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All issues Volume 184 (2020) E3S Web Conf., 184 (2020) 01102 References
Open Access
Issue
E3S Web Conf.
Volume 184, 2020
2nd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2020)
Article Number 01102
Number of page(s) 4
DOI https://doi.org/10.1051/e3sconf/202018401102
Published online 19 August 2020
  1. MarekSłon´ski, “A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks”, Computers and Structures, Vol 88, Issue2010, pp 1248-1253. [Google Scholar]
  2. Tao Ji, Tingwei Lin, Xujian Lin, “A concrete mix proportion design algorithm based on artificial neural networks” Issue 13 January 2006. [Google Scholar]
  3. Osama Hodhod, Gamal A. Salama, Ain “Analysis of sulfate resistance in concrete based on Artificial Neural Networks and USBR4908-modeling” Issue 27 March 2013. [Google Scholar]
  4. Vijay Pal Singh, Yogesh Chandra Kotiyal, “Prediction of compressive strength using artificial neural network” International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, Vol:7, No:12, Issue 2013. [Google Scholar]
  5. Hamid Eskandari, Mortez Tayyebini, “Effect of 32.5 and 42.5 cement grades on ANN prediction of fibrocement compressive strength” International Conference on Industrial Engineering, ICIE Issue 2016. [Google Scholar]
  6. Faezehossadat Khademi, Sayed Mohammadmehdi J amal, Neela Deshpande, Shreenivasondhec,”Predicting strength of recycled aggregate concrete using artificial neural network, adaptive neuro-fuzzy inference system and multiple linear regression” International Journal of Sustainable Built Environment, Issue 17 September 2016. [Google Scholar]
  7. O.A. Hodhoda, G. Salama, Ain “Simulation of expansion in cement based materials subjected to external sulfate attack” Shams Engineering Journal, Issue 1 July 2013. [Google Scholar]
  8. O.A. Hodhoda, H.I. Ahmed, “Developing an artificial neural network model to evaluate chloride diffusivity in high performance concrete” Housing and Building National Research Center, Issue 10 September 2012. [Google Scholar]
  9. Kasım Mermerdas, Mohamed Moafak Arbili,”Explicit formulation of drying and autogenous shrinkage of concretes with binary and ternary blends of silica fume and fly ash” Construction and Building Materials, Vol 94 Issue 2015 pp 371-379. [Google Scholar]
  10. M.B. Karakoça, R. Demirboğab,c, İ. Türkmena, İ. Canc “Expanded perlite aggregate on cyclic thermal loading of HSC and artificial neural network modeling” Scientia Iranica, Issue 21 November 2011. [Google Scholar]
  11. Anoop K. Sooda, Raj K. Ohdarb, Siba S. Mahapatrac, “Experimental investigation and empirical modelling of FDM process for compressive strength improvement” Journal of Advanced Research Issue June 2012, pp 81-90 . [Google Scholar]
  12. M. AminulHaque, Md.Rasel-Ul-Alam, “Non-linear models for the prediction of specified design strengths of concretes development profile” Housing and Building National Research Center, Issue 18 April 2016. [Google Scholar]
  13. Salah A. Abo-El-Enein, Hamdy A. El-Sayed, Ali H. Ali, Yasser T. Mohammed ,Hisham M. Khater, Ahmed S. Ouda, “Physico-mechanical properties of high performance concrete using different aggregates in presence of silica fume” Housing and Building National Research Center, Issue 18 June 2013. [Google Scholar]
  14. Adriana Trocoli AbdonDantas, Mônica Batista Leite, Koji de Jesus Nagahama”Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks”, Construction and Building Materials Vol 38, January 2013, pp. 717-722. [Google Scholar]
  15. Ahmed M. Diab, Hafez E. Elyamany, Abd Elmoaty M. AbdElmoaty, Ali H. Shalan, Alexandria “ Prediction of concrete compressive strength due to long term sulfate attack using neural network” University Alexandria Engineering Journal, Issue 9 May 2014. [Google Scholar]
  16. Seung-Chang Lee, “Prediction of concrete strength using artificial neural networks”, Engineering Structures, Vol 25 Issue 2003 pp. 849-857. [Google Scholar]
  17. Tummala Suresh Kumar, Kosaraju Satyanarayana, Materials Today: Proceeding, 26 (2), 3228-3233, (2020). [CrossRef] [Google Scholar]
  18. Hamid Eskandaria, Morteza Gharouni Nikb, Mohammad Mahdi Eidia “Prediction of mortar compressive strengths for different cement grades in the vicinity of sodium chloride using ANN” International Conference on Industrial Engineering, ICIE, Issue 2016. [Google Scholar]
  19. O.A. Hodhoda, G.Salama “Simulating USBR4908 by ANN modeling to analyze the effect of mineral admixture with ordinary and pozzolanic cements on the sulfate resistance of concrete” Housing and Building National Research Center, Issue 6 February 2013. [Google Scholar]
  20. Saif Salah Alquzweeni, “Artificial neural network model for predicting compressive strength of high strength concrete after burning” International Journal of Civil & Environmental Engineering IJCEE-IJENS, Vol: 15 No: 06. [Google Scholar]
  21. N.K. Lee, H. Souri and H.K. Lee “Neural network application overview in prediction of properties of cement-based mortar and concrete”. [Google Scholar]

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