Theoretical Formula of Ultimate Shear Strength for RC Beams without Transverse Reinforcement by Using External Vertical Prestressing Rebars

Author(s):  
Xingwei Xue ◽  
Meizhong Wu ◽  
Peng Zhou ◽  
Junlong Zhou
2013 ◽  
Vol 19 (3) ◽  
pp. 400-408 ◽  
Author(s):  
Guray Arslan ◽  
Zekeriya Polat

Reinforced concrete (RC) beams with light transverse reinforcement are vulnerable to shear failure during seismic response. In order to prevent brittle shear failures at beam plastic hinge regions of earthquake-resistant structures, the Turkish Earthquake Code and ACI318 require the use of sufficient transverse reinforcement to resist the total expected shear demand. These codes tend to be excessively conservative and, in some cases, the contribution of the concrete to the shear strength is neglected. The aim of this study is to investigate the contribution of concrete to shear strength of RC beams failing in shear experimentally. The beams were tested under monotonically increasing reversed cyclic loading to determine the concrete contribution to shear strength. It is observed that the concrete contribution to the shear strength at ultimate state ranges from 18% to 69% of the ultimate strength.


2014 ◽  
Vol 21 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Gunnur Yavuz ◽  
Musa Hakan Arslan ◽  
Omer Kaan Baykan

AbstractIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R2≈0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.


Materials ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1525 ◽  
Author(s):  
Altug Yavas ◽  
Cumali Ogun Goker

In the presented paper, the impacts of steel fiber use and tensile reinforcement ratio on shear behavior of Ultra-High Performance Concrete (UHPC) beams were investigated from the point of different tensile reinforcement ratios. In the scope of the experimental program, a total of eight beams consisting of four reinforcement ratios representing low to high ratios ranged from 0.8% to 2.2% were casted without shear reinforcement and subjected to the four-point loading test. While half of the test beams included 30 mm end-hooked steel fibers (SF-UHPC) with 2.0 vol%, the remaining beams were produced without the fiber to show possible effectiveness of the fiber use. The shear performances were discussed in terms of the load—deflection response, cracking pattern and failure mode, first cracking load and ultimate shear strength. In this sense, all the non-fiber beams were failed by shear with a dramatic load drop, regardless of the tensile reinforcement amount, before the yielding of reinforcement and they produced no deflection capability. The test results showed that while the inclusion of steel fibers to the UHPC mixture with low reinforcement ratios changed the failure mode from the shear to flexure, it significantly enhanced the ultimate shear strength in the case of higher reinforcement ratio through the SF-UHPC’ superior mechanical properties and fibers’ crack-bridging ability.


Author(s):  
Tetsuhiro SHIMOZATO ◽  
Yoshiaki TAMAKI ◽  
Yasunori ARIZUMI ◽  
Tetsuya YABUKI ◽  
Syuichi ONO ◽  
...  

2017 ◽  
Vol 15 (1) ◽  
pp. 32-48 ◽  
Author(s):  
T. Zhang ◽  
P. Visintin ◽  
D. J. Oehlers
Keyword(s):  

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