Predicting the Fracture Characteristics of Concrete Using Ensemble and Meta-heuristic Algorithms
Author:
Publisher
Springer Science and Business Media LLC
Subject
Civil and Structural Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12205-023-0965-4.pdf
Reference76 articles.
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3. An S, Liu W, Venkatesh S (2007) Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression. Pattern Recognit 40(8):2154–2162, DOI: https://doi.org/10.1016/j.patcog.2006.12.015
4. Asteris PG, Lourenço PB, Roussis PC, Adami CE, Armaghani DJ, Cavaleri L, Chalioris CE, Hajihassani M, Lemonis ME, Mohammed AS, Pilakoutas K (2022) Revealing the nature of metakaolin-based concrete materials using artificial intelligence techniques. Construction and Building Materials 322:126500, DOI: https://doi.org/10.1016/j.conbuildmat.2022.126500
5. Asteris PG, Skentou AD, Bardhan A, Samui P, Pilakoutas K (2021) Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models. Cement and Concrete Research 145:106449, DOI: https://doi.org/10.1016/j.cemconres.2021.106449
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