Artificial neural networks for the prediction of shear capacity of steel plate strengthened RC beams

2004 ◽  
Vol 18 (6) ◽  
pp. 409-417 ◽  
Author(s):  
Bimal B Adhikary ◽  
Hiroshi Mutsuyoshi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Steel Plate ◽  
Shear Capacity ◽  
Rc Beams ◽  
Artificial Neural

A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement

2005 ◽  
Vol 32 (4) ◽  
pp. 644-657 ◽  
Author(s):  
Ayman Ahmed Seleemah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Experimental Work ◽  
Concrete Beams ◽  
Shear Capacity ◽  
Transverse Reinforcement ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Different relationships have been proposed by codes and researchers for predicting the shear capacity of members without transverse reinforcement. In this paper, the applicability of the artificial neural network (ANN) technique as an analytical alternative to existing methods for predicting this shear capacity is investigated using a critically reviewed and agreed upon database of experimental work that serves as a basis of comparison and (or) assessment of existing and new relationships. Both ANN and eight different codes and researcher's predictions of the shear capacity of the specimens of the database were compared. The ANN predictions are much superior to those of any of the current available relationships.Key words: artificial neural networks, shear capacity, transverse reinforcement, beams.

Condition assessment of RC beams using artificial neural networks

Structures ◽  
2020 ◽  
Vol 23 ◽  
pp. 1-12 ◽  
Author(s):  
B. Murali Krishna ◽  
V. Guru Prathap Reddy ◽  
Mohammed Shafee ◽  
T. Tadepalli
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Condition Assessment ◽  
Rc Beams ◽  
Artificial Neural

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

2014 ◽  
Vol 21 (2) ◽  
pp. 239-255 ◽  
Author(s):  
Gunnur Yavuz ◽  
Musa Hakan Arslan ◽  
Omer Kaan Baykan
Keyword(s):  
Neural Networks ◽  
Shear Strength ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Fiber Reinforced Polymers ◽  
Rc Beams ◽  
Ann Model ◽  
Ultimate Shear Strength ◽  
Existing Building ◽  
Artificial Neural

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.

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