Concrete-filled steel tubular (CFST) columns are increasingly used around the world because they offer two significant advantages. The first one is the composite action of the steel tube and infilled concrete, which enhances the strength and ductility of the columns. The second one is the use of the steel tube as a permanent formwork for concrete casting, which allows saving construction time and costs. Although considerable research and several experimental tests have been carried out on CFST columns, there is not a probabilistic model for the evaluation of their axial capacity yet. Additionally, the effects of the axial load eccentricity and debonding have received little attention so far. Within this framework, the present paper proposes a physics-based probabilistic model to predict the ultimate axial capacity of CFST columns, which is developed as the sum of a deterministic part and a probabilistic correction term, together with two additional corrective models to describe its reduction due to load eccentricity and debonding, which are developed coherently with the axial capacity model as probabilistic correction terms. The accuracy of the proposed models is compared with that of existing capacity equations already in use within technical standards and available literature. Additionally, this study provides uncertainty factors to allow using the proposed capacity model for design applications. Thanks to their very good accuracy and compact form, the proposed models are suitable to be included within technical standards. On the other hand, differently from common deterministic models, they can also be updated as new experimental data are available. A case study with the derivation of fragility curves for a CFST bridge column shows a possible application and the advantages of the proposed model.
Probabilistic axial capacity model for concrete-filled steel tubes accounting for load eccentricity and debonding / Contento, A.; Aloisio, A.; Xue, J.; Quaranta, G.; Briseghella, B.; Gardoni, P.. - In: ENGINEERING STRUCTURES. - ISSN 0141-0296. - 268:(2022). [10.1016/j.engstruct.2022.114730]
Probabilistic axial capacity model for concrete-filled steel tubes accounting for load eccentricity and debonding
Contento A.;Quaranta G.;
2022
Abstract
Concrete-filled steel tubular (CFST) columns are increasingly used around the world because they offer two significant advantages. The first one is the composite action of the steel tube and infilled concrete, which enhances the strength and ductility of the columns. The second one is the use of the steel tube as a permanent formwork for concrete casting, which allows saving construction time and costs. Although considerable research and several experimental tests have been carried out on CFST columns, there is not a probabilistic model for the evaluation of their axial capacity yet. Additionally, the effects of the axial load eccentricity and debonding have received little attention so far. Within this framework, the present paper proposes a physics-based probabilistic model to predict the ultimate axial capacity of CFST columns, which is developed as the sum of a deterministic part and a probabilistic correction term, together with two additional corrective models to describe its reduction due to load eccentricity and debonding, which are developed coherently with the axial capacity model as probabilistic correction terms. The accuracy of the proposed models is compared with that of existing capacity equations already in use within technical standards and available literature. Additionally, this study provides uncertainty factors to allow using the proposed capacity model for design applications. Thanks to their very good accuracy and compact form, the proposed models are suitable to be included within technical standards. On the other hand, differently from common deterministic models, they can also be updated as new experimental data are available. A case study with the derivation of fragility curves for a CFST bridge column shows a possible application and the advantages of the proposed model.File | Dimensione | Formato | |
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