EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 641 (2025) E3S Web Conf., 641 (2025) 01026 References
Open Access
Issue
E3S Web Conf.
Volume 641, 2025
The 17th International Scientific Conference of Civil and Environmental Engineering for the PhD. Students and Young Scientists – Young Scientist 2025 (YS25)
Article Number 01026
Number of page(s) 7
Section Civil Engineering
DOI https://doi.org/10.1051/e3sconf/202564101026
Published online 12 August 2025
  1. P.-C. Aïtcin, Cements of yesterday and today: Concrete of tomorrow, Cement and Concrete Research, 30, 1349-1359 (2000), https://doi.org/10.1016/S0008-8846(00)00365-3 [Google Scholar]
  2. M.C.G. Juenger, F. Winnefeld, J.L. Provis, J.H. Ideker, Advances in alternative cementitious binders, Cement and Concrete Research, 41, 1232-1243 (2011), https://doi.org/10.1016/j.cemconres.2010.11.012 [CrossRef] [Google Scholar]
  3. S. Zhang, J. Teizer, J.-K. Lee, Ch. M. Eastman, M. Venugopal, Building Information Modeling (BIM) and Safety: Automatic Safety Checking of Construction Models and Schedules, Automation in Construction, 29, 183-195 (2013), https://doi.org/10.1016/j.autcon.2012.05.006 [Google Scholar]
  4. B. C. McLellan, R. P. Williams, J. Lay, A. van Riessen, G. D. Corder, Costs and carbon emissions for geopolymer pastes in comparison to ordinary portland cement, Journal of Cleaner Production, 19, 1080-1090 (2011), https://doi.org/10.1016/j.jclepro.2011.02.010 [Google Scholar]
  5. C. Meyer, The greening of the concrete industry, Cement and Concrete Composites, 31, 601-605 (2009), https://doi.org/10.1016/j.cemconcomp.2008.12.010 [Google Scholar]
  6. D.-Ch. Feng, Z.-T. Liu, X.-D. Wang, Y. Chen, J.-Q. Chang, et al., Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach, Construction and Building Materials, 230, 117000 (2020), https://doi.org/10.1016/j.conbuildmat.2019.117000 [Google Scholar]
  7. W. B. Chaabene, M. Flah, M. L. Nehdi, Machine learning prediction of mechanical properties of concrete: Critical review, Construction and Building Materials, 260, 119889 (2020), https://doi.org/10.1016/j.conbuildmat.2020.119889 [Google Scholar]
  8. H.-T. Thai, Machine learning for structural engineering: A state-of-the-art review, Structures, 38, 448-491 (2022), https://doi.org/10.1016/j.istruc.2022.02.003 [Google Scholar]
  9. I.-C. Yeh, Modeling of strength of high-performance concrete using artificial neural networks, Cement and Concrete Research, 28, Issue 12, 1797-1808 (1998), https://doi.org/10.1016/S0008-8846(98)00165-3 [Google Scholar]
  10. S.-Ch. Lee, Prediction of concrete strength using artificial neural networks, Engineering Structures, 25, 849-857 (2003), https://doi.org/10.1016/S0141-0296(03)00004-X [Google Scholar]
  11. H. Naseri, H. Jahanbakhsh, P. Hosseini, F. M. Nejad, Designing sustainable concrete mixture by developing a new machine learning technique, Journal of Cleaner Production, 258, 120578 (2020), https://doi.org/10.1016/j.jclepro.2020.120578 [Google Scholar]
  12. M. Shahrokhishahraki, M. Malekpour, S. Mirvalad, G. Faraone, Machine learning predictions for optimal cement content in sustainable concrete constructions, Journal of Building Engineering, 82, 108160 (2024), https://doi.org/10.1016/j.jobe.2023.108160 [Google Scholar]
  13. R. Gandel, J. Jerabek, Z. Marcalikova, Reinforced Concrete Beams Without Shear Reinforcement Using Fiber Reinforced Concrete and Alkali-Activated Material, Civil and Environmental Engineering, 19, 348-356, (2023), https://doi.org/10.2478/cee-2023-0031 [Google Scholar]
  14. O. Sucharda, Z. Marcalikova, R. Gandel, Microstructure, Shrinkage, and Mechanical Properties of Concrete with Fibers and Experiments of Reinforced Concrete Beams without Shear Reinforcement, Materials, 15, 5707, (2022), https://doi.org/10.3390/ma15165707 [Google Scholar]
  15. P. Mateckova, V. Bilek, O. Sucharda, Comparative Study of High-Performance Concrete Characteristics and Loading Test of Pretensioned Experimental Beams, Crystals, 11, 427, (2021), https://doi.org/10.3390/cryst11040427 [Google Scholar]
  16. Z. Marcalikova, J. Jerabek, R. Gandel, R. Gabor, V. Bilek, O. Sucharda, Mechanical Properties, Workability, and Experiments of Reinforced Composite Beams with Alternative Binder and Aggregate, Buildings, 14, 2142, (2024), https://doi.org/10.3390/buildings14072142 [Google Scholar]
  17. J. Nemec, R. Gandel, J. Jerabek, O. Sucharda, V. Bilek, Properties of Selected Alkali-Activated Materials for Sustainable Development, Civil and Environmental Engineering, 20, 307–318, (2024), https://doi.org/10.2478/cee-2024-0024 [Google Scholar]
  18. O. Sucharda, R. Gandel, P. Cmiel, J. Jerabek, V. Bilek, Utilization of High-Performance Concrete Mixtures for Advanced Manufacturing Technologies, Buildings, 14, 2269, (2024), https://doi.org/10.3390/buildings14082269\ [Google Scholar]
  19. I. Yeh, Concrete Compressive Strength [Dataset], UCI Machine Learning Repository (1998), https://doi.org/10.24432/C5PK67 [Google Scholar]
  20. ČSN EN 12390-3. Testing hardened concrete – Part 3: Compressive strength of test specimens. Prague: The Czech Office for Standards, Metrology and Testing, 2020 [Google Scholar]
  21. R. Gandel, J. Jerabek, P. Cmiel, O. Sucharda, Design of concrete mixtures and prediction of their compressive strength using machine learning (experimental dataset), https://doi.org/10.5281/zenodo.15406920 [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.