Shear Strengthening of RC Beams Subjected to Cyclic Load Using CFRP Strips

Şakar, Gökhan

doi:10.1177/096369350801700604

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…An extensive literature review has been carried out to collect test data of RC beams strengthened with anchoraged FRP composites. Seventy‐three RC beams strengthened with anchoraged FRP reinforcements were collected from the literature (Sato et al ., ; Mitsui et al ., ; Khalifa et al ., ; Khalifa and Nanni, , ; Chaallal et al ., ; Li et al ., ; Adhikary et al ., ; Xuesong and Zhongfan, ; Anil, , ; Bousselham and Chaallal, ; Monti and Liotta, ; Sakar, , ; Quinn, ; Altin et al ., ; Chaallal et al ., ). Subsequently, six shear‐deficient RC beams strengthened with different CFRP configurations were tested in the laboratory by the author (Tanarslan et al ., ; Tanarslan, ).…”

Section: Experimental Datamentioning

confidence: 99%

An anticipated shear design method for reinforced concrete beams strengthened with anchoraged carbon fiber‐reinforced polymer by using neural network

Tanarslan

Kumanlıoğlu

Şakar

2014

Structural Design Tall Build

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SUMMARY Using externally bonded carbon fiber‐reinforced polymer (FRP) for strengthening has been turned into a popular decision owing to its mechanical leads. Consequently, design guidelines and researchers have established several analytical equations to predict the contribution of FRP to ultimate shear capacity. The developed analytical equations projected the influence of FRP reinforcements within certain limits. However, not mentioned parameters such as the shear span‐to‐depth ratio and anchorage application influence the ultimate behavior of strengthened specimens. Accordingly, distant predictions between test results and code predictions are observed for the specimens in whom anchorage is applied. As an alternative method, artificial neural network (NN) can be used to predict the contribution of anchoraged carbon FRP to shear strength of deficient reinforced concrete beams. Accordingly, two NN models with back‐propagation are developed in this study. Unlike the existing design codes, the model considers the effect of anchorage and the shear span‐to‐depth ratio at the ultimate state. Artificial NN model is trained, validated and tested using the literature of 79 reinforced concrete beams. Then, NN results are compared with those ‘theoretical’ predictions calculated directly from International Federation for Structural Concrete, the American guideline (ACI 440.2R) and the Australian guideline. Within all theoretical predictions of design guidelines, fib14 provided the best predictions according to experimental results. Consequently, 25% of fib14 predictions are within ±10% of the experimental results, and also, 65% of the fib14 predictions are within ±25% of the measured values. Besides, executed comparisons indicated that the NN model is more exact than the guideline equations with respect to the experimental results and can be applied effectively within the range of parameters covered in this study. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Experimental Datamentioning

confidence: 99%

An anticipated shear design method for reinforced concrete beams strengthened with anchoraged carbon fiber‐reinforced polymer by using neural network

Tanarslan

Kumanlıoğlu

Şakar

2014

Structural Design Tall Build

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“…The primary objective of the experimental program was to investigate the shear behavior of CFRP strengthened RC beams [47][48][49][50] and providing experimental data for an ANN network. Ten shear deficient (without shear reinforcement) RC beams were strengthened and tested in the experimental program.…”

Section: Experimental Testsmentioning

confidence: 99%

Predicting the Capacity of RC Beams Strengthened in Shear with Side-Bonded FRP Reinforcements Using Artificial Neural Networks

Tanarslan

2011

Composite Interfaces

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The application of artificial neural network (ANN) to predict the shear capacity of reinforced concrete (RC) beams retrofitted in shear by means of side-bonded fiber-reinforced polymer (FRP) is investigated in this paper. An extensive literature review has been carried out. In addition, ten shear deficient RC beams with different carbon fiber-reinforced polymer (CFRP) configurations were tested and added as data to the collected data. It was aimed to build an efficient and practical ANN model with parameters which can easily be obtained without any calculation and/or experimental investigation. The results are compared with the design guideline equations that emerge as predictions of the FRP contribution using the trained neural networks: these are in good agreement with the experimental results and better than those calculated from the theoretical guideline equations. Based on ANN results, a parametric study has been carried out to study the importance of different influencing parameters on the FRP contribution. Thereafter, a new simple expression is proposed for determining the contribution of externally bonded side-bonded FRP. Accordingly, the suggested design formula is capable of predicting the experimental FRP satisfactorily so that it can be admitted as an alternative to the existing guideline equations within the range of parameters covered in the study.

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Shear Strengthening of RC Beams Subjected to Cyclic Load Using CFRP Strips

Cited by 2 publications

References 6 publications

An anticipated shear design method for reinforced concrete beams strengthened with anchoraged carbon fiber‐reinforced polymer by using neural network

An anticipated shear design method for reinforced concrete beams strengthened with anchoraged carbon fiber‐reinforced polymer by using neural network

Predicting the Capacity of RC Beams Strengthened in Shear with Side-Bonded FRP Reinforcements Using Artificial Neural Networks

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