Open Access
Issue
E3S Web Conf.
Volume 184, 2020
2nd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED 2020)
Article Number 01103
Number of page(s) 4
DOI https://doi.org/10.1051/e3sconf/202018401103
Published online 19 August 2020
  1. R. Agarwal., et.al., Effect of fibre reinforcing index on compressive and bond strength of steel fibre reinforced concrete, Journal of the institutions of engineers (India), vol. 77, pp. 37-40, (1996). [Google Scholar]
  2. 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. [CrossRef] [Google Scholar]
  3. Tao Ji, et.al. “A concrete mix proportion design algorithm based on artificial neural networks” Issue 13 January 2006. [Google Scholar]
  4. Osama Hodhod, et.al “Analysis of sulfate resistance in concrete based on Artificial Neural Networks and USBR4908-modeling” Issue 27 March 2013. [Google Scholar]
  5. Vijay Pal Singh, et.al “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]
  6. Hamid Eskandari, et.al, “Effect of 32.5 and 42.5 cement grades on ANN prediction of fibrocement compressive strength” International Conference on Industrial Engineering, ICIE Issue2016. [Google Scholar]
  7. Neela Deshpande, et.al, “Modeling compressive strength of recycled aggregate concrete by artificial neural network, model tree and non-linear regression” International Journal of Sustainable Built Environment, Issue 4 December 2014. [Google Scholar]
  8. Faezehossadat Khademi,Sayed et.al “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]
  9. Ahmet O ztas, et.al “Predicting the compressive strength and slump of high strength concrete using neural network” Construction and Building Materials, Vol 20 (2006) 769-775, Issue 23 March 2005. [Google Scholar]
  10. O.A. Hodhoda et.al G. Salama, Ain “Simulation of expansion in cement based materials subjected to external sulfate attack” Shams Engineering Journal, Issue 1 July 2013. [Google Scholar]
  11. O.A. Hodhoda, et.al, “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]
  12. Tummala Suresh Kumar, Kosaraju Satyanarayana, Materials Today: Proceeding, 26 (2), 3228-3233, (2020). [CrossRef] [Google Scholar]
  13. Anoop K. Sooda, et.al “Experimental investigation and empirical modelling of FDM process for compressive strength improvement” Journal of Advanced Research, Issue June 2012, pp 81-90. [Google Scholar]
  14. M. Aminul Haque, et.al, “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]
  15. Salah A. Abo-El-Enein, et.al “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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.