DIRECT SHEAR BEHAVIOR OF CARBON FIBER REINFORCED SELF-COMPACTING CONCRETE

Author(s): Dr. Kaiss F. Sarsam, Dr. Tareq S. Al-Attar and Ghzwan G. Jumah
Paper category: Proceeding
Book title: Design, Performance and Use of Self-Consolidating Concrete
Editor(s): Caijun Shi, Zhihua Ou and Kamal Henri Khayat
Print-ISBN: 978-2-35158-143-8
e-ISBN: 978-2-35158-144-5
Publisher: RILEM Publications SARL
Publication year: 2014
Pages: 243 - 250
Total Pages : 8
Language : English

Abstract: This study gives results of sixteen push-off or direct shear specimens in four groups. Variables include volume fraction for carbon fiber ( 0.00, 0.50, 0.75 and 1.00) % for every percentage change in the steel reinforcement. The steel reinforcement parameter values are (0.00, 2.66, 5.33 and 7.99) MPa. SCC with and without carbon fiber mixes at constant water to cementitious materials ratio of 0.3 by weight were investigated. The main concrete properties studied include compressive strength and splitting tensile strength. Measurements of deformations were made throughout testing of shear specimens.
It was found that using carbon fiber increased the direct shear strength. However, carbon fiber alone (without reinforcement) leads to a brittle failure. In contrast, adding rebars leads to higher strain and more ductile behaviorincreased shear capacity is obtained when higher steel quantity is used. The aim of adding carbon fibers was the increase of the horizontal strain (displacement). It was found that the optimum percentage of volume fraction was % for fresh and hardened concrete. The addition of carbon fiber leads to a drop in compressive strength () compared with reference specimens. This drop in was 2.39, 8.38 and 13.58% for 0.50, 0.75 and 1.00%, respectively. Meanwhile, the splitting tensile strength increased by 3.34, 31.2 and 18.2 as compared with the cylinder strength without carbon fibers at equal to 0.50, 0.75 and 1.00% respectively.
Based on push-off test results for this work and those available in the literature, two statistical models have been established using regression analysis. Four variables,, and , were included in these models. Both models showed good predictions according to their coefficient of variation (COV) values. Verification and assessment of the models was done by using 273 observations from literature and the present work.

Online publication: 2017
Publication type: full_text
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