EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 220 (2020) E3S Web Conf., 220 (2020) 01097 References
Open Access
Issue
E3S Web Conf.
Volume 220, 2020
Sustainable Energy Systems: Innovative Perspectives (SES-2020)
Article Number 01097
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/202022001097
Published online 19 February 2021
  1. P. Singh and P. Khaskil, “Prediction of Compressive Strength of Green Concrete with Admixtures Using Neural Networks, ” 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, 2020, pp. 714-717, doi: 10.1109/GUCON48875.2020.9231230. [Google Scholar]
  2. Golafshani, Emadaldin Mohammadi, Alireza Rahai, Mohammad Hassan Sebt, and Hamed Akbarpour. “Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic.” Construction and building materials 36 (2012): 411-418. [Google Scholar]
  3. Yan, Fei, Zhibin Lin, Xingyu Wang, Fardad Azarmi, and Konstantin Sobolev. “Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm.” Composite Structures 161 (2017): 441-452. [Google Scholar]
  4. Dahou, Zohra, Z. Mehdi Sbartaï, Arnaud Castel, and Fouad Ghomari. “Artificial neural network model for steel–concrete bond prediction.” Engineering Structures 31, No. 8 (2009): 1724-1733. [Google Scholar]
  5. Sancak, Emre. “Prediction of bond strength of lightweight concretes by using artificial neural networks.” Scientific Research and Essays 4, No. 4 (2009): 256-266. [Google Scholar]
  6. Ahmad, Sohaib, Kypros Pilakoutas, Muhammad M. Rafi, and Qaiser U. Zaman. “Bond strength prediction of steel bars in low strength concrete by using ANN.” Computers and Concrete 22, No. 2 (2018): 249-259. [Google Scholar]
  7. Dantas, Adriana Trocoli Abdon, Monica Batista Leite, and Koji de Jesus Nagahama. “Prediction of compressive strength of concrete containing construction and demolition waste using artificial neural networks.” Construction and Building Materials 38 (2013): 717-722. [Google Scholar]
  8. Duan, Zhen-Hua, Shi-Cong Kou, and Chi-Sun Poon. “Prediction of compressive strength of recycled aggregate concrete using artificial neural networks.” Construction and Building Materials 40 (2013): 1200-1206. [Google Scholar]
  9. Khademi, Faezehossadat, Sayed Mohammadmehdi Jamal, Neela Deshpande, and Shreenivas Londhe. “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 5, No. 2 (2016): 355-369. [Google Scholar]
  10. Vakhshouri, Behnam, and Shami Nejadi. “Prediction of compressive strength of selfcompacting concrete by ANFIS models.” Neurocomputing 280 (2018): 13-22. [Google Scholar]
  11. Singh, Priyanka Ramkripal, Aishwarya Goel, Shailendra Thakur, and N. D. Shah. “An experimental approach to investigate effect of steel fibers on tensile and flexural strength of fly ash concrete.” Int J Sci Eng Appl Sci (IJSEAS) 2, No. 5 (2016): 384-392. [Google Scholar]
  12. Singh, Priyanka, and Niraj D. Shah. “An experimental investigation on sustainable concrete with flyash and steel fibers.” Int J Civil Eng Technoly 9.6 (2018): 1131-1140. [Google Scholar]
  13. Singh, Priyanka Ramkripal, and N. D. Shah. “Impact of coal combustion fly ash used as a binder in pavement.” Civ. Eng. Environ. Tech 1 (2014): 57-60. [Google Scholar]
  14. S. Dixit, A. Stefan, A. Musiuk, and P. Singh, “Study of enabling factors affecting the adoption of ICT in the Indian built environment sector, ” Ain Shams Engineering Journal, no. xxxx, 2020. [Google Scholar]
  15. S. Dixit, A. Stefan, and A. Musiuk, “Architectural form finding in arboreal supporting structure optimisation, ” Ain Shams Engineering Journal, no. xxxx, 2020. [Google Scholar]
  16. Wang, Yi, Zong Woo Geem, and Kohei Nagai. “Bond strength assessment of concrete-corroded rebar interface using artificial neutral network.” Applied Sciences 10, No. 14 (2020): 4724. [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.