2014
DOI: 10.1515/secm-2013-0002
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Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

Abstract: 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 corres… Show more

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Cited by 8 publications
(4 citation statements)
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“…Similarly, recent studies have also discussed similar behavior [34][35][36][37][38][39][40]. The adopted procedure for evaluating each of the variables has been wildly utilized in research [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”
Section: Effect Of Shear Span To Depth Ratio (A/d)mentioning
confidence: 99%
“…Similarly, recent studies have also discussed similar behavior [34][35][36][37][38][39][40]. The adopted procedure for evaluating each of the variables has been wildly utilized in research [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”
Section: Effect Of Shear Span To Depth Ratio (A/d)mentioning
confidence: 99%
“…[ [22]] designed an approach for the inverse identification of parameters during cutting operation to predict. Yavuz et al [ [23]] developed an artificial neural network (ANN) model to estimate the shear capacities of the FRP-strengthened reinforced concrete beams, they concluded that the ANN model had a better prediction accuracy than existing building code approaches.…”
Section: Introductionmentioning
confidence: 99%
“…Postel et al [20] designed an approach for the inverse identification of parameters during cutting operation to predict. Yavuz et al [21] developed an artificial neural network (ANN) model to estimate the shear capacities of the FRP-strengthened reinforced concrete beams, they concluded that the ANN model had a better prediction accuracy than existing building code approaches. Qin et al [22] used deep-learning technology to establish an end-to end relationship between cross-sectional SEM images of cement backfill beam (CPB) and its mechanical strength, a convolutional neural network was used to predict the mechanical strength of CPB based on the features extracted from the cross-sectional SEM images.…”
Section: Introductionmentioning
confidence: 99%