Metaheuristic optimization of machine learning models for strength prediction of high-performance self-compacting alkali-activated slag concrete
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41939-023-00349-4.pdf
Reference89 articles.
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2. Ahmed HU, Abdalla AA, Mohammed AS, Mohammed AA, Mosavi A (2022a) Statistical methods for modeling the compressive strength of geopolymer mortar. Materials 15(5):5. https://doi.org/10.3390/ma15051868
3. Ahmed HU, Mohammed AA, Mohammed A (2022b) Soft computing models to predict the compressive strength of GGBS/FA- geopolymer concrete. PLoS One 17(5):e0265846. https://doi.org/10.1371/journal.pone.0265846
4. Ahmed HU, Mohammed AA, Mohammed AS (2022c) The role of nanomaterials in geopolymer concrete composites: a state-of-the-art review. J Build Eng 49:104062. https://doi.org/10.1016/j.jobe.2022.104062
5. Ahmed HU, Mohammed AS, Faraj RH, Qaidi SMA, Mohammed AA (2022d) Compressive strength of geopolymer concrete modified with nano-silica: experimental and modeling investigations. Case Stud Constr Mater 16:e01036. https://doi.org/10.1016/j.cscm.2022.e01036
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