Retraction Note to: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

Neural Computing and Applications https://doi.org/10.1007/s00521-020-05121-0 (0123456789().,-volV)(0123456789(). ,- volV) RETRACTION NOTE Retraction Note to: Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks Ali Nazari1 • Hadi Hajiallahyari1 • Ali Rahimi1 • Hamid Khanmohammadi1 • Mohammad Amini1 Ó Springer-Verlag London Ltd., part of Springer Nature 2020 Retraction Note to: Neural Comput & Applic (2019) 31 (Suppl 2):S733–S741 https://doi.org/10.1007/s00521-012-1082-3 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, 3] and previously published articles [4–6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction. References 1. Nazari A, Hajiallahyari H, Rahimi A et al (2019) Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks. Neural Comput Appl 31:733–741. https://doi.org/10.1007/s00521-012-1082-3 2. Nazari A (2012) Application of fuzzy logic for prediction compressive strength of OPC based geopolymers. Mater Technol 27(5):364–370. https://doi.org/10.1179/1753555712Y.0000000021 3. Riahi S, Nazari A (2019) Predicting the effects of nanoparticles on early age compressive strength of ash-based geopolymers by artificial neural networks. Neural Comput Appl 31:743–750. https://doi.org/10.1007/s00521-012-1085-0 4. Nazari Ali (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 5. Nazari A (2013) Artificial neural networks for prediction compressive strength of geopolymers with seeded waste ashes. Neural Comput Appl 23:391–402. https://doi.org/10.1007/s00521-0120931-4 6. Nazari A (2013) Artificial neural networks application to predict the compressive damage of lightweight geopolymer. Neural Comput Appl 23:507–518. 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/s00521-012-1082-3. & Ali Nazari 1 Department of Materials Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran 123  (...truncated)