Khalid, H. A., Yusoff, N. I. M., & Khan, M. I. (2020). Hybrid artificial intelligence model for predicting the fatigue and rutting performance of polymer-modified asphalt mixtures. Materials, 13(15), 3422. https://doi.org/10.3390/ma13153422
Khan, M. A., Ali, M., Shah, A. A., & Sultan, M. T. (2024). Mechanical and economic feasibility of LDPE waste-modified asphalt mixtures. Scientific Reports, 14, 75196.
https://doi.org/10.1038/s41598-024-75196-5
Kristoffersen, A. B., & Smucker, B. J. (2020). Model-robust design of mixture experiments. Journal of Statistical Planning and Inference, 206, 1–15
Kumar, A., & Gupta, R. (2024). Recyclability potential of waste plastic-modified asphalt concrete. Construction and Building Materials, 289, 130245. https://doi.org/10.1016/j.conbuildmat.2024.130245
Lee, S., & Kim, J. (2024). Waste plastic in asphalt mixtures via the dry method: A bibliometric analysis. Journal of Cleaner Production, 312, 127654. https://doi.org/10.1016/j.jclepro.2024.127654
Ma, Y., Zhang, F., Zhang, X., & Zhao, H. (2021). Potential applications for composite utilization of rubber and plastic waste in asphalt mixtures. Construction and Building Materials, 270, 121451. https://doi.org/10.1016/j.conbuildmat.2020.121451
Mirjalili, S., & Lewis, A. (2019). Genetic algorithms: Concepts, implementations, and applications. In S. Mirjalili (Ed.), Evolutionary algorithms and neural networks (pp. 43–55). Springer. https://doi.org/10.1007/978-3-030-15495-0_3
Patel, D., & Modarres, A. (2024). Evaluation of asphalt mixtures modified with polyethylene terephthalate (PET) waste. Innovative Infrastructure Solutions, 9(2), 17. https://doi.org/10.1007/s41062-024-01734-9
Rawat, B., Duwal, D., Phuyal, S., & Pant, A. (2022). A comparative review between various selection techniques in genetic algorithm for finding optimal solutions. International Journal of Computer Science and Engineering, 10(5), 1–8.
https://www.ijcseonline.org/pub_paper/3-IJCSE-08999.pdf
Sarang, M., & Dalhat, M. (2024). Incorporating waste plastic bottles as an additive in asphalt mixtures. Slovak Journal of Civil Engineering, 32(2), 24–35. https://doi.org/10.2478/sjce-2024-0024
Sivanandam, S. N., & Deepa, S. N. (2023). Introduction to genetic algorithms (2nd ed.). Springer. https://doi.org/10.1007/978-981-99-2080-5
Transportation Research Board. (2023). Annual meeting compendium of papers. National Academies of Sciences, Engineering, and Medicine.
Zhang, H., Wang, H., & Chen, J. S. (2021). Multi-objective optimization of fiber-modified asphalt mixtures using hybrid genetic algorithm and response surface methodology. Transportation Research Record, 2675(6), 376–389. https://doi.org/10.1177/0361198121998697
Zhang, Y., Ma, T., Yang, H., & Li, X. (2021). Waste plastics in asphalt concrete: A review on the performance and mechanism. Construction and Building Materials, 278, 122293. https://doi.org/10.1016/j.conbuildmat.2021.122293
Zhao, S., Guo, R., & Xiao, Y. (2021). An extreme vertices design approach for optimizing geopolymer-based composite materials. Materials, 14(2), 287. https://doi.org/10.3390/ma14020287
