Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-022-07724-1.pdf
Reference42 articles.
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2. Mahasenan N, Smith S, Humphreys K (2003) The cement industry and global climate change: current and potential future cement industry CO2 emissions. In: Greenhouse Gas Control Technologies-6th International Conference (pp. 995–1000). Pergamon, UK. https://doi.org/10.1016/B978-008044276-1/50157-4.
3. Guo X, Shi H, Dick WA (2010) Compressive strength and microstructural characteristics of class C fly ash geopolymer. Cement Concr Compos 32(2):142–147. https://doi.org/10.1016/j.cemconcomp.2009.11.003
4. Li M, Wang GG (2022) A review of green shop scheduling problem. Inf Sci 589:478-496
5. Mejeoumov GG (2007) Improved cement quality and grinding efficiency by means of closed mill circuit modeling. Texas A&M University.
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