Prediction of split tensile strength of recycled aggregate concrete leveraging explainable hybrid XGB with optimization algorithm
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s41939-024-00480-w.pdf
Reference59 articles.
1. Alarfaj M, Qureshi HJ, Shahab MZ, Javed MF, Arifuzzaman M, Gamil Y (2024) Machine learning based prediction models for spilt tensile strength of fiber reinforced recycled aggregate concrete. Case Stud Constr Mater 20:e02836. https://doi.org/10.1016/j.cscm.2023.e02836
2. Alyaseen A, Poddar A, Kumar N, Sihag P, Lee D, kumar R, Singh T (2024) Assessing the compressive and splitting tensile strength of self-compacting recycled coarse aggregate concrete using machine learning and statistical techniques. Mater Today Commun 38. https://doi.org/10.1016/j.mtcomm.2023.107970
3. Asteris PG, Koopialipoor M, Armaghani DJ, Kotsonis EA, Lourenço PB (2021) Prediction of cement-based mortars compressive strength using machine learning techniques. In Neural Computing and Applications (Vol. 33, Issue 19). Springer London. https://doi.org/10.1007/s00521-021-06004-8
4. Bardhan A, Singh RK, Ghani S, Konstantakatos G, Asteris PG (2023) Modelling soil compaction parameters using an enhanced Hybrid Intelligence Paradigm of ANFIS and Improved Grey Wolf Optimiser. Mathematics 11(14). https://doi.org/10.3390/math11143064
5. Bentéjac C, Csörgő A, Martínez-Muñoz G (2021) A comparative analysis of gradient boosting algorithms. Artif Intell Rev 54(3):1937–1967. https://doi.org/10.1007/s10462-020-09896-5
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1. Explainable hybridized ensemble machine learning for the prognosis of the compressive strength of recycled plastic-based sustainable concrete with experimental validation;Multiscale and Multidisciplinary Modeling, Experiments and Design;2024-08-21
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