A design approach to determine the shear capacity of reinforced concrete beams shear strengthened with NSM systems

This paper present a design approach to predict the shear capacity of reinforced concrete (RC) beams strengthened with fiber reinforced polymer (FRP) laminates/rods applied according to the near surface mounted (NSM) technique. The new approach is based on the simplified modified compression field t...

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Bibliographic Details
Main Author: Baghi, Hadi (author)
Other Authors: Barros, Joaquim A. O. (author)
Format: article
Language:eng
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/1822/45427
Country:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/45427
Description
Summary:This paper present a design approach to predict the shear capacity of reinforced concrete (RC) beams strengthened with fiber reinforced polymer (FRP) laminates/rods applied according to the near surface mounted (NSM) technique. The new approach is based on the simplified modified compression field theory (SMCFT) and considers the relevant features of the interaction between NSM FRP systems and surrounding concrete, like debond and concrete fracture. In the SMCFT model, the shear strength of a RC element is a function of two parameters: the tensile stress factor in the cracked concrete ( β ), and the inclination of the diagonal compressive stress in the web of the section ( θ ). However, this approach is not a straightforward design methodology due to its iterative nature. A sensitivity analysis is carried out to assess the relative importance of each input parameter that mostly affect the shear capacity of RC beams shear strengthened according to the NSM technique. Taking into account the obtained results, equations to determine β and θ without recurring to an iterative procedure are derived. The experimental results of 112 beams shear strengthened with NSM FRP are used to appraise the predictive performance of the developed approach. By evaluating the ratio between the experimental results and the analytical predictions, an average value of 1.14 is obtained, with a coefficient of variation of 13.1%, being safe estimations 87% of the predictions.