Open Access
| Issue |
EPJ Web Conf.
Volume 345, 2026
4th International Conference & Exposition on Materials, Manufacturing and Modelling Techniques (ICE3MT2025)
|
|
|---|---|---|
| Article Number | 01036 | |
| Number of page(s) | 11 | |
| DOI | https://doi.org/10.1051/epjconf/202634501036 | |
| Published online | 07 January 2026 | |
- M. Mishra, Machine learning techniques for structural health monitoring of heritage buildings: a state-of-the-art review and case studies. J. Cult. Herit. 47, 227–245 (2021). https://doi.org/10.1016/j.culher.2020.09.005 [Google Scholar]
- J.C. Morel, A. Pkla, P. Walker, Compressive strength testing of compressed earth blocks. Constr. Build. Mater. 21(2), 303–309 (2007). https://doi.org/10.1016/j.conbuildmat.2005.08.021 [Google Scholar]
- N.M. Mubarak, M. Anwar, S. Debnath, I. Sudin, Fundamentals of biomaterials. Springer Nature Singapore, Singapore (2023). https://doi.org/10.1007/978-981-19- 9300-8 [Google Scholar]
- K.T. Nguyen, Q.D. Nguyen, T.A. Le, J. Shin, K. Lee, Analyzing the compressive strength of green fly ash based geopolymer concrete using experiment and machine learning approaches. Constr. Build. Mater. 247, 118581 (2020). https://doi.org/10.1016/j.conbuildmat.2020.118581 [Google Scholar]
- F. Deng, Y. He, S. Zhou, Y. Yu, H. Cheng, X. Wu, Compressive strength prediction of recycled concrete based on deep learning. Constr. Build. Mater. 175, 562–569 (2018). https://doi.org/10.1016/j.conbuildmat.2018.04.169 [Google Scholar]
- D.C. Feng, Z.-T. Liu, X.-D. Wang, Y. Chen, J.-Q. Chang, D.-F. Wei, Z.-M. Jiang, Machine learning based compressive strength prediction for concrete: an adaptive boosting approach. Constr. Build. Mater. 230, 117000 (2020). https://doi.org/10.1016/j.conbuildmat.2019.117000 [Google Scholar]
- W.Z. Taffese, E. Sistonen, Machine learning for durability and service-life assessment of reinforced concrete structures: recent advances and future directions. Autom. Constr. 77, 1–14 (2017). https://doi.org/10.1016/j.autcon.2017.01.016 [Google Scholar]
- M. Mishra, A.S. Bhatia, D. Maity, Predicting the compressive strength of unreinforced brick masonry using machine learning techniques validated on a case study of a museum through nondestructive testing. J. Civ. Struct. Health Monit. 10(3), 389–403 (2020). https://doi.org/10.1007/s13349-020-00391-7 [Google Scholar]
- J. Garzón-Roca, C. Obrer Marco, J.M. Adam, Compressive strength of masonry made of clay bricks and cement mortar: estimation based on neural networks and fuzzy logic. Eng. Struct. 48, 21–27 (2013). https://doi.org/10.1016/j.engstruct.2012.09.029 [Google Scholar]
- Q. Zhou, F. Wang, F. Zhu, Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems. Constr. Build. Mater. 125, 417–426 (2016). https://doi.org/10.1016/j.conbuildmat.2016.08.064 [Google Scholar]
- U. Reuter, A. Sultan, D.S. Reischl, A comparative study of machine learning approaches for modeling concrete failure surfaces. Adv. Eng. Softw. 116, 67–79 (2018). https://doi.org/10.1016/j.advengsoft.2017.11.006. [Google Scholar]
- Bhumika M R, M Tech thesis submitted to Department of Civil Engineering, RV College of Engineering,Bangalore. [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.

