Retraction Note to: Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks

Neural Computing and Applications https://doi.org/10.1007/s00521-020-05122-z (0123456789().,-volV)(0123456789().,-volV) 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 under consideration at the same time [2–4] 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. References 1. Riahi, S., Nazari, A. Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks. Neural Comput Appl 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 Appl 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/SiCp-laminated nanocomposites. Neural Comput Appl 23, 801–813 (2013). https://doi.org/ 10.1007/s00521-012-0996-0 4. Nazari, A. Prediction water absorption resistance of lightweight geopolymers by artificial neural networks. Neural Comput Appl 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 Appl 23, 391–402 (2013). https://doi.org/10.1007/s00521-0120931-4 6. Nazari, A. Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput Appl 23, 507–518 (2013). https://doi.org/10.1007/s00521-0120945-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 1 Department of Materials Science and Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran 123  (...truncated)