Evolutionary optimization of machine learning algorithm hyperparameters for strength prediction of high-performance concrete
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s42107-023-00698-y.pdf
Reference85 articles.
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2. Aïtcin, P.-C. (1998). High performance concrete. CRC Press. https://doi.org/10.4324/9780203475034
3. Al Yamani, W. H., Ghunimat, D. M., & Bisharah, M. M. (2023). Modeling and predicting the sensitivity of high-performance concrete compressive strength using machine learning methods. Asian Journal of Civil Engineering. https://doi.org/10.1007/s42107-023-00614-4
4. Alqahtani, M., Gumaei, A., Mathkour, H., & Maher Ben Ismail, M. (2019). A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks. Sensors, 19, 4383. https://doi.org/10.3390/s19204383
5. Andonie, R. (2019). Hyperparameter optimization in learning systems. Journal of Membrane Computing, 1, 279–291. https://doi.org/10.1007/s41965-019-00023-0
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