Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes

Arslan, Musa Hakan

doi:10.1016/j.advengsoft.2010.05.009

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…In this study, contributions of V c and V s were computed by ACI 318 [28]. The contribution of FRP is found by truss analogy, similar to the determination of the contribution of steel shear reinforcement [34].…”

Section: Shear Strength Of Beams With Externally Bonded Frpmentioning

confidence: 99%

“…ANN have been successfully applied to a number of areas of structural engineering, an important branch of civil engineering. In recent literature, structural analysis and design, structural dynamics and control, structural damage assessment, and the structural behavior and properties of concrete materials, such as strength and constitutive modeling, are good examples for the application of ANN [25][26][27][28].…”

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: Shear Strength Of Beams With Externally Bonded Frpmentioning

confidence: 99%

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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“…Some of the well-known methods in this area are auto-regression, Markov chain, or robust optimization techniques [7][8][9]. Among the empirical methods, machine learning has been widely used to solve real world problems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Artificial neural networks (ANNs) are well-known machine learning systems that have been utilized to predict the solar radiation [2][3][4][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Using measured daily meteorological parameters to predict daily solar radiation

et al. 2015

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“…For instance, Arslan et al [2] estimated reduction factors for prefabricated single bay-single story reinforced concrete (RC) buildings using ANN with 81% accuracy. Arslan [4] determined the torsional strength of RC beams using various ANN algorithms and showed that the ANNs were better able to predict the torsional strength of the beams than conventional and code approaches. Ko-roglu et al [21] used combined artificial neural networks (CANNs) to estimate the flexural capacity of quadrilateral fiber-reinforced polymer (FRP) confined RC columns.…”

Section: Introductionmentioning

confidence: 99%

Statistical Feature Extraction Based on an Ann Approach for Estimating the Compressive Strength of Concrete

Doğan¹,

Arslan²,

Ceylan³

2015

NNW

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Applications of artificial intelligence in engineering disciplines have become widespread and have provided alternative solutions to engineering problems. Image processing technology (IPT) and artificial neural networks (ANNs) are types of artificial intelligence methods. However, IPT and ANN have been used together in extremely few studies. In this study, these two methods were used to determine the compressive strength of concrete, a complex material whose mechanical features are difficult to predict. Sixty cube-shaped specimens were manufactured, and images of specific features of the specimens were taken before they were tested to determine their compressive strengths. An ANN model was constituted as a result of the process of digitizing the images. In this way, the two different artificial intelligence methods were used together to carry out the analysis. The compressive strength values of the concrete obtained via analytical modeling were compared with the test results. The results of the comparison (R 2 = 0.9837−0.9961) indicate that the combination of these two artificial intelligence methods is highly capable of predicting the compressive strengths of the specimens. The model's predictive capability was also evaluated in terms of several statistical parameters using a set of statistical methods during the digitization of the images constituting the artificial neural network.

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Predicting of torsional strength of RC beams by using different artificial neural network algorithms and building codes

Cited by 35 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

Using measured daily meteorological parameters to predict daily solar radiation

Statistical Feature Extraction Based on an Ann Approach for Estimating the Compressive Strength of Concrete

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