Unified compressive strength and strain ductility models for fully and partially FRP-confined circular, square, and rectangular concrete columns

• Autor(a) principal: Shayanfar, Javad
• Data de Publicação: 2023
• Outros Autores: Barros, Joaquim A. O., Abedi, Mohammadmahdi, Rezazadeh, Mohammadali
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/1822/88664
• topic: Unified model; Compressive strength; Axial strain ductility; Partial fiber-reinforced polymer (FRP) confinement; Noncircular cross section; Engenharia e Tecnologia::Engenharia Civil

Determination of fiber-reinforced polymer (FRP) confinement-induced improvements in the mechanical properties of concrete columns under compression is a current concern, particularly if partial confinement applied on a noncircular cross-sectional shape is to be considered. Although several design-oriented predictive formulations have been proposed for the calculation of axial strength and axial strain ductility of FRP-confined concrete, their applications are, in general, limited to a specific cross-sectional shape (circular, square, or rectangular cross section) and a certain confinement arrangement (fully or partially confining system). Accordingly, the aim in this study is to establish new unified strength and ductility models for concrete columns of circular or noncircular cross sections with fully or partially confining FRP systems. To achieve the highest level of predictive performance through a nonlinear regression technique, two datasets, consisting of 2,117 test data of peak strength and 2,050 test data of strain ductility, available in the literature, were collected. The dominance degrees of size effect, sectional noncircularity (corner radius ratio), cross-sectional aspect ratio, and confinement configuration type on confinement effectiveness were evaluated and reflected in the development of these regression-based models. Through predictions of test data compiled in the datasets and a comparison with the performances of available predictive models, the proposed unified formulations demonstrated a high level of reliability and were found to be proper for design purposes.