An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks

Tanarslan, Hasan Murat; Seçer, Mutlu; Kumanlıoğlu, Ahmet Ali

doi:10.1016/j.conbuildmat.2011.12.008

Cited by 71 publications

(24 citation statements)

References 27 publications

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“…In the literature, there are some studies which have been performed on FRP confinement of concrete and RC members by using ANN [4,[29][30][31][32]. For instance, Elsanadedy et al [29] used neural network and regression models to predict the compressive strength of FRP-confined concrete.…”

Section: Introductionmentioning

confidence: 98%

“…The performance of the proposed equations was compared to the predictions with some of the current shear design guidelines for strengthening concrete structures using FRPs. Tanarslan et al [31] used an ANN model for predicting the shear capacity of RC beams, retrofitted in shear by means of externally bonded wrapped and U-jacketed FRP. They directly compared some codes with their calculated theoretical predictions.…”

Section: Introductionmentioning

confidence: 99%

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Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Yavuz

Arslan

Baykan

2014

Science and Engineering of Composite Materials

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In 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 (R 2 ≈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.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Yavuz

Arslan

Baykan

2014

Science and Engineering of Composite Materials

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“…In a recent work, Tanarslan et al [10] developed an artificial neural network (ANN) model to predict the shear capacity of RC beams strengthened with FRP reinforcements. They declared that the ANN model was more accurate than the guideline equations with respect to the experimental results.…”

Section: Introductionmentioning

confidence: 99%

Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP

Doran

Yetilmezsoy

Murtazaoglu

2015

Engineering Structures

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“…CONBPS (Construction Best Practice System) for modelling of the construction process by Poon (2004); neural networks to calculate the shear bearing capacity of FRP -fibre reinforced polymer -strengthened reinforced concrete beams by Tanarslan et al (2012); or for the assessment of construction environmental impact in Zhao et al (2006).…”

Section: Review On Artificial Intelligence In Construction and Aim Ofmentioning

confidence: 99%

Aided Diagnosis of Structural Pathologies with an Expert System

Bernat

Gil

2013

Advances in Structural Engineering

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Sustainability and safety are social demands for long-life buildings. Suitable inspection and maintenance tasks on structural elements are needed for keeping buildings safely in service. Any malfunction that causes structural damage could be called pathology by analogy between structural engineering and medicine.Even the easiest evaluation tasks require expensive training periods that may be shortened with a suitable tool. This work presents an expert system (called Doctor House or DH) for diagnosing pathologies of structural elements in buildings. DH differs from other expert systems when it deals with uncertainty in a far easier but still useful way and it is capable of aiding during the initial survey 'in situ', when damage should be detected at a glance. DH is a powerful tool that represents complex knowledge gathered from bibliography and experts.Knowledge codification and uncertainty treatment are the main achievements presented. Finally, DH was tested and validated during real surveys.

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An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks

Cited by 71 publications

References 27 publications

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Application of fuzzy logic approach in predicting the lateral confinement coefficient for RC columns wrapped with CFRP

Aided Diagnosis of Structural Pathologies with an Expert System

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