Shear strength model for reinforced concrete corbels based on panel response
Author
dc.contributor.author
Massone Sánchez, Leonardo
Author
dc.contributor.author
Alvarez, Julio
Admission date
dc.date.accessioned
2017-11-29T20:24:08Z
Available date
dc.date.available
2017-11-29T20:24:08Z
Publication date
dc.date.issued
2016
Cita de ítem
dc.identifier.citation
Earthquakes and Structures, Vol. 11, N° 4 (2016): 723-740
es_ES
Identifier
dc.identifier.issn
2092-7614
Identifier
dc.identifier.other
10.12989/eas.2016.11.4.723
Identifier
dc.identifier.uri
https://repositorio.uchile.cl/handle/2250/145925
Abstract
dc.description.abstract
Reinforced concrete corbels are generally used to transfer loads within a structural system, such as buildings, bridges, and facilities in general. They commonly present low aspect ratio, requiring an accurate model for shear strength prediction in order to promote flexural behavior. The model described here, originally developed for walls, was adapted for corbels. The model is based on a reinforced concrete panel, described by constitutive laws for concrete and steel and applied in a fixed direction. Equilibrium in the orthogonal direction to the shearing force allows for the estimation of the shear stress versus strain response. The original model yielded conservative results with important scatter, thus various modifications were implemented in order to improve strength predictions: 1) recalibration of the strut (crack) direction, capturing the absence of transverse reinforcement and axial load in most corbels, 2) inclusion of main (boundary) reinforcement in the equilibrium equation, capturing its participation in the mechanism, and 3) decrease in aspect ratio by considering the width of the loading plate in the formulation. To analyze the behavior of the theoretical model, a database of 109 specimens available in the literature was collected. The model yielded an average model-to-test shear strength ratio of 0.98 and a coefficient of variation of 0.16, showing also that most test variables are well captured with the model, and providing better results than the original model. The model strength prediction is compared with other models in the literature, resulting in one of the most accurate estimates