Retraction Note to: Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers b
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RETRACTION NOTE
Retraction Note to: Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks Shadi Riahi1 • Ali Nazari1
Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Retraction Note to: Neural Comput & Applic (2019) 31:743–750 https://doi.org/10.1007/s00521-012-1085-0
The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were consideration at the same time [2, 3, 4] and previously published articles [5, 6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction. [1] Riahi, S., Nazari, A. Predicting the effects of nanoparticles on early age compressive strength of ashbased geopolymers by artificial neural networks. Neural Comput & Applic 31, 743–750 (2019). https://doi.org/10. 1007/s00521-012-1085-0 [2] Nazari, A., Hajiallahyari, H., Rahimi, A. et al. Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks. Neural Comput & Applic 31, 733–741 (2019). https://doi.org/10.1007/ s00521-012-1082-3
[3] Nazari, A., Abdinejad, V.R. Artificial neural networks for prediction Charpy impact energy of Al6061/SiCplaminated nanocomposites. Neural Comput & Applic 23, 801–813 (2013). https://doi.org/10.1007/s00521-012-09960 [4] Nazari, A. Prediction water absorption resistance of lightweight geopolymers by artificial neural networks. Neural Comput & Applic 31, 759–766 (2019). https://doi. org/10.1007/s00521-012-1136-6 [5] Nazari, A. 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 [6] 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 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original article can be found online at https:// doi.org/10.1007/s00521-012-1085-0. & Ali Nazari [email protected] 1
Department of Materials Science and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
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