Effectiveness of artificial neural network for forecasting of fracture toughness of concrete specimens
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42107-024-01074-0.pdf
Reference51 articles.
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2. Akid, A. S. M., Hossain, S., Munshi, M. I. U., Elahi, M. M. A., Sobuz, M. H. R., Tam, V. W. Y., & Islam, M. S. (2021). Assessing the influence of fly ash and polypropylene fiber on fresh, mechanical and durability properties of concrete. Journal of King Saud University—Engineering Sciences. https://doi.org/10.1016/j.jksues.2021.06.005
3. Amoosoltani, E., Ameli, A., Jabari, F., & Asadi, S. (2021). Employing a hybrid GA-ANN method for simulating fracture toughness of RCC mixture containing waste materials. Construction and Building Materials, 272, 121928. https://doi.org/10.1016/j.conbuildmat.2020.121928
4. Bazant, Z. P., & Kazemi, M. T. (1990). Determination of fracture energy, process zone longth and brittleness number from size effect, with application to rock and conerete. International Journal of Fracture, 44(2), 111–131. https://doi.org/10.1007/BF00047063
5. Bencardino, F., Rizzuti, L., Spadea, G., & Swamy, R. N. (2010). Composites: Part B Experimental evaluation of fiber reinforced concrete fracture properties. Composites, Part B: Engineering, 41(1), 17–24. https://doi.org/10.1016/j.compositesb.2009.09.002
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