Prediction of the compressive strength of concrete made with soap factory wastewater using machine learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computers in Earth Sciences,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,General Environmental Science
Link
https://link.springer.com/content/pdf/10.1007/s40808-022-01445-z.pdf
Reference40 articles.
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2. ACI Committee 318 (2014) Building Code Requirements for Structural Concrete and Commentary. Consulté 23 mars 2022 (https://www.concrete.org/store/productdetail.aspx?ItemID=318U14&Language=English&Units=US_Units)
3. AFNOR (2013) NF EN 1008: 2003 mixing water for concrete—specification for sampling, testing and assessing the suitability of water, including water recovered from processes in the concrete industry, as mixing water for concrete. Consulté 20 mars 2022 (https://infostore.saiglobal.com/en-us/Standards/NF-EN-1008-2003-76462_SAIG_AFNOR_AFNOR_161604/)
4. Akpinar P, Uwanuakwa ID (2020) Investigation of the parameters influencing progress of concrete carbonation depth by using artificial neural networks. Mater Constr 70(337):209. https://doi.org/10.3989/mc.2020.02019
5. Akyuncu V, Uysal M, Tanyildizi H, Sumer M (2019) Modeling the weight and length changes of the concrete exposed to sulfate using artificial neural network. Revista De La Construcción 17(3):337–353. https://doi.org/10.7764/RDLC.17.3.337
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