Influence of machine learning approaches for partial replacement of cement content through waste in construction sector
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00972-z.pdf
Reference19 articles.
1. Arora, R., Kumar, K., & Dixit, S. (2023). Comparative analysis of the influence of partial replacement of cement with supplementing cementitious materials in sustainable concrete using machine learning approach. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00858-0
2. Arora, R., Kumar, K., Dixit, S., & Mishra, L. (2022a). Analyze the outcome of waste material as cement replacement agent in basic concrete. Material Today Proceedings, 56(4), 1877–1881. https://doi.org/10.1016/j.matpr.2021.11.148
3. Arora, R., Kumar, K., Saini, R., Sharma, K., Dixit, S., Dixit, A. K., & Taskaeva, N. (2022b). Utilization of waste materials for the production of green concrete: A review. Materials Today: Proceedings, 69(2), 317–322. https://doi.org/10.1016/j.matpr.2022.08.542
4. Dixit, S., Arora, R., Kumar, K., Bansal, S., Vatin, N., Araszkiewicz, K., & Epifantsev, K. (2022). Replacing e-waste with coarse aggregate in architectural engineering and construction industry. Material Today Proceedings, 56(1), 2353–2358. https://doi.org/10.1016/j.matpr.2021.12.154
5. Dong, W., Huang, Y., Lehane, B., & Ma, G. (2020). XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 103155. https://doi.org/10.1016/j.autcon.2020.103155
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