1 Retraction Note to: Neural Comput & Applic (2019) 31 (Suppl 2):S751–S757 https://doi.org/10.1007/s00521-012-1135-7
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 author has not responded to any correspondence regarding this retraction.
References
Nazari A (2019) Predicting the total specific pore volume of geopolymers produced from waste ashes by gene expression programming. Neural Comput Appl 31:751–757. https://doi.org/10.1007/s00521-012-1135-7
Nazari A, Riahi S (2013) RETRACTED ARTICLE: Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming. Neural Comput Appl 23:1677–1685. https://doi.org/10.1007/s00521-012-1127-7
Nazari A (2013) Compressive strength of geopolymers produced by ordinary Portland cement: application of genetic programming for design. Mater Des 43:356–366. https://doi.org/10.1016/j.matdes.2012.07.012
Nazari A (2012) Experimental study and computer-aided prediction of percentage of water absorption of geopolymers produced by waste fly ash and rice husk bark ash. Int J Miner Process 110–111:74–81. https://doi.org/10.1016/j.minpro.2012.04.007
Nazari A, Riahi S, Khalaj G, Bohlooli H, Kaykha MM (2012) RETRACTED: Prediction of compressive strength of geopolymers with seeded fly ash and rice husk–bark ash by gene expression programming. Int J Damage Mech 21(8):1202–1226. https://doi.org/10.1177/1056789511431991
Nazari A (2019) RETRACTED ARTICLE: Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer. Neural Comput Appl 31:767–776. https://doi.org/10.1007/s00521-012-1137-5
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Nazari, A. Retraction Note to: Predicting the total specific pore volume of geopolymers produced from waste ashes by gene expression programming. Neural Comput & Applic 32, 17811 (2020). https://doi.org/10.1007/s00521-020-05155-4
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DOI: https://doi.org/10.1007/s00521-020-05155-4