Multivariable models including artificial neural network and M5P-tree to forecast the stress at the failure of alkali-activated concrete at ambient curing condition and various mixture proportions-Reference-Cited by-同舟云学术

Multivariable models including artificial neural network and M5P-tree to forecast the stress at the failure of alkali-activated concrete at ambient curing condition and various mixture proportions

Published:2022-06-05 Issue:20 Volume:34 Page:17853-17876
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Ahmed Hemn Unis,Mohammed Ahmed S.,Mohammed Azad A.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-022-07427-7.pdf

Reference109 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. Elsevier, Pergamon, pp 995–1000. 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

4. Mejeoumov GG (2007) Improved cement quality and grinding efficiency by means of closed mill circuit modeling. Texas A&M University

5. Provis JL, Palomo A, Shi C (2015) Advances in understanding alkali-activated materials. Cem Concr Res 78:110–125

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