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On developing accurate prediction models for residual tensi..:
Chaeyeon Go
;
Yun-Ji Kwak
;
Shinyoung Kwag
...
http://www.sciencedirect.com/science/article/pii/S2214509523003376. , 2023
Link:
https://doi.org/10.1016/j.cscm.2023.e02157
RT Journal T1 <Title>On developing accurate prediction models for residual tensile strength of GFRP bars under alkaline-concrete environment using a combined ensemble machine learning methods</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:365ed85641304ee0990161bd33a55dab&Exemplar=1&LAN=DE A1 Chaeyeon Go A1 Yun-Ji Kwak A1 Shinyoung Kwag A1 Seunghyun Eem A1 Sangwoo Lee A1 Bu-Seog Ju PB Elsevier YR 2023 K1 GFRP(Glass-fiber reinforced polymer) bar K1 Residual Tensile Strength K1 Ensemble Machine Learning K1 Bagging K1 Stacking K1 Materials of engineering and construction. Mechanics of materials K1 TA401-492 JF http://www.sciencedirect.com/science/article/pii/S2214509523003376 LK http://dx.doi.org/https://doi.org/10.1016/j.cscm.2023.e02157 DO https://doi.org/10.1016/j.cscm.2023.e02157 SF ELIB - SuUB Bremen
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