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Retraction Note to: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam

  • Ali Toghroli
  • Meldi Suhatril
  • Zainah Ibrahim
  • Maryam SafaEmail author
  • Mahdi Shariati
  • Shahaboddin Shamshirband
Retraction Note
  • 75 Downloads

Retraction To: J Intell Manuf (2018) 29:1793–1801  https://doi.org/10.1007/s10845-016-1217-y

The Editor-in-Chief has retracted this article (Toghroli et al. 2018) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited Cojbasic et al. 2016; Mazinani et al. 2016; Mohammadian et al. 2016; Mansourvar et al. 2015) and authorship manipulation. Meldi Suhatril, Zainah Ibrahim, Maryam Safa, Mahdi Shariati and Shahaboddin Shamshirband do not agree to this retraction. Ali Toghroli has not responded to any correspondence about this retraction.

Notes

References

  1. Cojbasic, Z., et al. (2016). Surface roughness prediction by extreme learning machine constructed with abrasive water jet. Precision Engineering,43, 86–92.  https://doi.org/10.1016/j.precisioneng.2015.06.013.CrossRefGoogle Scholar
  2. Mansourvar, M., Shamshirband, S., Raj, R. G., Gunalan, R., & Mazinani, I. (2015). An automated system for skeletal maturity assessment by extreme learning machines. PLoS ONE,10(9), e0138493.  https://doi.org/10.1371/journal.pone.0138493.CrossRefGoogle Scholar
  3. Mazinani, I., et al. (2016). Estimation of Tsunami bore forces on a coastal bridge using an extreme learning machine. Entropy,18(5), 167.  https://doi.org/10.3390/e18050167.CrossRefGoogle Scholar
  4. Mohammadian, E., Motamedi, S., Shamshirband, S., et al. (2016). Application of extreme learning machine for prediction of aqueous solubility of carbon dioxide. Environmental Earth Science,75, 215.  https://doi.org/10.1007/s12665-015-4798-4.CrossRefGoogle Scholar
  5. Toghroli, A., Suhatril, M., Ibrahim, Z., et al. (2018). Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam. Journal of Intelligent Manufacturing,29, 1793.  https://doi.org/10.1007/s10845-016-1217-y.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Ali Toghroli
    • 1
  • Meldi Suhatril
    • 1
  • Zainah Ibrahim
    • 1
  • Maryam Safa
    • 1
    Email author
  • Mahdi Shariati
    • 1
  • Shahaboddin Shamshirband
    • 2
  1. 1.Department of Civil EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Computer System and Technology, Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala LumpurMalaysia

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