Open Access
Issue |
E3S Web of Conf.
Volume 559, 2024
2024 International Conference on Sustainable Technologies in Civil and Environmental Engineering (ICSTCE 2024)
|
|
---|---|---|
Article Number | 04033 | |
Number of page(s) | 7 | |
Section | Structural Engineering & Concrete Technology | |
DOI | https://doi.org/10.1051/e3sconf/202455904033 | |
Published online | 08 August 2024 |
- Carolina Luiza Emerenciana Pessoa · Victor Hugo Peres Silva · Ricardo Stefani, Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning Asian Journal of Civil Engineering (2024) 25:1801–1810. https://doi.org/10.1007/s42107-023-00878-w [CrossRef] [Google Scholar]
- Ai, H., Wu, X., Zhang, L., Qi, M., Zhao, Y., Zhao, Q., Zhao, J., & Liu, H. (2019). QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods. Ecotoxicology and Environmental Safety, 179, 71–78. https://doi.org/10.1016/j.ecoenv.2019.04.035 [CrossRef] [Google Scholar]
- Alabduljabbar, H., Khan, K., Awan, H. H., Alyousef, R., Mohamed, A. M., & Eldin, S. M. (2023). Modeling the capacity of engineered cementitious composites for self-healing using AI-based ensemble techniques. Case Studies in Construction Materials. https://doi.org/10.1016/j.cscm 2022.e01805 [Google Scholar]
- Althoey, F., Amin, M. N., Khan, K., Usman, M. M., Khan, M. A., Javed, M. F., Sabri, M. M. S., Alrowais, R., & Maglad, A. M. (2022). Machine learning based computational approach for crack width detection of self-healing concrete. Case Studies in Construction Materials, 17, e01610. https://doi.org/10.1016/j.cscm 2022.e01610 [CrossRef] [Google Scholar]
- Balcázar, J., Dai, Y., & Watanabe, O. (2001). A random sampling technique for training support vector machines: for primal-form maximal-margin classifiers. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2225, 119–134. https://doi.org/10.1007/3-540-45583-3_11 [Google Scholar]
- Barbosa-Da-Silva, R., & Stefani, R. (2013). QSPR based on support vector machines to predict the glass transition temperature of compounds used in manufacturing OLEDs. Molecular Simulation. https://doi.org/10.1080/08927022.2012.717282 [Google Scholar]
- Bayar, G., & Bilir, T. (2019). A novel study for the estimation of crack propagation in concrete using machine learning algorithms. Construction and Building Materials, 215, 670–685. https://doi.org/10.1016/j.conbuildmat.2019.04.227 [CrossRef] [Google Scholar]
- Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. https://doi.org/10.1023/A:1009715923555 [Google Scholar]
- Chen, G., Tang, W., Chen, S., Wang, S., & Cui, H. (2022). Prediction of self-healing of engineered cementitious composite using machine learning approaches. Applied Sciences (Switzerland), 12(7), 1–27. https://doi.org/10.3390/app12073605 [Google Scholar]
- Congro, M., de Monteiro, V. M. A., Brandão, A. L. T., dos Santos, B. F., Roehl, D., & de Silva, F. A. (2021). Prediction of the residual flexural strength of fiber reinforced concrete using artificial neural networks. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2021.124502 [Google Scholar]
- Ehrman, T. M., Barlow, D. J., & Hylands, P. J. (2007). Virtual screening of Chinese herbs with random forest. Journal of Chemical Information and Modeling, 47(2), 264–278. https://doi.org/10.1021/ci600289v [CrossRef] [PubMed] [Google Scholar]
- Feng, D. C., Liu, Z. T., Wang, X. D., Chen, Y., Chang, J. Q., Wei, D. F., & Jiang, Z.M. (2020). Machine learning-based compressive strength prediction for concrete: an adaptive boosting approach. Construction and Building Materials. https://doi.org/10.1016/j.conbuildmat.2019.117000 [Google Scholar]
- Feng, J., Chen, B., Sun, W., & Wang, Y. (2021). Microbial induced calcium carbonate precipitation study using Bacillus subtilis with application to self-healing concrete preparation and characterization. Construction and Building Materials, 280, 122460. https://doi.org/10.1016/J.CONBUILDMAT.2021.122460 [CrossRef] [Google Scholar]
- Feng, J., Su, Y., & Qian, C. (2019). Coupled effect of PP fiber, PVA fiber and bacteria on self-healing efficiency of early-age cracks in concrete. Construction and Building Materials, 228, 116810. https://doi.org/10.1016/J.CONBUILDMAT.2019.116810 [CrossRef] [Google Scholar]
- Güçlüer, K., Özbeyaz, A., Göymen, S., & Günaydın, O. (2021). A comparative investigation using machine learning methods for concrete compressive strength estimation. Materials Today Communications. https://doi.org/10.1016/j.mtcomm.2021.102278 [Google Scholar]
- Gupta, S., Kua, H. W., & Pang, S. D. (2018). Healing cement mortar by immobilization of bacteria in biochar: an integrated approach of self-healing and carbon sequestration. Cement and Concrete Composites, 86, 238–254. https://doi.org/10.1016/j.cemconcomp.2017.11.015 [CrossRef] [Google Scholar]
- Hemmateenejad, B., & Yazdani, M. (2009). QSPR models for half-wave reduction potential of steroids: A comparative study between feature selection and feature extraction from subsets of or entire set of descriptors. Analytica Chimica Acta, 634(1), 27–35. https://doi.org/10.1016/j.aca.2008.11.062 [CrossRef] [PubMed] [Google Scholar]
- Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019). Data-driven materials science: Status, challenges, and perspectives. Advanced Science. https://doi.org/10.1002/advs.201900808 [Google Scholar]
- Hossain, M. R., Sultana, R., Patwary, M. M., Khunga, N., Sharma, P., & Shaker, S. J. (2022). Self-healing concrete for sustainable buildings. A review. Environmental Chemistry Letters (Vol. 20, pp. 1265–1273). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/s10311-021-01375-9 (Issue 2). [CrossRef] [Google Scholar]
- Huang, X., Ge, J., Kaewunruen, S., & Su, Q. (2020). The self-sealing capacity of environmentally friendly, highly damped, fibre-reinforced concrete. Materials. https://doi.org/10.3390/ma13020298 [Google Scholar]
- Huang, X., Sresakoolchai, J., Qin, X., Ho, Y. F., & Kaewunruen, S. (2022). Self-healing performance assessment of bacterial-based concrete using machine learning approaches. Materials. https://doi.org/10.3390/ma15134436 [Google Scholar]
- Jamshidi, M., El-Badry, M., & Nourian, N. (2023). Improving concrete crack segmentation networks through cutmix data synthesis and temporal data fusion. Sensors. https://doi.org/10.3390/s23010504 [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.