Innovative machine learning approaches to predict the compressive strength of recycled plastic aggregate self-compacting concrete incorporating different waste ashes
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Mechanics of Materials,General Materials Science
Link
https://link.springer.com/content/pdf/10.1007/s41939-023-00356-5.pdf
Reference67 articles.
1. Ahmad A, Ostrowski KA et al (2021a) Comparative study of supervised machine learning algorithms for predicting the compressive strength of concrete at high temperature. Materials. https://doi.org/10.3390/ma14154222
2. Ahmad A, Farooq F et al (2021b) Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials 14(4):1–21. https://doi.org/10.3390/ma14040794
3. Ahmad A et al (2021c) Prediction of compressive strength of fly ash based concrete using individual and ensemble algorithm. Materials 14(4):1–21. https://doi.org/10.3390/ma14040794
4. Ahmed HU, Faraj RH, Hilal N, Mohammed AA, Sherwani AFH (2021) Use of recycled fibers in concrete composites: a systematic comprehensive review. Compos Part B: Eng. https://doi.org/10.1016/j.compositesb.2021.108769
5. Ahmed HU, Mohammed AS, Mohammed AA (2023) Fresh and mechanical performances of recycled plastic aggregate geopolymer concrete modified with nano-silica: experimental and computational investigation. Constr Build Mater 394:132266. https://doi.org/10.1016/j.conbuildmat.2023.132266
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