Prediction of fracture energy of concrete notched beam using artificial neural network
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00674-6.pdf
Reference54 articles.
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2. Alyhya, W. S., Abo Dhaheer, M. S., Al-Rubaye, M. M., & Karihaloo, B. L. (2016). Influence of mix composition and strength on the fracture properties of self-compacting concrete. Construction and Building Materials, 110, 312–322. https://doi.org/10.1016/j.conbuildmat.2016.02.037
3. 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
4. Bažant, Z. P., & Pfeiffer, P. A. (1987). Determination of fracture energy from size effect and brittleness number. Materials, 84, 463–480.
5. Beshr, A. A. A., & Zarzoura, F. H. (2021). Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge. Innovative Infrastructure Solutions, 6(2), 1–15. https://doi.org/10.1007/s41062-021-00458-4
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