Open Access
| Issue |
BIO Web Conf.
Volume 199, 2025
2nd International Graduate Conference on Smart Agriculture and Green Renewable Energy (SAGE-Grace 2025)
|
|
|---|---|---|
| Article Number | 05003 | |
| Number of page(s) | 9 | |
| Section | Sustainable Land Planning and Construction | |
| DOI | https://doi.org/10.1051/bioconf/202519905003 | |
| Published online | 05 December 2025 | |
- IPCC, Sixth Assessment Report – Summary for Policymakers, Intergovernmental Panel on Climate Change, 2021. [Online]. Available: https://www.ipcc.ch/report/ar6“ \t [Google Scholar]
- IEA, Global Status Report for Buildings and Construction 2022, International Energy Agency, 2022. [Online]. Available: https://www.iea.org/reports/global-status-report- for-buildings-and-construction-2022 [Google Scholar]
- Chen, Z., Wang, H., & Li, Q., “Carbon emissions and life cycle assessment of asphalt pavement: A review,” J. Cleaner Prod., vol. 346, p. 131157, 2022. [Google Scholar]
- Zaumanis, M., Mallick, R. B., & Frank, R., “Evaluation of different warm mix asphalt technologies: A comprehensive review,” Constr. Build. Mater., vol. 261, p. 119984, 2020. [Google Scholar]
- Raji, A., Ahmad, M., & Basit, A., “Machine learning applications in environmental assessment of construction materials,” Environ. Model. Softw., vol. 157, p. 105553, 2023. [Google Scholar]
- Yousefi, A. A., & Moghadas Nejad, F., “Sustainable asphalt pavement using warm mix technology: A review,” J. Cleaner Prod., vol. 142, pp. 1725–1736, 2017. [Google Scholar]
- Wang, H., Li, X., & Zhao, Y., “Predictive modeling of asphalt emissions using machine learning techniques,” Sustain. Mater. Technol., vol. 36, p. e00645, 2023. [Google Scholar]
- Giani, M. I., Dotelli, G., Brandini, N., & Zampori, L., “Comparative life cycle assessment of warm mix asphalt and hot mix asphalt,” Resour. Conserv. Recycl., vol. 54, no. 6, pp. 337–345, 2010. [Google Scholar]
- Tariq, S., Khan, M. J., & Khan, A., “Predicting environmental impacts of asphalt pavements using machine learning,” Constr. Build. Mater., vol. 343, p. 128061, 2022. [Google Scholar]
- Zortea, R. B., Teixeira, S. R., & Silvestre, J. D., “Life cycle assessment in the construction industry: Opportunities for machine learning,” Renew. Sustain. Energy Rev., vol. 160, p. 112273, 2022. [Google Scholar]
- Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T., “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002. [CrossRef] [Google Scholar]
- Ghosh, S., Ghosh, S., & Dey, A., “Artificial intelligence in civil engineering: A review,” Innov. Infrastruct. Solut., vol. 5, no. 4, pp. 1–11, 2020. [Google Scholar]
- Huang, B., Shu, X., & Chen, X., “Lifecycle assessment of hot mix asphalt and reclaimed asphalt pavement: A review,” Constr. Build. Mater., vol. 67, pp. 385–396, 2014. [Google Scholar]
- Zaumanis, M., & Mallick, R. B., “Review of very high RAP content mix design processes and performance,” Road Mater. Pavement Des., vol. 16, sup1, pp. 207–236, 2015. [Google Scholar]
- Xie, H., Wang, S., & Chen, Q., “Carbon emissions quantification and prediction model of asphalt pavement construction based on machine learning,” J. Cleaner Prod., vol. 286, p. 125476, 2021. [Google Scholar]
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