Retraction Note to: Artificial neural networks application to predict the compressive damage of lightweight geopolymer

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Retraction Note: Neural Comput & Applic (2013) 23:507–518 https://doi.org/10.1007/s00521-012-0945-y

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a large number of articles that were under consideration at the same time, including [2, 3], and previously published articles, including [4, 5, 6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction.

[1] Nazari, A. Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput & Applic 23, 507–518 (2013). https://doi.org/10.1007/s00521-012-0945-y.

[2] Nazari, A. RETRACTED ARTICLE: Fuzzy logic-based prediction of compressive strength of lightweight geopolymers. Neural Comput & Applic 23, 865–872 (2013). https://doi.org/10.1007/s00521-012-1009-z.

[3] Ali Nazari, Gholamreza Khalaj, Prediction compressive strength of lightweight geopolymers by ANFIS, Ceramics International, Volume 38, Issue 6, 2012, Pages 4501–4510, https://doi.org/10.1016/j.ceramint.2012.02.026.

[4] Nazari, A. RETRACTED ARTICLE: Artificial neural networks for prediction compressive strength of geopolymers with seeded waste ashes. Neural Comput & Applic 23, 391–402 (2013). https://doi.org/10.1007/s00521-012-0931-4.

[5] Nazari, A. RETRACTED ARTICLE: Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials. Neural Comput & Applic 23, 417–427 (2013). https://doi.org/10.1007/s00521-012-0934-1.

[6] Nazari, A., Riahi, S. RETRACTED ARTICLE: Artificial neural networks to prediction total specific pore volume of geopolymers produced from waste ashes. Neural Comput & Applic 22, 719–729 (2013). https://doi.org/10.1007/s00521-011-0760-x.

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Nazari, A. Retraction Note to: Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput & Applic (2021). https://doi.org/10.1007/s00521-020-05660-6

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