Prediction of the self-healing properties of concrete modified with bacteria and fibers using machine learning
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00878-w.pdf
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2. 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
3. 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
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5. 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
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