Open Access
| Issue |
E3S Web Conf.
Volume 596, 2024
International Conference on Civil, Materials, and Environment for Sustainability (ICCMES 2024)
|
|
|---|---|---|
| Article Number | 01021 | |
| Number of page(s) | 7 | |
| Section | Civil, Materials and Environment for Sustainability ICCMES 2024 | |
| DOI | https://doi.org/10.1051/e3sconf/202459601021 | |
| Published online | 22 November 2024 | |
- Mansouri, Ehsan, Maeve Manfredi, and Jong- Wan Hu. “Environmentally friendly concrete compressive strength prediction using hybrid machine learning.” Sustainability 14.20 (2022): 12990. https://www.mdpi.com/2071-1050/14/20/12990 [CrossRef] [Google Scholar]
- Feng, De-Cheng, et al. “Machine learning- based compressive strength prediction for concrete: An adaptive boosting approach.” Construction and Building Materials 230 (2020): 117000. https://www.sciencedirect.com/science/article/pii/S0950061819324420 [CrossRef] [Google Scholar]
- Alhakeem, Zaineb M., et al. “Prediction of ecofriendly concrete compressive strength using gradient boosting regression tree combined with GridSearchCV hyperparameter-optimization techniques.” Materials 15.21 (2022): 7432. https://www.mdpi.com/19961944/15/21/7432 [CrossRef] [PubMed] [Google Scholar]
- Dat, L. T. M., et al. “An Artificial intelligence approach for predicting compressive strength of eco-friendly concrete containing waste tire rubber.” IOP Conference Series: Earth and Environmental Science. Vol. 612. No. 1. IOP Publishing, 2020. https://iopscience.iop.org/article/10.1088/1755-1315/612/1/012029/meta [CrossRef] [Google Scholar]
- Yang, Yanhua, et al. “Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms.” Buildings 14.1 (2024): 190. https://www.mdpi.com/2075-5309/14/1/190 [CrossRef] [Google Scholar]
- Shahmansouri, Amir Ali, et al. “Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite.” Journal of Cleaner Production 279 (2021): 123697. https://www.sciencedirect.com/science/article/pii/S0959652620337422 [CrossRef] [Google Scholar]
- Rehman, Fazal, Sikandar Ali Khokhar, and Rao Arsalan Khushnood. “ANN based predictive mimicker for mechanical and rheological properties of eco-friendly geopolymer concrete.” Case Studies in Construction Materials 17 (2022): e01536. https://www.sciencedirect.com/science/article/pii/S2214509522006684 [CrossRef] [Google Scholar]
- Omran, Behzad Abounia, Qian Chen, and Ruoyu Jin. “Prediction of Compressive Strength of ‘Green’Concrete Using Artificial Neural Networks.” Sulbaran, T (2014). [Google Scholar]
- Ranasinghe, R. S. S., et al. “Eco-friendly mix design of slag-ash-based geopolymer concrete using explainable deep learning.” Results in Engineering 23 (2024): 102503. https://www.sciencedirect.com/science/article/pii/S2590123024007588 [CrossRef] [Google Scholar]
- Yang, Yanhua, et al. “Predicting the Compressive Strength of Environmentally Friendly Concrete Using Multiple Machine Learning Algorithms.” Buildings 14.1 (2024): 190. https://www.mdpi.com/2075-5309/14/1/190 [CrossRef] [Google Scholar]
- Sobuz, Md Habibur Rahman, et al. “Assessing the influence of sugarcane bagasse ash for the production of eco-friendly concrete: experimental and machine learning approaches.” Case Studies in Construction Materials 20 (2024): e02839. https://www.sciencedirect.com/science/article/pii/S2214509523010203 [CrossRef] [Google Scholar]
- Evangelista, Ana Catarina Jorge, and Vivian WY Tam. “Machine learning decision-making tools-eco friendly materials.” Materials Selection for Sustainability in the Built Environment. Woodhead Publishing, 2024. 319- 337.https://www.sciencedirect.com/science/article/pii/B9780323951227000150 [Google Scholar]
- Prabhakar, Prajjwal, and Rohit Kumar. “Compatibility of sustainable geopolymer based on artificial neural network.” Innovative Infrastructure Solutions 9.8 (2024): 323. https://link.springer.com/article/10.1007/s41062-024-01632-0 [CrossRef] [Google Scholar]
- Ali, Tariq, et al. “A systematic literature review of AI-based prediction methods for self- compacting, geopolymer, and other eco-friendly concrete types: Advancing sustainable concrete.” Construction and Building Materials 440 (2024): 137370. https://www.sciencedirect.com/science/article/pii/S0950061824025121 [CrossRef] [Google Scholar]
- Singh, Priyanka, et al. “Development of performance-based models for green concrete using multiple linear regression and artificial neural network.” International Journal on Interactive Design and Manufacturing (IJIDeM) (2023):1–12. https://link.springer.com/article/10.1007/s12008-023-01386-6 [Google Scholar]
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